Introduction

The correct interpretation of a multiverse analysis can be difficult due to their potential size and the complexity of correctly interpreting their uncertainty. Our recent work in developing Milliways, an interactive visualisation interface for the principled evaluation and interpretation of the results of multiverse analysis aims to address this problem. For more details, please refer to our paper.

Milliways

To visualise the results of a multiverse analysis using Milliways, the user needs to provide four files:

In this document, I outline how to generate each of these files for the multiverse analysis on the Durante dataset (see vignette("visualising-multiverse")). Compiling this document will, by itself, result in the creation of the EMAR, provided the following function is declared.

Note #1: If compiling the document as an EMAR, each code chunk will execute automatically. You do not need to call execute_multiverse() to obtain the results unless you want to extract the results and access it from an R code chunk.

Note #2: If you are not compiling the document as an EMAR, the code chunks will not execute automatically. The only way to obtain the results is to first execute all the universes in the multiverse using execute_multiverse() and then accessing the results using an R code chunk.

Note #3: On the use of magrittr pipes (%>%) instead of the native R pipe (|>). Because multiverse rewrites R expressions, when I parse the code declared into individual R scripts, the native R pipe is “evaluated” (i.e. df |> mutate(...) |> filter(...) becomes filter(mutate(df, ...), ...)); this makes the code readable. Instead I use magrittr pipes which does not “evaluate” the R expressions.

knit_as_emar()

Analysis

The analysis follows the same steps outlined in vignette("example-durante"). The first step is to read the raw data from the file and store it as a tibble.

data("durante")

data.raw.study2 <- durante %>%
  mutate(
    Abortion = abs(7 - Abortion) + 1,
    StemCell = abs(7 - StemCell) + 1,
    Marijuana = abs(7 - Marijuana) + 1,
    RichTax = abs(7 - RichTax) + 1,
    StLiving = abs(7 - StLiving) + 1,
    Profit = abs(7 - Profit) + 1,
    FiscConsComp = FreeMarket + PrivSocialSec + RichTax + StLiving + Profit,
    SocConsComp = Marriage + RestrictAbortion + Abortion + StemCell + Marijuana
  )

M = multiverse()

Cycle Length

In their Durante et al. exclude participants based on the length of their menstrual cycle, and only include those whose cycle lengths are between 25 and 35 days. However, according to Steegen et al., due to the flexibility in the data collection, “this exclusion criterion can be instantiated in two reasonable ways, using either a woman’s computed cycle length or a woman’s self-reported typical cycle length.”

Note: we can define a tangle widget to allow the user to switch between which operationalisation of the outlier exclusion criteria is used using the syntax <mv param="cycle_length"/>. Here, cycle_length can be replaced with the name of any parameter. In the EMAR document, you can see and interact with the tangle widget.

df <- data.raw.study2 %>%
  mutate(ComputedCycleLength = StartDateofLastPeriod - StartDateofPeriodBeforeLast) %>%
  filter(TRUE)
df <- data.raw.study2 %>%
  mutate(ComputedCycleLength = StartDateofLastPeriod - StartDateofPeriodBeforeLast) %>%
  filter(ComputedCycleLength > 25 & ComputedCycleLength < 35)
df <- data.raw.study2 %>%
  mutate(ComputedCycleLength = StartDateofLastPeriod - StartDateofPeriodBeforeLast) %>%
  filter(ReportedCycleLength > 25 & ReportedCycleLength < 35)

Certainty

Steegen et al. describe that how certain the participants are in their reported dates can be another justifiable exclusion criteria:

df <- df %>%
  filter(TRUE)
df <- df %>%
  filter(Sure1 > 6 | Sure2 > 6)
df <- df %>%
  filter(TRUE)
df <- df %>%
  filter(Sure1 > 6 | Sure2 > 6)
df <- df %>%
  filter(TRUE)
df <- df %>%
  filter(Sure1 > 6 | Sure2 > 6)

Menstrual Calculation

The flexibility in how the data is collected also allows three reasonable alternatives for estimating a woman’s next menstrual onset, which is an intermediate step in determining cycle day.

df <- df %>%
  mutate(NextMenstrualOnset = StartDateofLastPeriod + ComputedCycleLength) %>%
  mutate(CycleDay = 28 - (NextMenstrualOnset - DateTesting), CycleDay = ifelse(WorkerID ==
    15, 11, ifelse(WorkerID == 16, 18, CycleDay)), CycleDay = ifelse(CycleDay >
    1 & CycleDay < 28, CycleDay, ifelse(CycleDay < 1, 1, 28)))
df <- df %>%
  mutate(NextMenstrualOnset = StartDateofLastPeriod + ReportedCycleLength) %>%
  mutate(CycleDay = 28 - (NextMenstrualOnset - DateTesting), CycleDay = ifelse(WorkerID ==
    15, 11, ifelse(WorkerID == 16, 18, CycleDay)), CycleDay = ifelse(CycleDay >
    1 & CycleDay < 28, CycleDay, ifelse(CycleDay < 1, 1, 28)))
df <- df %>%
  mutate(NextMenstrualOnset = StartDateNext) %>%
  mutate(CycleDay = 28 - (NextMenstrualOnset - DateTesting), CycleDay = ifelse(WorkerID ==
    15, 11, ifelse(WorkerID == 16, 18, CycleDay)), CycleDay = ifelse(CycleDay >
    1 & CycleDay < 28, CycleDay, ifelse(CycleDay < 1, 1, 28)))
df <- df %>%
  mutate(NextMenstrualOnset = StartDateofLastPeriod + ComputedCycleLength) %>%
  mutate(CycleDay = 28 - (NextMenstrualOnset - DateTesting), CycleDay = ifelse(WorkerID ==
    15, 11, ifelse(WorkerID == 16, 18, CycleDay)), CycleDay = ifelse(CycleDay >
    1 & CycleDay < 28, CycleDay, ifelse(CycleDay < 1, 1, 28)))
df <- df %>%
  mutate(NextMenstrualOnset = StartDateofLastPeriod + ReportedCycleLength) %>%
  mutate(CycleDay = 28 - (NextMenstrualOnset - DateTesting), CycleDay = ifelse(WorkerID ==
    15, 11, ifelse(WorkerID == 16, 18, CycleDay)), CycleDay = ifelse(CycleDay >
    1 & CycleDay < 28, CycleDay, ifelse(CycleDay < 1, 1, 28)))
df <- df %>%
  mutate(NextMenstrualOnset = StartDateNext) %>%
  mutate(CycleDay = 28 - (NextMenstrualOnset - DateTesting), CycleDay = ifelse(WorkerID ==
    15, 11, ifelse(WorkerID == 16, 18, CycleDay)), CycleDay = ifelse(CycleDay >
    1 & CycleDay < 28, CycleDay, ifelse(CycleDay < 1, 1, 28)))
df <- df %>%
  mutate(NextMenstrualOnset = StartDateofLastPeriod + ComputedCycleLength) %>%
  mutate(CycleDay = 28 - (NextMenstrualOnset - DateTesting), CycleDay = ifelse(WorkerID ==
    15, 11, ifelse(WorkerID == 16, 18, CycleDay)), CycleDay = ifelse(CycleDay >
    1 & CycleDay < 28, CycleDay, ifelse(CycleDay < 1, 1, 28)))
df <- df %>%
  mutate(NextMenstrualOnset = StartDateNext) %>%
  mutate(CycleDay = 28 - (NextMenstrualOnset - DateTesting), CycleDay = ifelse(WorkerID ==
    15, 11, ifelse(WorkerID == 16, 18, CycleDay)), CycleDay = ifelse(CycleDay >
    1 & CycleDay < 28, CycleDay, ifelse(CycleDay < 1, 1, 28)))
df <- df %>%
  mutate(NextMenstrualOnset = StartDateofLastPeriod + ComputedCycleLength) %>%
  mutate(CycleDay = 28 - (NextMenstrualOnset - DateTesting), CycleDay = ifelse(WorkerID ==
    15, 11, ifelse(WorkerID == 16, 18, CycleDay)), CycleDay = ifelse(CycleDay >
    1 & CycleDay < 28, CycleDay, ifelse(CycleDay < 1, 1, 28)))
df <- df %>%
  mutate(NextMenstrualOnset = StartDateNext) %>%
  mutate(CycleDay = 28 - (NextMenstrualOnset - DateTesting), CycleDay = ifelse(WorkerID ==
    15, 11, ifelse(WorkerID == 16, 18, CycleDay)), CycleDay = ifelse(CycleDay >
    1 & CycleDay < 28, CycleDay, ifelse(CycleDay < 1, 1, 28)))
df <- df %>%
  mutate(NextMenstrualOnset = StartDateofLastPeriod + ComputedCycleLength) %>%
  mutate(CycleDay = 28 - (NextMenstrualOnset - DateTesting), CycleDay = ifelse(WorkerID ==
    15, 11, ifelse(WorkerID == 16, 18, CycleDay)), CycleDay = ifelse(CycleDay >
    1 & CycleDay < 28, CycleDay, ifelse(CycleDay < 1, 1, 28)))
df <- df %>%
  mutate(NextMenstrualOnset = StartDateofLastPeriod + ReportedCycleLength) %>%
  mutate(CycleDay = 28 - (NextMenstrualOnset - DateTesting), CycleDay = ifelse(WorkerID ==
    15, 11, ifelse(WorkerID == 16, 18, CycleDay)), CycleDay = ifelse(CycleDay >
    1 & CycleDay < 28, CycleDay, ifelse(CycleDay < 1, 1, 28)))
df <- df %>%
  mutate(NextMenstrualOnset = StartDateNext) %>%
  mutate(CycleDay = 28 - (NextMenstrualOnset - DateTesting), CycleDay = ifelse(WorkerID ==
    15, 11, ifelse(WorkerID == 16, 18, CycleDay)), CycleDay = ifelse(CycleDay >
    1 & CycleDay < 28, CycleDay, ifelse(CycleDay < 1, 1, 28)))
df <- df %>%
  mutate(NextMenstrualOnset = StartDateofLastPeriod + ComputedCycleLength) %>%
  mutate(CycleDay = 28 - (NextMenstrualOnset - DateTesting), CycleDay = ifelse(WorkerID ==
    15, 11, ifelse(WorkerID == 16, 18, CycleDay)), CycleDay = ifelse(CycleDay >
    1 & CycleDay < 28, CycleDay, ifelse(CycleDay < 1, 1, 28)))
df <- df %>%
  mutate(NextMenstrualOnset = StartDateofLastPeriod + ReportedCycleLength) %>%
  mutate(CycleDay = 28 - (NextMenstrualOnset - DateTesting), CycleDay = ifelse(WorkerID ==
    15, 11, ifelse(WorkerID == 16, 18, CycleDay)), CycleDay = ifelse(CycleDay >
    1 & CycleDay < 28, CycleDay, ifelse(CycleDay < 1, 1, 28)))
df <- df %>%
  mutate(NextMenstrualOnset = StartDateNext) %>%
  mutate(CycleDay = 28 - (NextMenstrualOnset - DateTesting), CycleDay = ifelse(WorkerID ==
    15, 11, ifelse(WorkerID == 16, 18, CycleDay)), CycleDay = ifelse(CycleDay >
    1 & CycleDay < 28, CycleDay, ifelse(CycleDay < 1, 1, 28)))

Fertility

Durante et al. classify women into a high or low fertility group based on cycle day, but this classification can be done in various different reasonable ways:

df <- df %>%
  mutate(Fertility = factor(ifelse(CycleDay >= 7 & CycleDay <= 14, "high", ifelse(CycleDay >=
    17 & CycleDay <= 25, "low", NA))))
df <- df %>%
  mutate(Fertility = factor(ifelse(CycleDay >= 6 & CycleDay <= 14, "high", ifelse(CycleDay >=
    17 & CycleDay <= 27, "low", NA))))
df <- df %>%
  mutate(Fertility = factor(ifelse(CycleDay >= 9 & CycleDay <= 17, "high", ifelse(CycleDay >=
    18 & CycleDay <= 25, "low", NA))))
df <- df %>%
  mutate(Fertility = factor(ifelse(CycleDay >= 8 & CycleDay <= 14, "high", "low")))
df <- df %>%
  mutate(Fertility = factor(ifelse(CycleDay >= 8 & CycleDay <= 17, "high", "low")))
df <- df %>%
  mutate(Fertility = factor(ifelse(CycleDay >= 7 & CycleDay <= 14, "high", ifelse(CycleDay >=
    17 & CycleDay <= 25, "low", NA))))
df <- df %>%
  mutate(Fertility = factor(ifelse(CycleDay >= 6 & CycleDay <= 14, "high", ifelse(CycleDay >=
    17 & CycleDay <= 27, "low", NA))))
df <- df %>%
  mutate(Fertility = factor(ifelse(CycleDay >= 9 & CycleDay <= 17, "high", ifelse(CycleDay >=
    18 & CycleDay <= 25, "low", NA))))
df <- df %>%
  mutate(Fertility = factor(ifelse(CycleDay >= 8 & CycleDay <= 14, "high", "low")))
df <- df %>%
  mutate(Fertility = factor(ifelse(CycleDay >= 8 & CycleDay <= 17, "high", "low")))
df <- df %>%
  mutate(Fertility = factor(ifelse(CycleDay >= 7 & CycleDay <= 14, "high", ifelse(CycleDay >=
    17 & CycleDay <= 25, "low", NA))))
df <- df %>%
  mutate(Fertility = factor(ifelse(CycleDay >= 6 & CycleDay <= 14, "high", ifelse(CycleDay >=
    17 & CycleDay <= 27, "low", NA))))
df <- df %>%
  mutate(Fertility = factor(ifelse(CycleDay >= 9 & CycleDay <= 17, "high", ifelse(CycleDay >=
    18 & CycleDay <= 25, "low", NA))))
df <- df %>%
  mutate(Fertility = factor(ifelse(CycleDay >= 8 & CycleDay <= 14, "high", "low")))
df <- df %>%
  mutate(Fertility = factor(ifelse(CycleDay >= 8 & CycleDay <= 17, "high", "low")))
df <- df %>%
  mutate(Fertility = factor(ifelse(CycleDay >= 7 & CycleDay <= 14, "high", ifelse(CycleDay >=
    17 & CycleDay <= 25, "low", NA))))
df <- df %>%
  mutate(Fertility = factor(ifelse(CycleDay >= 6 & CycleDay <= 14, "high", ifelse(CycleDay >=
    17 & CycleDay <= 27, "low", NA))))
df <- df %>%
  mutate(Fertility = factor(ifelse(CycleDay >= 9 & CycleDay <= 17, "high", ifelse(CycleDay >=
    18 & CycleDay <= 25, "low", NA))))
df <- df %>%
  mutate(Fertility = factor(ifelse(CycleDay >= 8 & CycleDay <= 14, "high", "low")))
df <- df %>%
  mutate(Fertility = factor(ifelse(CycleDay >= 8 & CycleDay <= 17, "high", "low")))
df <- df %>%
  mutate(Fertility = factor(ifelse(CycleDay >= 7 & CycleDay <= 14, "high", ifelse(CycleDay >=
    17 & CycleDay <= 25, "low", NA))))
df <- df %>%
  mutate(Fertility = factor(ifelse(CycleDay >= 6 & CycleDay <= 14, "high", ifelse(CycleDay >=
    17 & CycleDay <= 27, "low", NA))))
df <- df %>%
  mutate(Fertility = factor(ifelse(CycleDay >= 9 & CycleDay <= 17, "high", ifelse(CycleDay >=
    18 & CycleDay <= 25, "low", NA))))
df <- df %>%
  mutate(Fertility = factor(ifelse(CycleDay >= 8 & CycleDay <= 14, "high", "low")))
df <- df %>%
  mutate(Fertility = factor(ifelse(CycleDay >= 8 & CycleDay <= 17, "high", "low")))
df <- df %>%
  mutate(Fertility = factor(ifelse(CycleDay >= 7 & CycleDay <= 14, "high", ifelse(CycleDay >=
    17 & CycleDay <= 25, "low", NA))))
df <- df %>%
  mutate(Fertility = factor(ifelse(CycleDay >= 6 & CycleDay <= 14, "high", ifelse(CycleDay >=
    17 & CycleDay <= 27, "low", NA))))
df <- df %>%
  mutate(Fertility = factor(ifelse(CycleDay >= 9 & CycleDay <= 17, "high", ifelse(CycleDay >=
    18 & CycleDay <= 25, "low", NA))))
df <- df %>%
  mutate(Fertility = factor(ifelse(CycleDay >= 8 & CycleDay <= 14, "high", "low")))
df <- df %>%
  mutate(Fertility = factor(ifelse(CycleDay >= 8 & CycleDay <= 17, "high", "low")))
df <- df %>%
  mutate(Fertility = factor(ifelse(CycleDay >= 7 & CycleDay <= 14, "high", ifelse(CycleDay >=
    17 & CycleDay <= 25, "low", NA))))
df <- df %>%
  mutate(Fertility = factor(ifelse(CycleDay >= 6 & CycleDay <= 14, "high", ifelse(CycleDay >=
    17 & CycleDay <= 27, "low", NA))))
df <- df %>%
  mutate(Fertility = factor(ifelse(CycleDay >= 9 & CycleDay <= 17, "high", ifelse(CycleDay >=
    18 & CycleDay <= 25, "low", NA))))
df <- df %>%
  mutate(Fertility = factor(ifelse(CycleDay >= 8 & CycleDay <= 14, "high", "low")))
df <- df %>%
  mutate(Fertility = factor(ifelse(CycleDay >= 8 & CycleDay <= 17, "high", "low")))
df <- df %>%
  mutate(Fertility = factor(ifelse(CycleDay >= 7 & CycleDay <= 14, "high", ifelse(CycleDay >=
    17 & CycleDay <= 25, "low", NA))))
df <- df %>%
  mutate(Fertility = factor(ifelse(CycleDay >= 6 & CycleDay <= 14, "high", ifelse(CycleDay >=
    17 & CycleDay <= 27, "low", NA))))
df <- df %>%
  mutate(Fertility = factor(ifelse(CycleDay >= 9 & CycleDay <= 17, "high", ifelse(CycleDay >=
    18 & CycleDay <= 25, "low", NA))))
df <- df %>%
  mutate(Fertility = factor(ifelse(CycleDay >= 8 & CycleDay <= 14, "high", "low")))
df <- df %>%
  mutate(Fertility = factor(ifelse(CycleDay >= 8 & CycleDay <= 17, "high", "low")))
df <- df %>%
  mutate(Fertility = factor(ifelse(CycleDay >= 7 & CycleDay <= 14, "high", ifelse(CycleDay >=
    17 & CycleDay <= 25, "low", NA))))
df <- df %>%
  mutate(Fertility = factor(ifelse(CycleDay >= 6 & CycleDay <= 14, "high", ifelse(CycleDay >=
    17 & CycleDay <= 27, "low", NA))))
df <- df %>%
  mutate(Fertility = factor(ifelse(CycleDay >= 9 & CycleDay <= 17, "high", ifelse(CycleDay >=
    18 & CycleDay <= 25, "low", NA))))
df <- df %>%
  mutate(Fertility = factor(ifelse(CycleDay >= 8 & CycleDay <= 14, "high", "low")))
df <- df %>%
  mutate(Fertility = factor(ifelse(CycleDay >= 8 & CycleDay <= 17, "high", "low")))
df <- df %>%
  mutate(Fertility = factor(ifelse(CycleDay >= 7 & CycleDay <= 14, "high", ifelse(CycleDay >=
    17 & CycleDay <= 25, "low", NA))))
df <- df %>%
  mutate(Fertility = factor(ifelse(CycleDay >= 6 & CycleDay <= 14, "high", ifelse(CycleDay >=
    17 & CycleDay <= 27, "low", NA))))
df <- df %>%
  mutate(Fertility = factor(ifelse(CycleDay >= 9 & CycleDay <= 17, "high", ifelse(CycleDay >=
    18 & CycleDay <= 25, "low", NA))))
df <- df %>%
  mutate(Fertility = factor(ifelse(CycleDay >= 8 & CycleDay <= 14, "high", "low")))
df <- df %>%
  mutate(Fertility = factor(ifelse(CycleDay >= 8 & CycleDay <= 17, "high", "low")))
df <- df %>%
  mutate(Fertility = factor(ifelse(CycleDay >= 7 & CycleDay <= 14, "high", ifelse(CycleDay >=
    17 & CycleDay <= 25, "low", NA))))
df <- df %>%
  mutate(Fertility = factor(ifelse(CycleDay >= 6 & CycleDay <= 14, "high", ifelse(CycleDay >=
    17 & CycleDay <= 27, "low", NA))))
df <- df %>%
  mutate(Fertility = factor(ifelse(CycleDay >= 9 & CycleDay <= 17, "high", ifelse(CycleDay >=
    18 & CycleDay <= 25, "low", NA))))
df <- df %>%
  mutate(Fertility = factor(ifelse(CycleDay >= 8 & CycleDay <= 14, "high", "low")))
df <- df %>%
  mutate(Fertility = factor(ifelse(CycleDay >= 8 & CycleDay <= 17, "high", "low")))
df <- df %>%
  mutate(Fertility = factor(ifelse(CycleDay >= 7 & CycleDay <= 14, "high", ifelse(CycleDay >=
    17 & CycleDay <= 25, "low", NA))))
df <- df %>%
  mutate(Fertility = factor(ifelse(CycleDay >= 6 & CycleDay <= 14, "high", ifelse(CycleDay >=
    17 & CycleDay <= 27, "low", NA))))
df <- df %>%
  mutate(Fertility = factor(ifelse(CycleDay >= 9 & CycleDay <= 17, "high", ifelse(CycleDay >=
    18 & CycleDay <= 25, "low", NA))))
df <- df %>%
  mutate(Fertility = factor(ifelse(CycleDay >= 8 & CycleDay <= 14, "high", "low")))
df <- df %>%
  mutate(Fertility = factor(ifelse(CycleDay >= 8 & CycleDay <= 17, "high", "low")))
df <- df %>%
  mutate(Fertility = factor(ifelse(CycleDay >= 7 & CycleDay <= 14, "high", ifelse(CycleDay >=
    17 & CycleDay <= 25, "low", NA))))
df <- df %>%
  mutate(Fertility = factor(ifelse(CycleDay >= 6 & CycleDay <= 14, "high", ifelse(CycleDay >=
    17 & CycleDay <= 27, "low", NA))))
df <- df %>%
  mutate(Fertility = factor(ifelse(CycleDay >= 9 & CycleDay <= 17, "high", ifelse(CycleDay >=
    18 & CycleDay <= 25, "low", NA))))
df <- df %>%
  mutate(Fertility = factor(ifelse(CycleDay >= 8 & CycleDay <= 14, "high", "low")))
df <- df %>%
  mutate(Fertility = factor(ifelse(CycleDay >= 8 & CycleDay <= 17, "high", "low")))
df <- df %>%
  mutate(Fertility = factor(ifelse(CycleDay >= 7 & CycleDay <= 14, "high", ifelse(CycleDay >=
    17 & CycleDay <= 25, "low", NA))))
df <- df %>%
  mutate(Fertility = factor(ifelse(CycleDay >= 6 & CycleDay <= 14, "high", ifelse(CycleDay >=
    17 & CycleDay <= 27, "low", NA))))
df <- df %>%
  mutate(Fertility = factor(ifelse(CycleDay >= 9 & CycleDay <= 17, "high", ifelse(CycleDay >=
    18 & CycleDay <= 25, "low", NA))))
df <- df %>%
  mutate(Fertility = factor(ifelse(CycleDay >= 8 & CycleDay <= 14, "high", "low")))
df <- df %>%
  mutate(Fertility = factor(ifelse(CycleDay >= 8 & CycleDay <= 17, "high", "low")))
df <- df %>%
  mutate(Fertility = factor(ifelse(CycleDay >= 7 & CycleDay <= 14, "high", ifelse(CycleDay >=
    17 & CycleDay <= 25, "low", NA))))
df <- df %>%
  mutate(Fertility = factor(ifelse(CycleDay >= 6 & CycleDay <= 14, "high", ifelse(CycleDay >=
    17 & CycleDay <= 27, "low", NA))))
df <- df %>%
  mutate(Fertility = factor(ifelse(CycleDay >= 9 & CycleDay <= 17, "high", ifelse(CycleDay >=
    18 & CycleDay <= 25, "low", NA))))
df <- df %>%
  mutate(Fertility = factor(ifelse(CycleDay >= 8 & CycleDay <= 14, "high", "low")))
df <- df %>%
  mutate(Fertility = factor(ifelse(CycleDay >= 8 & CycleDay <= 17, "high", "low")))
df <- df %>%
  mutate(Fertility = factor(ifelse(CycleDay >= 7 & CycleDay <= 14, "high", ifelse(CycleDay >=
    17 & CycleDay <= 25, "low", NA))))
df <- df %>%
  mutate(Fertility = factor(ifelse(CycleDay >= 6 & CycleDay <= 14, "high", ifelse(CycleDay >=
    17 & CycleDay <= 27, "low", NA))))
df <- df %>%
  mutate(Fertility = factor(ifelse(CycleDay >= 9 & CycleDay <= 17, "high", ifelse(CycleDay >=
    18 & CycleDay <= 25, "low", NA))))
df <- df %>%
  mutate(Fertility = factor(ifelse(CycleDay >= 8 & CycleDay <= 14, "high", "low")))
df <- df %>%
  mutate(Fertility = factor(ifelse(CycleDay >= 8 & CycleDay <= 17, "high", "low")))

Relationship Status

The participants in the study described their relationship status as one of the following options: (1) not dating/romantically involved with anyone, (2) dating or involved with only one partner, (3) engaged or living with my partner, and (4) married. This allows various different ways of classifying whether a participants is in a relationship or not:

df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
    2, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
    3 | Relationship == 4, "Relationship", NA)))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
    2, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
    3 | Relationship == 4, "Relationship", NA)))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
    2, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
    3 | Relationship == 4, "Relationship", NA)))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
    2, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
    3 | Relationship == 4, "Relationship", NA)))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
    2, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
    3 | Relationship == 4, "Relationship", NA)))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
    2, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
    3 | Relationship == 4, "Relationship", NA)))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
    2, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
    3 | Relationship == 4, "Relationship", NA)))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
    2, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
    3 | Relationship == 4, "Relationship", NA)))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
    2, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
    3 | Relationship == 4, "Relationship", NA)))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
    2, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
    3 | Relationship == 4, "Relationship", NA)))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
    2, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
    3 | Relationship == 4, "Relationship", NA)))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
    2, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
    3 | Relationship == 4, "Relationship", NA)))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
    2, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
    3 | Relationship == 4, "Relationship", NA)))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
    2, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
    3 | Relationship == 4, "Relationship", NA)))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
    2, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
    3 | Relationship == 4, "Relationship", NA)))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
    2, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
    3 | Relationship == 4, "Relationship", NA)))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
    2, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
    3 | Relationship == 4, "Relationship", NA)))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
    2, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
    3 | Relationship == 4, "Relationship", NA)))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
    2, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
    3 | Relationship == 4, "Relationship", NA)))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
    2, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
    3 | Relationship == 4, "Relationship", NA)))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
    2, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
    3 | Relationship == 4, "Relationship", NA)))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
    2, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
    3 | Relationship == 4, "Relationship", NA)))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
    2, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
    3 | Relationship == 4, "Relationship", NA)))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
    2, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
    3 | Relationship == 4, "Relationship", NA)))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
    2, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
    3 | Relationship == 4, "Relationship", NA)))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
    2, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
    3 | Relationship == 4, "Relationship", NA)))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
    2, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
    3 | Relationship == 4, "Relationship", NA)))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
    2, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
    3 | Relationship == 4, "Relationship", NA)))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
    2, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
    3 | Relationship == 4, "Relationship", NA)))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
    2, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
    3 | Relationship == 4, "Relationship", NA)))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
    2, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
    3 | Relationship == 4, "Relationship", NA)))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
    2, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
    3 | Relationship == 4, "Relationship", NA)))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
    2, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
    3 | Relationship == 4, "Relationship", NA)))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
    2, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
    3 | Relationship == 4, "Relationship", NA)))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
    2, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
    3 | Relationship == 4, "Relationship", NA)))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
    2, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
    3 | Relationship == 4, "Relationship", NA)))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
    2, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
    3 | Relationship == 4, "Relationship", NA)))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
    2, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
    3 | Relationship == 4, "Relationship", NA)))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
    2, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
    3 | Relationship == 4, "Relationship", NA)))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
    2, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
    3 | Relationship == 4, "Relationship", NA)))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
    2, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
    3 | Relationship == 4, "Relationship", NA)))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
    2, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
    3 | Relationship == 4, "Relationship", NA)))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
    2, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
    3 | Relationship == 4, "Relationship", NA)))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
    2, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
    3 | Relationship == 4, "Relationship", NA)))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
    2, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
    3 | Relationship == 4, "Relationship", NA)))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
    2, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
    3 | Relationship == 4, "Relationship", NA)))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
    2, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
    3 | Relationship == 4, "Relationship", NA)))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
    2, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
    3 | Relationship == 4, "Relationship", NA)))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
    2, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
    3 | Relationship == 4, "Relationship", NA)))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
    2, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
    3 | Relationship == 4, "Relationship", NA)))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
    2, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
    3 | Relationship == 4, "Relationship", NA)))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
    2, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
    3 | Relationship == 4, "Relationship", NA)))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
    2, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
    3 | Relationship == 4, "Relationship", NA)))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
    2, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
    3 | Relationship == 4, "Relationship", NA)))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
    2, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
    3 | Relationship == 4, "Relationship", NA)))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
    2, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
    3 | Relationship == 4, "Relationship", NA)))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
    2, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
    3 | Relationship == 4, "Relationship", NA)))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
    2, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
    3 | Relationship == 4, "Relationship", NA)))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
    2, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
    3 | Relationship == 4, "Relationship", NA)))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
    2, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
    3 | Relationship == 4, "Relationship", NA)))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
    2, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
    3 | Relationship == 4, "Relationship", NA)))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
    2, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
    3 | Relationship == 4, "Relationship", NA)))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
    2, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
    3 | Relationship == 4, "Relationship", NA)))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
    2, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
    3 | Relationship == 4, "Relationship", NA)))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
    2, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
    3 | Relationship == 4, "Relationship", NA)))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
    2, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
    3 | Relationship == 4, "Relationship", NA)))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
    2, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
    3 | Relationship == 4, "Relationship", NA)))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
    2, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
    3 | Relationship == 4, "Relationship", NA)))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
    2, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
    3 | Relationship == 4, "Relationship", NA)))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
    2, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
    3 | Relationship == 4, "Relationship", NA)))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
    2, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
    3 | Relationship == 4, "Relationship", NA)))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
    2, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
    3 | Relationship == 4, "Relationship", NA)))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
    2, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
    3 | Relationship == 4, "Relationship", NA)))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
    2, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
    3 | Relationship == 4, "Relationship", NA)))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
    2, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
    3 | Relationship == 4, "Relationship", NA)))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
    2, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
    3 | Relationship == 4, "Relationship", NA)))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
    2, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
    3 | Relationship == 4, "Relationship", NA)))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
    2, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
    3 | Relationship == 4, "Relationship", NA)))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
    2, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
    3 | Relationship == 4, "Relationship", NA)))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
    2, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship"))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))
df <- df %>%
  mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
    3 | Relationship == 4, "Relationship", NA)))) %>%
  mutate(RelComp = round((Rel1 + Rel2 + Rel3) / 3, 2))

Regression Model

The authors perform an ANOVA to study the effect of Fertility, Relationship and their interaction term, on the composite Religiosity score. We fit the linear model using the call: lm( RelComp ~ Fertility * RelationshipStatus, data = df ) inside our multiverse and save the result to a variable called fit_RelComp.

fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)               6.37     0.405     15.8  4.02e-41    5.58      7.17 
2 Fertilitylow             -1.16     0.534     -2.18 3.02e- 2   -2.21     -0.112
3 RelationshipStatusSi…    -1.51     0.538     -2.80 5.38e- 3   -2.57     -0.450
4 Fertilitylow:Relatio…     2.05     0.714      2.87 4.46e- 3    0.640     3.45 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              5.78      0.322     18.0  1.86e-49    5.15      6.42 
2 Fertilitylow            -0.583     0.428     -1.36 1.74e- 1   -1.42      0.259
3 RelationshipStatusSi…   -0.859     0.583     -1.47 1.41e- 1   -2.01      0.287
4 Fertilitylow:Relatio…    1.85      0.772      2.40 1.71e- 2    0.332     3.37 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)               6.37     0.402     15.8  2.17e-38    5.58      7.17 
2 Fertilitylow             -1.16     0.531     -2.19 2.95e- 2   -2.21     -0.117
3 RelationshipStatusSi…    -1.45     0.626     -2.31 2.16e- 2   -2.68     -0.215
4 Fertilitylow:Relatio…     2.43     0.828      2.94 3.67e- 3    0.800     4.07 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)               6.40     0.376     17.0  7.76e-48    5.66      7.13 
2 Fertilitylow             -1.14     0.492     -2.31 2.13e- 2   -2.11     -0.170
3 RelationshipStatusSi…    -1.70     0.493     -3.44 6.63e- 4   -2.67     -0.725
4 Fertilitylow:Relatio…     2.28     0.653      3.49 5.49e- 4    0.993     3.56 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              5.63      0.295    19.1   2.43e-56    5.05      6.21 
2 Fertilitylow            -0.325     0.391    -0.830 4.07e- 1   -1.09      0.445
3 RelationshipStatusSi…   -0.722     0.536    -1.35  1.78e- 1   -1.78      0.331
4 Fertilitylow:Relatio…    1.63      0.712     2.29  2.27e- 2    0.230     3.03 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)               6.40     0.371     17.2  6.75e-45    5.66      7.13 
2 Fertilitylow             -1.14     0.486     -2.34 1.99e- 2   -2.10     -0.182
3 RelationshipStatusSi…    -1.49     0.573     -2.60 9.97e- 3   -2.62     -0.359
4 Fertilitylow:Relatio…     2.44     0.757      3.23 1.41e- 3    0.953     3.94 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              6.18      0.352     17.5  3.73e-48    5.48      6.87 
2 Fertilitylow            -0.871     0.507     -1.72 8.70e- 2   -1.87      0.127
3 RelationshipStatusSi…   -1.12      0.497     -2.25 2.49e- 2   -2.10     -0.142
4 Fertilitylow:Relatio…    1.55      0.699      2.22 2.69e- 2    0.179     2.93 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              5.79      0.289     20.0  1.31e-57    5.22      6.36 
2 Fertilitylow            -0.551     0.412     -1.34 1.82e- 1   -1.36      0.259
3 RelationshipStatusSi…   -0.682     0.566     -1.21 2.29e- 1   -1.80      0.431
4 Fertilitylow:Relatio…    1.69      0.777      2.17 3.08e- 2    0.157     3.21 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              6.18      0.355     17.4  6.26e-44    5.48      6.87 
2 Fertilitylow            -0.871     0.510     -1.71 8.94e- 2   -1.88      0.135
3 RelationshipStatusSi…   -1.07      0.604     -1.77 7.86e- 2   -2.26      0.123
4 Fertilitylow:Relatio…    2.00      0.836      2.40 1.73e- 2    0.358     3.65 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              6.58      0.426     15.5  2.78e-44    5.74     7.41  
2 Fertilitylow            -0.920     0.483     -1.91 5.73e- 2   -1.87     0.0287
3 RelationshipStatusSi…   -1.59      0.569     -2.79 5.48e- 3   -2.70    -0.469 
4 Fertilitylow:Relatio…    1.54      0.648      2.38 1.76e- 2    0.270    2.82  
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              5.94      0.337     17.6  1.69e-54  5.28        6.61 
2 Fertilitylow            -0.457     0.383     -1.19 2.33e- 1 -1.21        0.295
3 RelationshipStatusSi…   -0.875     0.622     -1.41 1.60e- 1 -2.10        0.347
4 Fertilitylow:Relatio…    1.41      0.713      1.98 4.85e- 2  0.00942     2.81 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              6.58      0.421     15.6  2.09e-42    5.75     7.40  
2 Fertilitylow            -0.920     0.478     -1.93 5.50e- 2   -1.86     0.0196
3 RelationshipStatusSi…   -1.51      0.666     -2.26 2.42e- 2   -2.82    -0.198 
4 Fertilitylow:Relatio…    1.87      0.762      2.46 1.44e- 2    0.375    3.37  
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              6.23      0.336     18.5  1.10e-58   5.57       6.89 
2 Fertilitylow            -0.573     0.420     -1.36 1.73e- 1  -1.40       0.253
3 RelationshipStatusSi…   -1.17      0.466     -2.51 1.25e- 2  -2.08      -0.253
4 Fertilitylow:Relatio…    1.17      0.575      2.04 4.20e- 2   0.0424     2.30 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              5.80      0.275    21.1   4.81e-71   5.26       6.34 
2 Fertilitylow            -0.318     0.338    -0.940 3.48e- 1  -0.982      0.347
3 RelationshipStatusSi…   -0.640     0.519    -1.23  2.18e- 1  -1.66       0.380
4 Fertilitylow:Relatio…    1.28      0.641     2.00  4.65e- 2   0.0202     2.54 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              6.23      0.333     18.7  3.22e-55    5.57    6.88   
2 Fertilitylow            -0.573     0.416     -1.38 1.69e- 1   -1.39    0.245  
3 RelationshipStatusSi…   -1.07      0.547     -1.95 5.18e- 2   -2.14    0.00848
4 Fertilitylow:Relatio…    1.53      0.679      2.26 2.46e- 2    0.198   2.87   
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)               6.55     0.413     15.9  9.65e-40    5.74      7.37 
2 Fertilitylow             -1.28     0.564     -2.27 2.41e- 2   -2.39     -0.169
3 RelationshipStatusSi…    -1.79     0.545     -3.28 1.19e- 3   -2.86     -0.714
4 Fertilitylow:Relatio…     2.26     0.759      2.98 3.15e- 3    0.768     3.76 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              5.76      0.324    17.8   3.38e-46    5.12      6.40 
2 Fertilitylow            -0.332     0.453    -0.732 4.65e- 1   -1.22      0.560
3 RelationshipStatusSi…   -0.817     0.611    -1.34  1.82e- 1   -2.02      0.385
4 Fertilitylow:Relatio…    1.17      0.856     1.36  1.74e- 1   -0.520     2.85 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)               6.55     0.420     15.6  2.41e-35    5.73      7.38 
2 Fertilitylow             -1.28     0.574     -2.23 2.69e- 2   -2.41     -0.148
3 RelationshipStatusSi…    -1.61     0.666     -2.42 1.66e- 2   -2.92     -0.297
4 Fertilitylow:Relatio…     2.11     0.925      2.29 2.34e- 2    0.289     3.94 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)               6.55     0.369     17.7  3.42e-48    5.82      7.28 
2 Fertilitylow             -1.26     0.513     -2.45 1.48e- 2   -2.27     -0.249
3 RelationshipStatusSi…    -1.73     0.500     -3.46 6.14e- 4   -2.72     -0.748
4 Fertilitylow:Relatio…     2.16     0.697      3.10 2.15e- 3    0.786     3.53 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              5.89      0.295     19.9  2.17e-56    5.31     6.47  
2 Fertilitylow            -0.445     0.412     -1.08 2.81e- 1   -1.25     0.365 
3 RelationshipStatusSi…   -1.06      0.572     -1.85 6.48e- 2   -2.19     0.0656
4 Fertilitylow:Relatio…    1.35      0.796      1.70 9.05e- 2   -0.215    2.92  
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)               6.55     0.373     17.5  4.81e-43    5.81      7.28 
2 Fertilitylow             -1.26     0.518     -2.43 1.60e- 2   -2.28     -0.237
3 RelationshipStatusSi…    -1.72     0.614     -2.81 5.49e- 3   -2.94     -0.513
4 Fertilitylow:Relatio…     2.16     0.853      2.54 1.19e- 2    0.483     3.85 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)               6.61     0.385     17.2  9.88e-44    5.85      7.36 
2 Fertilitylow             -1.39     0.557     -2.50 1.29e- 2   -2.49     -0.297
3 RelationshipStatusSi…    -1.38     0.526     -2.63 9.09e- 3   -2.42     -0.347
4 Fertilitylow:Relatio…     1.71     0.763      2.24 2.60e- 2    0.207     3.21 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              6.07      0.311     19.5  1.11e-51    5.46      6.69 
2 Fertilitylow            -0.781     0.453     -1.73 8.56e- 2   -1.67      0.110
3 RelationshipStatusSi…   -0.770     0.597     -1.29 1.98e- 1   -1.95      0.405
4 Fertilitylow:Relatio…    1.09      0.860      1.27 2.05e- 1   -0.601     2.79 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)               6.61     0.390     16.9  5.48e-39    5.84     7.38  
2 Fertilitylow             -1.39     0.564     -2.47 1.45e- 2   -2.51    -0.280 
3 RelationshipStatusSi…    -1.30     0.643     -2.02 4.45e- 2   -2.57    -0.0325
4 Fertilitylow:Relatio…     1.71     0.926      1.84 6.71e- 2   -0.121    3.53  
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              6.64      0.427     15.6  4.05e-43    5.80     7.48  
2 Fertilitylow            -0.990     0.497     -1.99 4.72e- 2   -1.97    -0.0122
3 RelationshipStatusSi…   -1.90      0.582     -3.27 1.16e- 3   -3.05    -0.759 
4 Fertilitylow:Relatio…    1.79      0.677      2.64 8.62e- 3    0.456    3.12  
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              5.91      0.344    17.2   4.49e-50    5.23      6.58 
2 Fertilitylow            -0.386     0.398    -0.970 3.33e- 1   -1.17      0.396
3 RelationshipStatusSi…   -1.05      0.657    -1.61  1.09e- 1   -2.35      0.237
4 Fertilitylow:Relatio…    1.32      0.770     1.72  8.69e- 2   -0.192     2.83 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              6.64      0.422     15.7  1.37e-40    5.81     7.47  
2 Fertilitylow            -0.990     0.492     -2.01 4.51e- 2   -1.96    -0.0216
3 RelationshipStatusSi…   -1.79      0.692     -2.59 1.02e- 2   -3.15    -0.427 
4 Fertilitylow:Relatio…    1.93      0.812      2.37 1.84e- 2    0.327    3.52  
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)               6.62     0.368     18.0  1.38e-53    5.90      7.34 
2 Fertilitylow             -1.10     0.459     -2.41 1.66e- 2   -2.01     -0.202
3 RelationshipStatusSi…    -1.48     0.506     -2.92 3.69e- 3   -2.47     -0.483
4 Fertilitylow:Relatio…     1.38     0.626      2.21 2.79e- 2    0.150     2.61 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              6.06      0.298     20.3  1.16e-63    5.47     6.64  
2 Fertilitylow            -0.661     0.366     -1.81 7.17e- 2   -1.38     0.0585
3 RelationshipStatusSi…   -0.793     0.572     -1.39 1.67e- 1   -1.92     0.332 
4 Fertilitylow:Relatio…    1.07      0.714      1.50 1.33e- 1   -0.329    2.48  
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)               6.62     0.363     18.2  1.01e-49   5.91       7.34 
2 Fertilitylow             -1.10     0.453     -2.44 1.55e- 2  -2.00      -0.212
3 RelationshipStatusSi…    -1.36     0.601     -2.26 2.46e- 2  -2.54      -0.175
4 Fertilitylow:Relatio…     1.52     0.753      2.01 4.48e- 2   0.0351     3.00 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)               6.18     0.439     14.1  2.71e-34    5.32     7.05  
2 Fertilitylow             -1.25     0.563     -2.21 2.78e- 2   -2.35    -0.137 
3 RelationshipStatusSi…    -1.21     0.574     -2.10 3.63e- 2   -2.34    -0.0774
4 Fertilitylow:Relatio…     2.10     0.752      2.80 5.49e- 3    0.624    3.58  
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              5.52      0.338    16.4   1.66e-42    4.86      6.19 
2 Fertilitylow            -0.454     0.449    -1.01  3.13e- 1   -1.34      0.430
3 RelationshipStatusSi…   -0.161     0.629    -0.256 7.98e- 1   -1.40      1.08 
4 Fertilitylow:Relatio…    1.22      0.817     1.49  1.37e- 1   -0.390     2.83 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              6.18      0.439     14.1  5.32e-32    5.32      7.05 
2 Fertilitylow            -1.25      0.564     -2.21 2.82e- 2   -2.36     -0.134
3 RelationshipStatusSi…   -0.821     0.687     -1.20 2.33e- 1   -2.17      0.533
4 Fertilitylow:Relatio…    2.01      0.882      2.28 2.37e- 2    0.271     3.75 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)               6.23     0.384     16.2  8.68e-44    5.47      6.98 
2 Fertilitylow             -1.16     0.501     -2.32 2.10e- 2   -2.15     -0.176
3 RelationshipStatusSi…    -1.13     0.517     -2.19 2.90e- 2   -2.15     -0.117
4 Fertilitylow:Relatio…     1.85     0.678      2.73 6.73e- 3    0.515     3.18 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)             5.63       0.306    18.4   3.21e-52    5.02      6.23 
2 Fertilitylow           -0.520      0.405    -1.28  2.00e- 1   -1.32      0.277
3 RelationshipStatusSi…  -0.0844     0.569    -0.148 8.82e- 1   -1.20      1.03 
4 Fertilitylow:Relatio…   1.18       0.739     1.59  1.12e- 1   -0.277     2.63 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              6.23      0.380     16.4  2.95e-41    5.48      6.98 
2 Fertilitylow            -1.16      0.496     -2.34 2.01e- 2   -2.14     -0.184
3 RelationshipStatusSi…   -0.684     0.607     -1.13 2.61e- 1   -1.88      0.512
4 Fertilitylow:Relatio…    1.82      0.787      2.31 2.16e- 2    0.269     3.37 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              6.28      0.405     15.5  2.67e-39    5.48      7.08 
2 Fertilitylow            -1.42      0.545     -2.61 9.67e- 3   -2.49     -0.347
3 RelationshipStatusSi…   -0.806     0.531     -1.52 1.30e- 1   -1.85      0.239
4 Fertilitylow:Relatio…    1.72      0.735      2.34 1.99e- 2    0.274     3.17 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              5.74      0.308    18.7   1.34e-50    5.13      6.34 
2 Fertilitylow            -0.707     0.438    -1.61  1.08e- 1   -1.57      0.156
3 RelationshipStatusSi…    0.271     0.593     0.457 6.48e- 1   -0.897     1.44 
4 Fertilitylow:Relatio…    0.633     0.804     0.787 4.32e- 1   -0.950     2.22 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              6.28      0.404    15.5   1.89e-36    5.48      7.08 
2 Fertilitylow            -1.42      0.544    -2.61  9.70e- 3   -2.49     -0.348
3 RelationshipStatusSi…   -0.271     0.646    -0.419 6.76e- 1   -1.54      1.00 
4 Fertilitylow:Relatio…    1.35      0.863     1.56  1.20e- 1   -0.355     3.05 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              6.24      0.457     13.6  6.10e-36    5.34     7.14  
2 Fertilitylow            -0.549     0.512     -1.07 2.84e- 1   -1.56     0.458 
3 RelationshipStatusSi…   -1.24      0.600     -2.06 3.98e- 2   -2.42    -0.0576
4 Fertilitylow:Relatio…    1.20      0.679      1.77 7.80e- 2   -0.135    2.53  
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)             5.59       0.349    16.0   2.17e-46    4.90      6.27 
2 Fertilitylow           -0.0614     0.395    -0.155 8.77e- 1   -0.838     0.715
3 RelationshipStatusSi…  -0.231      0.664    -0.349 7.27e- 1   -1.54      1.07 
4 Fertilitylow:Relatio…   0.771      0.753     1.02  3.06e- 1   -0.709     2.25 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              6.24      0.454     13.7  1.66e-34    5.35      7.14 
2 Fertilitylow            -0.549     0.509     -1.08 2.81e- 1   -1.55      0.452
3 RelationshipStatusSi…   -0.886     0.721     -1.23 2.20e- 1   -2.30      0.532
4 Fertilitylow:Relatio…    1.26      0.814      1.55 1.23e- 1   -0.343     2.86 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              6.23      0.374     16.7  2.67e-49    5.50      6.97 
2 Fertilitylow            -0.614     0.448     -1.37 1.71e- 1   -1.50      0.267
3 RelationshipStatusSi…   -0.842     0.496     -1.70 9.03e- 2   -1.82      0.133
4 Fertilitylow:Relatio…    0.781     0.602      1.30 1.95e- 1   -0.402     1.96 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)             5.67       0.286    19.8   7.31e-64    5.11      6.23 
2 Fertilitylow           -0.193      0.350    -0.552 5.81e- 1   -0.880     0.494
3 RelationshipStatusSi…   0.322      0.560     0.575 5.65e- 1   -0.778     1.42 
4 Fertilitylow:Relatio…   0.0699     0.677     0.103 9.18e- 1   -1.26      1.40 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              6.23      0.371    16.8   1.47e-46    5.50      6.96 
2 Fertilitylow            -0.614     0.445    -1.38  1.68e- 1   -1.49      0.261
3 RelationshipStatusSi…   -0.241     0.604    -0.398 6.91e- 1   -1.43      0.948
4 Fertilitylow:Relatio…    0.491     0.726     0.676 4.99e- 1   -0.938     1.92 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)               6.56     0.450     14.6  8.40e-35    5.68      7.45 
2 Fertilitylow             -1.42     0.597     -2.37 1.87e- 2   -2.59     -0.238
3 RelationshipStatusSi…    -1.76     0.597     -2.94 3.57e- 3   -2.94     -0.581
4 Fertilitylow:Relatio…     2.38     0.810      2.94 3.61e- 3    0.785     3.98 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              5.86      0.359     16.3  1.33e-40    5.15      6.57 
2 Fertilitylow            -0.656     0.482     -1.36 1.75e- 1   -1.61      0.294
3 RelationshipStatusSi…   -0.955     0.649     -1.47 1.42e- 1   -2.23      0.323
4 Fertilitylow:Relatio…    1.98      0.904      2.19 2.95e- 2    0.199     3.76 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)               6.56     0.451     14.6  5.64e-32    5.67      7.45 
2 Fertilitylow             -1.42     0.598     -2.36 1.91e- 2   -2.60     -0.234
3 RelationshipStatusSi…    -1.66     0.700     -2.37 1.88e- 2   -3.04     -0.279
4 Fertilitylow:Relatio…     2.74     0.965      2.84 5.10e- 3    0.833     4.64 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)               6.56     0.413     15.9  4.43e-41     5.75     7.37 
2 Fertilitylow             -1.38     0.546     -2.52 1.22e- 2    -2.45    -0.303
3 RelationshipStatusSi…    -1.88     0.541     -3.48 5.79e- 4    -2.95    -0.819
4 Fertilitylow:Relatio…     2.63     0.736      3.57 4.12e- 4     1.18     4.08 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              5.67      0.325    17.5   6.92e-47  5.03        6.31 
2 Fertilitylow            -0.342     0.438    -0.783 4.34e- 1 -1.20        0.519
3 RelationshipStatusSi…   -0.684     0.594    -1.15  2.51e- 1 -1.85        0.486
4 Fertilitylow:Relatio…    1.63      0.832     1.96  5.08e- 2 -0.00559     3.27 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)               6.56     0.410     16.0  5.32e-38    5.75      7.37 
2 Fertilitylow             -1.38     0.542     -2.54 1.17e- 2   -2.45     -0.310
3 RelationshipStatusSi…    -1.58     0.635     -2.48 1.38e- 2   -2.83     -0.325
4 Fertilitylow:Relatio…     2.67     0.877      3.04 2.67e- 3    0.938     4.40 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)               6.40     0.396     16.2  3.08e-40    5.62     7.18  
2 Fertilitylow             -1.15     0.574     -2.00 4.72e- 2   -2.28    -0.0145
3 RelationshipStatusSi…    -1.32     0.559     -2.36 1.93e- 2   -2.42    -0.216 
4 Fertilitylow:Relatio…     1.82     0.808      2.26 2.50e- 2    0.231    3.41  
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              5.89      0.328    18.0   2.95e-46    5.25      6.54 
2 Fertilitylow            -0.644     0.471    -1.37  1.73e- 1   -1.57      0.285
3 RelationshipStatusSi…   -0.569     0.640    -0.889 3.75e- 1   -1.83      0.692
4 Fertilitylow:Relatio…    1.63      0.932     1.75  8.15e- 2   -0.206     3.47 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)               6.40     0.400     16.0  2.92e-36    5.61    7.19   
2 Fertilitylow             -1.15     0.581     -1.97 5.02e- 2   -2.29    0.00125
3 RelationshipStatusSi…    -1.08     0.683     -1.58 1.16e- 1   -2.43    0.269  
4 Fertilitylow:Relatio…     2.13     0.996      2.14 3.37e- 2    0.167   4.10   
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              6.70      0.470     14.3  8.12e-38    5.78      7.62 
2 Fertilitylow            -0.926     0.532     -1.74 8.28e- 2   -1.97      0.121
3 RelationshipStatusSi…   -1.75      0.627     -2.79 5.58e- 3   -2.98     -0.514
4 Fertilitylow:Relatio…    1.63      0.716      2.28 2.34e- 2    0.222     3.04 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              5.99      0.372     16.1  1.65e-45   5.25       6.72 
2 Fertilitylow            -0.429     0.423     -1.02 3.10e- 1  -1.26       0.401
3 RelationshipStatusSi…   -0.905     0.685     -1.32 1.87e- 1  -2.25       0.440
4 Fertilitylow:Relatio…    1.53      0.793      1.92 5.52e- 2  -0.0339     3.09 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              6.70      0.463     14.5  2.82e-36    5.79      7.61 
2 Fertilitylow            -0.926     0.525     -1.76 7.89e- 2   -1.96      0.108
3 RelationshipStatusSi…   -1.62      0.730     -2.22 2.74e- 2   -3.05     -0.181
4 Fertilitylow:Relatio…    2.02      0.843      2.40 1.70e- 2    0.363     3.68 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              6.44      0.371     17.4  4.03e-51   5.72       7.17 
2 Fertilitylow            -0.725     0.462     -1.57 1.18e- 1  -1.63       0.184
3 RelationshipStatusSi…   -1.36      0.514     -2.65 8.47e- 3  -2.37      -0.349
4 Fertilitylow:Relatio…    1.32      0.636      2.08 3.82e- 2   0.0723     2.57 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              5.89      0.305    19.3   1.03e-59    5.29      6.49 
2 Fertilitylow            -0.361     0.374    -0.967 3.34e- 1   -1.10      0.373
3 RelationshipStatusSi…   -0.552     0.572    -0.965 3.35e- 1   -1.68      0.572
4 Fertilitylow:Relatio…    1.23      0.719     1.72  8.68e- 2   -0.179     2.65 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              6.44      0.366     17.6  5.41e-48    5.72     7.17  
2 Fertilitylow            -0.725     0.456     -1.59 1.13e- 1   -1.62     0.173 
3 RelationshipStatusSi…   -1.10      0.600     -1.84 6.70e- 2   -2.28     0.0779
4 Fertilitylow:Relatio…    1.60      0.757      2.11 3.56e- 2    0.108    3.09  
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)               6.87     0.450     15.3  9.23e-36    5.99      7.76 
2 Fertilitylow             -1.50     0.618     -2.43 1.59e- 2   -2.72     -0.284
3 RelationshipStatusSi…    -2.05     0.596     -3.45 6.90e- 4   -3.23     -0.878
4 Fertilitylow:Relatio…     2.36     0.836      2.82 5.27e- 3    0.709     4.01 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              5.93      0.353    16.8   1.40e-40    5.24      6.63 
2 Fertilitylow            -0.450     0.496    -0.908 3.65e- 1   -1.43      0.527
3 RelationshipStatusSi…   -0.859     0.683    -1.26  2.10e- 1   -2.21      0.487
4 Fertilitylow:Relatio…    1.06      0.971     1.09  2.77e- 1   -0.857     2.97 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)               6.87     0.462     14.9  3.90e-31   5.96       7.79 
2 Fertilitylow             -1.50     0.634     -2.37 1.91e- 2  -2.75      -0.249
3 RelationshipStatusSi…    -1.80     0.746     -2.42 1.69e- 2  -3.28      -0.329
4 Fertilitylow:Relatio…     2.11     1.05       2.01 4.62e- 2   0.0362     4.18 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)               6.80     0.397     17.1  1.32e-43    6.02      7.58 
2 Fertilitylow             -1.42     0.562     -2.52 1.24e- 2   -2.52     -0.309
3 RelationshipStatusSi…    -1.93     0.540     -3.56 4.40e- 4   -2.99     -0.861
4 Fertilitylow:Relatio…     2.21     0.767      2.88 4.38e- 3    0.695     3.72 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              6.04      0.317     19.1  4.21e-50    5.42      6.67 
2 Fertilitylow            -0.569     0.449     -1.27 2.07e- 1   -1.45      0.317
3 RelationshipStatusSi…   -1.13      0.634     -1.78 7.62e- 2   -2.38      0.120
4 Fertilitylow:Relatio…    1.38      0.903      1.53 1.29e- 1   -0.402     3.15 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)               6.80     0.404     16.9  3.54e-38    6.00      7.60 
2 Fertilitylow             -1.42     0.571     -2.48 1.41e- 2   -2.54     -0.289
3 RelationshipStatusSi…    -1.89     0.680     -2.77 6.14e- 3   -3.23     -0.545
4 Fertilitylow:Relatio…     2.22     0.967      2.30 2.27e- 2    0.315     4.13 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)               6.74     0.425     15.9  3.94e-37    5.90      7.58 
2 Fertilitylow             -1.43     0.620     -2.30 2.24e- 2   -2.65     -0.205
3 RelationshipStatusSi…    -1.43     0.585     -2.44 1.54e- 2   -2.58     -0.276
4 Fertilitylow:Relatio…     1.55     0.857      1.81 7.14e- 2   -0.136     3.24 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              6.21      0.345    18.0   1.42e-43     5.53     6.89 
2 Fertilitylow            -0.859     0.503    -1.71  8.94e- 2    -1.85     0.133
3 RelationshipStatusSi…   -0.844     0.666    -1.27  2.06e- 1    -2.16     0.469
4 Fertilitylow:Relatio…    0.937     0.984     0.951 3.43e- 1    -1.00     2.88 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)               6.74     0.436     15.4  2.03e-32    5.88     7.60  
2 Fertilitylow             -1.43     0.638     -2.24 2.66e- 2   -2.69    -0.168 
3 RelationshipStatusSi…    -1.37     0.725     -1.89 6.05e- 2   -2.80     0.0614
4 Fertilitylow:Relatio…     1.51     1.07       1.41 1.62e- 1   -0.611    3.62  
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)               6.90     0.467     14.8  1.64e-38    5.98     7.82  
2 Fertilitylow             -1.15     0.545     -2.11 3.53e- 2   -2.22    -0.0795
3 RelationshipStatusSi…    -2.17     0.642     -3.38 7.98e- 4   -3.44    -0.910 
4 Fertilitylow:Relatio…     1.98     0.747      2.65 8.51e- 3    0.507    3.44  
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              6.08      0.377     16.1  7.77e-44    5.33      6.82 
2 Fertilitylow            -0.507     0.436     -1.16 2.46e- 1   -1.37      0.351
3 RelationshipStatusSi…   -1.26      0.741     -1.70 8.91e- 2   -2.72      0.194
4 Fertilitylow:Relatio…    1.55      0.867      1.78 7.52e- 2   -0.158     3.25 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)               6.90     0.463     14.9  7.19e-36    5.99     7.81  
2 Fertilitylow             -1.15     0.541     -2.13 3.42e- 2   -2.22    -0.0866
3 RelationshipStatusSi…    -2.09     0.778     -2.68 7.90e- 3   -3.62    -0.552 
4 Fertilitylow:Relatio…     2.19     0.912      2.40 1.70e- 2    0.396    3.99  
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)               6.85     0.405     16.9  5.44e-47   6.05       7.64 
2 Fertilitylow             -1.23     0.505     -2.44 1.52e- 2  -2.22      -0.239
3 RelationshipStatusSi…    -1.64     0.558     -2.93 3.62e- 3  -2.73      -0.537
4 Fertilitylow:Relatio…     1.43     0.690      2.07 3.94e- 2   0.0699     2.78 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              6.24      0.330     18.9  4.79e-55    5.59     6.89  
2 Fertilitylow            -0.806     0.403     -2.00 4.63e- 2   -1.60    -0.0132
3 RelationshipStatusSi…   -0.924     0.633     -1.46 1.45e- 1   -2.17     0.321 
4 Fertilitylow:Relatio…    1.22      0.796      1.53 1.27e- 1   -0.349    2.78  
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)               6.85     0.401     17.1  3.78e-43   6.06       7.64 
2 Fertilitylow             -1.23     0.500     -2.46 1.45e- 2  -2.22      -0.247
3 RelationshipStatusSi…    -1.53     0.665     -2.31 2.20e- 2  -2.84      -0.223
4 Fertilitylow:Relatio…     1.64     0.839      1.96 5.14e- 2  -0.0101     3.30 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)               6.43     0.471     13.6  3.48e-31    5.50      7.36 
2 Fertilitylow             -1.38     0.619     -2.24 2.64e- 2   -2.60     -0.164
3 RelationshipStatusSi…    -1.48     0.628     -2.36 1.94e- 2   -2.72     -0.242
4 Fertilitylow:Relatio…     2.21     0.841      2.63 9.16e- 3    0.553     3.87 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              5.66      0.372    15.2   2.96e-36    4.92      6.39 
2 Fertilitylow            -0.547     0.503    -1.09  2.78e- 1   -1.54      0.445
3 RelationshipStatusSi…   -0.209     0.694    -0.301 7.64e- 1   -1.58      1.16 
4 Fertilitylow:Relatio…    1.24      0.927     1.34  1.83e- 1   -0.589     3.06 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              6.43      0.471     13.7  4.46e-29    5.50      7.36 
2 Fertilitylow            -1.38      0.617     -2.24 2.64e- 2   -2.60     -0.164
3 RelationshipStatusSi…   -0.983     0.747     -1.32 1.90e- 1   -2.46      0.491
4 Fertilitylow:Relatio…    2.07      0.987      2.10 3.70e- 2    0.127     4.02 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)               6.43     0.412     15.6  4.11e-39    5.62      7.24 
2 Fertilitylow             -1.35     0.555     -2.44 1.55e- 2   -2.44     -0.260
3 RelationshipStatusSi…    -1.34     0.566     -2.36 1.88e- 2   -2.45     -0.224
4 Fertilitylow:Relatio…     2.00     0.766      2.62 9.39e- 3    0.496     3.51 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              5.75      0.337    17.1   3.15e-44    5.09      6.41 
2 Fertilitylow            -0.629     0.456    -1.38  1.69e- 1   -1.53      0.270
3 RelationshipStatusSi…   -0.102     0.630    -0.162 8.71e- 1   -1.34      1.14 
4 Fertilitylow:Relatio…    1.15      0.853     1.35  1.78e- 1   -0.527     2.83 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              6.43      0.409     15.7  1.39e-36    5.62      7.24 
2 Fertilitylow            -1.35      0.550     -2.46 1.49e- 2   -2.44     -0.267
3 RelationshipStatusSi…   -0.782     0.665     -1.18 2.41e- 1   -2.09      0.530
4 Fertilitylow:Relatio…    1.88      0.898      2.09 3.80e- 2    0.104     3.65 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)               6.50     0.452     14.4  2.78e-33  5.61        7.39 
2 Fertilitylow             -1.46     0.613     -2.38 1.81e- 2 -2.67       -0.252
3 RelationshipStatusSi…    -1.05     0.600     -1.75 8.13e- 2 -2.23        0.132
4 Fertilitylow:Relatio…     1.66     0.840      1.98 4.91e- 2  0.00670     3.32 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              5.87      0.349    16.8   3.74e-41     5.18     6.56 
2 Fertilitylow            -0.758     0.498    -1.52  1.29e- 1    -1.74     0.224
3 RelationshipStatusSi…    0.106     0.676     0.157 8.76e- 1    -1.23     1.44 
4 Fertilitylow:Relatio…    0.634     0.933     0.680 4.97e- 1    -1.20     2.47 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              6.50      0.452    14.4   9.47e-31    5.60      7.39 
2 Fertilitylow            -1.46      0.613    -2.38  1.83e- 2   -2.67     -0.251
3 RelationshipStatusSi…   -0.520     0.731    -0.710 4.78e- 1   -1.96      0.924
4 Fertilitylow:Relatio…    1.34      0.994     1.34  1.81e- 1   -0.626     3.30 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              6.39      0.486     13.2  6.25e-33   5.44       7.35 
2 Fertilitylow            -0.602     0.549     -1.10 2.73e- 1  -1.68       0.477
3 RelationshipStatusSi…   -1.48      0.652     -2.28 2.32e- 2  -2.77      -0.203
4 Fertilitylow:Relatio…    1.38      0.742      1.86 6.35e- 2  -0.0780     2.84 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              5.68      0.382    14.9   8.49e-40    4.93      6.43 
2 Fertilitylow            -0.106     0.434    -0.246 8.06e- 1   -0.959     0.746
3 RelationshipStatusSi…   -0.397     0.725    -0.547 5.84e- 1   -1.82      1.03 
4 Fertilitylow:Relatio…    1.07      0.834     1.28  2.03e- 1   -0.575     2.71 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              6.39      0.482     13.3  1.68e-31    5.44      7.34 
2 Fertilitylow            -0.602     0.544     -1.11 2.69e- 1   -1.67      0.469
3 RelationshipStatusSi…   -1.11      0.776     -1.43 1.53e- 1   -2.64      0.417
4 Fertilitylow:Relatio…    1.56      0.890      1.75 8.07e- 2   -0.192     3.31 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              6.40      0.408     15.7  2.38e-43    5.60     7.21  
2 Fertilitylow            -0.700     0.491     -1.43 1.54e- 1   -1.66     0.265 
3 RelationshipStatusSi…   -1.06      0.547     -1.93 5.44e- 2   -2.13     0.0200
4 Fertilitylow:Relatio…    0.928     0.667      1.39 1.65e- 1   -0.383    2.24  
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              5.77      0.317    18.2   6.09e-54     5.15     6.39 
2 Fertilitylow            -0.258     0.386    -0.669 5.04e- 1    -1.02     0.500
3 RelationshipStatusSi…    0.191     0.624     0.306 7.60e- 1    -1.04     1.42 
4 Fertilitylow:Relatio…    0.314     0.763     0.412 6.81e- 1    -1.19     1.81 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              6.40      0.404    15.8   8.93e-41    5.61      7.20 
2 Fertilitylow            -0.700     0.486    -1.44  1.51e- 1   -1.66      0.257
3 RelationshipStatusSi…   -0.444     0.667    -0.665 5.06e- 1   -1.76      0.870
4 Fertilitylow:Relatio…    0.756     0.812     0.930 3.53e- 1   -0.843     2.35 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)               6.31     0.470     13.4  8.07e-31    5.38     7.24  
2 Fertilitylow             -1.22     0.612     -2.00 4.66e- 2   -2.43    -0.0187
3 RelationshipStatusSi…    -1.49     0.614     -2.42 1.64e- 2   -2.70    -0.275 
4 Fertilitylow:Relatio…     2.09     0.814      2.57 1.09e- 2    0.485    3.69  
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              5.66      0.366     15.5  1.10e-37   4.94       6.38 
2 Fertilitylow            -0.549     0.486     -1.13 2.60e- 1  -1.51       0.409
3 RelationshipStatusSi…   -0.725     0.660     -1.10 2.73e- 1  -2.02       0.575
4 Fertilitylow:Relatio…    1.72      0.882      1.95 5.25e- 2  -0.0189     3.46 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)               6.31     0.472     13.4  1.92e-28    5.38     7.24  
2 Fertilitylow             -1.22     0.614     -1.99 4.78e- 2   -2.44    -0.0119
3 RelationshipStatusSi…    -1.37     0.723     -1.90 5.92e- 2   -2.80     0.0539
4 Fertilitylow:Relatio…     2.39     0.958      2.50 1.33e- 2    0.505    4.28  
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)               6.36     0.442     14.4  2.32e-35    5.49     7.23  
2 Fertilitylow             -1.19     0.569     -2.10 3.70e- 2   -2.31    -0.0723
3 RelationshipStatusSi…    -1.71     0.568     -3.01 2.86e- 3   -2.83    -0.591 
4 Fertilitylow:Relatio…     2.13     0.751      2.84 4.90e- 3    0.651    3.61  
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              5.49      0.336    16.3   2.26e-42    4.83      6.16 
2 Fertilitylow            -0.332     0.447    -0.744 4.58e- 1   -1.21      0.547
3 RelationshipStatusSi…   -0.558     0.611    -0.913 3.62e- 1   -1.76      0.644
4 Fertilitylow:Relatio…    1.36      0.820     1.66  9.90e- 2   -0.257     2.97 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)               6.36     0.440     14.5  7.72e-33    5.49     7.23  
2 Fertilitylow             -1.19     0.567     -2.10 3.66e- 2   -2.31    -0.0750
3 RelationshipStatusSi…    -1.42     0.667     -2.14 3.40e- 2   -2.74    -0.109 
4 Fertilitylow:Relatio…     2.22     0.883      2.51 1.28e- 2    0.477    3.96  
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              5.95      0.402     14.8  1.20e-35    5.16      6.74 
2 Fertilitylow            -0.760     0.579     -1.31 1.90e- 1   -1.90      0.380
3 RelationshipStatusSi…   -0.887     0.562     -1.58 1.16e- 1   -1.99      0.220
4 Fertilitylow:Relatio…    1.31      0.799      1.64 1.03e- 1   -0.267     2.88 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              5.62      0.327    17.2   1.10e-43    4.97      6.26 
2 Fertilitylow            -0.463     0.470    -0.984 3.26e- 1   -1.39      0.464
3 RelationshipStatusSi…   -0.459     0.641    -0.716 4.74e- 1   -1.72      0.803
4 Fertilitylow:Relatio…    1.34      0.892     1.51  1.33e- 1   -0.414     3.10 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              5.95      0.410     14.5  3.78e-32    5.14      6.76 
2 Fertilitylow            -0.760     0.589     -1.29 1.99e- 1   -1.92      0.403
3 RelationshipStatusSi…   -0.794     0.694     -1.14 2.55e- 1   -2.16      0.577
4 Fertilitylow:Relatio…    1.64      0.970      1.69 9.28e- 2   -0.275     3.55 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              6.48      0.492     13.2  4.55e-33    5.51      7.45 
2 Fertilitylow            -0.855     0.555     -1.54 1.25e- 1   -1.95      0.237
3 RelationshipStatusSi…   -1.50      0.650     -2.31 2.12e- 2   -2.78     -0.226
4 Fertilitylow:Relatio…    1.25      0.740      1.68 9.30e- 2   -0.209     2.70 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              5.82      0.383    15.2   2.85e-41    5.06      6.57 
2 Fertilitylow            -0.419     0.436    -0.962 3.36e- 1   -1.28      0.437
3 RelationshipStatusSi…   -0.687     0.716    -0.960 3.38e- 1   -2.10      0.721
4 Fertilitylow:Relatio…    1.02      0.820     1.24  2.15e- 1   -0.594     2.63 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              6.48      0.490     13.2  1.90e-31    5.52      7.44 
2 Fertilitylow            -0.855     0.553     -1.54 1.23e- 1   -1.94      0.234
3 RelationshipStatusSi…   -1.35      0.774     -1.74 8.22e- 2   -2.88      0.174
4 Fertilitylow:Relatio…    1.45      0.883      1.65 1.01e- 1   -0.285     3.19 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              6.01      0.382    15.7   1.40e-43    5.26     6.76  
2 Fertilitylow            -0.315     0.478    -0.659 5.10e- 1   -1.25     0.624 
3 RelationshipStatusSi…   -0.947     0.525    -1.80  7.21e- 2   -1.98     0.0855
4 Fertilitylow:Relatio…    0.629     0.652     0.964 3.35e- 1   -0.653    1.91  
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              5.62      0.309    18.2   4.74e-54    5.02      6.23 
2 Fertilitylow            -0.202     0.383    -0.528 5.98e- 1   -0.955     0.551
3 RelationshipStatusSi…   -0.413     0.589    -0.701 4.84e- 1   -1.57      0.744
4 Fertilitylow:Relatio…    0.777     0.731     1.06  2.88e- 1   -0.660     2.21 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              6.01      0.381    15.8   9.39e-41    5.26      6.76 
2 Fertilitylow            -0.315     0.476    -0.661 5.09e- 1   -1.25      0.623
3 RelationshipStatusSi…   -0.800     0.627    -1.28  2.03e- 1   -2.03      0.434
4 Fertilitylow:Relatio…    0.889     0.780     1.14  2.55e- 1   -0.647     2.43 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)               5.97     0.499     12.0  1.12e-25    4.98     6.95  
2 Fertilitylow             -1.10     0.637     -1.73 8.53e- 2   -2.36     0.154 
3 RelationshipStatusSi…    -1.30     0.652     -1.99 4.77e- 2   -2.58    -0.0135
4 Fertilitylow:Relatio…     2.02     0.854      2.37 1.86e- 2    0.341    3.71  
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              5.34      0.382    14.0   3.76e-32    4.59      6.10 
2 Fertilitylow            -0.449     0.506    -0.887 3.76e- 1   -1.45      0.548
3 RelationshipStatusSi…   -0.479     0.713    -0.671 5.03e- 1   -1.88      0.926
4 Fertilitylow:Relatio…    1.61      0.932     1.73  8.58e- 2   -0.229     3.45 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)               5.97     0.503     11.9  8.95e-24    4.97      6.96 
2 Fertilitylow             -1.10     0.642     -1.71 8.85e- 2   -2.37      0.168
3 RelationshipStatusSi…    -1.10     0.787     -1.40 1.64e- 1   -2.65      0.453
4 Fertilitylow:Relatio…     2.26     1.02       2.23 2.74e- 2    0.255     4.27 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)               6.13     0.443     13.8  9.36e-33    5.26     7.01  
2 Fertilitylow             -1.23     0.576     -2.13 3.39e- 2   -2.36    -0.0940
3 RelationshipStatusSi…    -1.32     0.593     -2.23 2.64e- 2   -2.49    -0.156 
4 Fertilitylow:Relatio…     1.89     0.780      2.42 1.64e- 2    0.349    3.42  
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              5.51      0.351    15.7   3.97e-39    4.82      6.20 
2 Fertilitylow            -0.638     0.463    -1.38  1.69e- 1   -1.55      0.274
3 RelationshipStatusSi…   -0.392     0.648    -0.605 5.46e- 1   -1.67      0.885
4 Fertilitylow:Relatio…    1.52      0.856     1.78  7.64e- 2   -0.163     3.21 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)               6.13     0.445     13.8  2.21e-30    5.26     7.01  
2 Fertilitylow             -1.23     0.577     -2.13 3.48e- 2   -2.37    -0.0885
3 RelationshipStatusSi…    -1.02     0.703     -1.45 1.50e- 1   -2.40     0.370 
4 Fertilitylow:Relatio…     2.11     0.923      2.29 2.32e- 2    0.292    3.93  
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              6.17      0.443     13.9  7.05e-32   5.30       7.05 
2 Fertilitylow            -1.42      0.607     -2.33 2.05e- 2  -2.61      -0.221
3 RelationshipStatusSi…   -0.851     0.589     -1.45 1.50e- 1  -2.01       0.309
4 Fertilitylow:Relatio…    1.55      0.832      1.86 6.39e- 2  -0.0903     3.19 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              5.65      0.345    16.4   7.63e-40    4.98      6.33 
2 Fertilitylow            -0.841     0.495    -1.70  9.09e- 2   -1.82      0.135
3 RelationshipStatusSi…    0.131     0.652     0.201 8.41e- 1   -1.15      1.42 
4 Fertilitylow:Relatio…    0.800     0.911     0.878 3.81e- 1   -0.996     2.60 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              6.17      0.445    13.9   1.96e-29    5.30      7.05 
2 Fertilitylow            -1.42      0.609    -2.32  2.13e- 2   -2.62     -0.214
3 RelationshipStatusSi…   -0.389     0.711    -0.547 5.85e- 1   -1.79      1.01 
4 Fertilitylow:Relatio…    1.38      0.979     1.41  1.62e- 1   -0.557     3.31 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              6.01      0.526    11.4   4.40e-26    4.98     7.04  
2 Fertilitylow            -0.331     0.587    -0.564 5.73e- 1   -1.49     0.823 
3 RelationshipStatusSi…   -1.30      0.684    -1.89  5.92e- 2   -2.64     0.0508
4 Fertilitylow:Relatio…    1.03      0.774     1.33  1.85e- 1   -0.493    2.55  
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)            5.40        0.400   13.5    5.76e-34    4.61      6.19 
2 Fertilitylow           0.00873     0.453    0.0193 9.85e- 1   -0.881     0.899
3 RelationshipStatusSi… -0.540       0.745   -0.725  4.69e- 1   -2.00      0.925
4 Fertilitylow:Relatio…  1.03        0.851    1.21   2.26e- 1   -0.640     2.70 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              6.01      0.526    11.4   6.90e-25    4.97      7.05 
2 Fertilitylow            -0.331     0.587    -0.564 5.73e- 1   -1.49      0.825
3 RelationshipStatusSi…   -1.15      0.818    -1.40  1.62e- 1   -2.76      0.462
4 Fertilitylow:Relatio…    1.37      0.928     1.48  1.40e- 1   -0.454     3.20 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              6.09      0.410    14.8   2.41e-39    5.28      6.90 
2 Fertilitylow            -0.512     0.500    -1.02  3.06e- 1   -1.49      0.471
3 RelationshipStatusSi…   -0.892     0.549    -1.63  1.05e- 1   -1.97      0.187
4 Fertilitylow:Relatio…    0.580     0.676     0.858 3.91e- 1   -0.749     1.91 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              5.55      0.320    17.3   1.20e-49    4.92      6.17 
2 Fertilitylow            -0.208     0.394    -0.527 5.99e- 1   -0.984     0.568
3 RelationshipStatusSi…    0.171     0.618     0.277 7.82e- 1   -1.04      1.39 
4 Fertilitylow:Relatio…    0.113     0.761     0.148 8.82e- 1   -1.38      1.61 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              6.09      0.411    14.8   1.08e-36     5.28     6.90 
2 Fertilitylow            -0.512     0.500    -1.02  3.07e- 1    -1.50     0.473
3 RelationshipStatusSi…   -0.373     0.669    -0.559 5.77e- 1    -1.69     0.943
4 Fertilitylow:Relatio…    0.417     0.820     0.508 6.12e- 1    -1.20     2.03 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)               6.54     0.519     12.6  6.75e-27    5.52      7.57 
2 Fertilitylow             -1.50     0.677     -2.21 2.82e- 2   -2.83     -0.162
3 RelationshipStatusSi…    -1.69     0.680     -2.49 1.35e- 2   -3.03     -0.354
4 Fertilitylow:Relatio…     2.44     0.914      2.67 8.22e- 3    0.638     4.24 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              5.81      0.404     14.4  2.73e-32    5.01      6.61 
2 Fertilitylow            -0.721     0.539     -1.34 1.83e- 1   -1.78      0.342
3 RelationshipStatusSi…   -0.861     0.734     -1.17 2.42e- 1   -2.31      0.587
4 Fertilitylow:Relatio…    2.16      1.02       2.12 3.52e- 2    0.152     4.18 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)               6.54     0.523     12.5  1.46e-24    5.51    7.58   
2 Fertilitylow             -1.50     0.682     -2.20 2.97e- 2   -2.84   -0.150  
3 RelationshipStatusSi…    -1.59     0.805     -1.97 5.03e- 2   -3.18    0.00218
4 Fertilitylow:Relatio…     2.94     1.10       2.67 8.52e- 3    0.762   5.12   
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)               6.57     0.485     13.5  4.90e-31    5.62      7.53 
2 Fertilitylow             -1.42     0.626     -2.26 2.45e- 2   -2.65     -0.184
3 RelationshipStatusSi…    -1.93     0.622     -3.09 2.23e- 3   -3.15     -0.699
4 Fertilitylow:Relatio…     2.50     0.840      2.98 3.20e- 3    0.848     4.16 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              5.60      0.369    15.2   2.06e-36    4.87      6.32 
2 Fertilitylow            -0.422     0.494    -0.854 3.94e- 1   -1.40      0.551
3 RelationshipStatusSi…   -0.648     0.674    -0.961 3.37e- 1   -1.98      0.680
4 Fertilitylow:Relatio…    1.74      0.950     1.83  6.81e- 2   -0.131     3.61 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)               6.57     0.484     13.6  1.39e-28    5.62      7.53 
2 Fertilitylow             -1.42     0.625     -2.27 2.45e- 2   -2.65     -0.185
3 RelationshipStatusSi…    -1.62     0.736     -2.21 2.87e- 2   -3.08     -0.171
4 Fertilitylow:Relatio…     2.74     1.01       2.70 7.61e- 3    0.737     4.74 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)               6.31     0.455     13.8  1.30e-30    5.41      7.20 
2 Fertilitylow             -1.14     0.651     -1.76 8.06e- 2   -2.43      0.141
3 RelationshipStatusSi…    -1.11     0.638     -1.74 8.33e- 2   -2.37      0.148
4 Fertilitylow:Relatio…     1.64     0.916      1.79 7.46e- 2   -0.165     3.45 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              5.83      0.371    15.7   3.63e-36    5.10      6.57 
2 Fertilitylow            -0.697     0.531    -1.31  1.91e- 1   -1.74      0.350
3 RelationshipStatusSi…   -0.359     0.729    -0.493 6.23e- 1   -1.80      1.08 
4 Fertilitylow:Relatio…    1.56      1.06      1.47  1.43e- 1   -0.531     3.64 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              6.31      0.464     13.6  1.95e-27    5.39      7.22 
2 Fertilitylow            -1.14      0.663     -1.72 8.67e- 2   -2.45      0.167
3 RelationshipStatusSi…   -0.832     0.788     -1.06 2.93e- 1   -2.39      0.725
4 Fertilitylow:Relatio…    2.00      1.14       1.75 8.17e- 2   -0.255     4.26 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)               6.77     0.544     12.4  2.16e-29    5.70      7.84 
2 Fertilitylow             -1.02     0.613     -1.66 9.81e- 2   -2.22      0.189
3 RelationshipStatusSi…    -1.73     0.719     -2.41 1.67e- 2   -3.15     -0.315
4 Fertilitylow:Relatio…     1.38     0.819      1.69 9.25e- 2   -0.229     2.99 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              6.01      0.424     14.2  6.13e-36    5.18      6.85 
2 Fertilitylow            -0.567     0.481     -1.18 2.39e- 1   -1.51      0.379
3 RelationshipStatusSi…   -0.815     0.793     -1.03 3.05e- 1   -2.38      0.745
4 Fertilitylow:Relatio…    1.31      0.914      1.44 1.52e- 1   -0.485     3.11 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)               6.77     0.539     12.6  4.62e-28    5.71      7.83 
2 Fertilitylow             -1.02     0.608     -1.67 9.56e- 2   -2.21      0.180
3 RelationshipStatusSi…    -1.57     0.852     -1.84 6.65e- 2   -3.25      0.108
4 Fertilitylow:Relatio…     1.76     0.977      1.80 7.26e- 2   -0.163     3.69 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              6.35      0.426     14.9  1.17e-38    5.51     7.18  
2 Fertilitylow            -0.580     0.528     -1.10 2.73e- 1   -1.62     0.459 
3 RelationshipStatusSi…   -1.17      0.585     -2.00 4.63e- 2   -2.32    -0.0195
4 Fertilitylow:Relatio…    0.790     0.724      1.09 2.76e- 1   -0.634    2.21  
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              5.82      0.346    16.8   2.86e-46    5.14      6.50 
2 Fertilitylow            -0.374     0.425    -0.880 3.80e- 1   -1.21      0.462
3 RelationshipStatusSi…   -0.340     0.655    -0.519 6.04e- 1   -1.63      0.949
4 Fertilitylow:Relatio…    0.808     0.821     0.984 3.26e- 1   -0.807     2.42 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              6.35      0.423     15.0  3.45e-36    5.51      7.18 
2 Fertilitylow            -0.580     0.524     -1.11 2.70e- 1   -1.61      0.453
3 RelationshipStatusSi…   -0.867     0.694     -1.25 2.13e- 1   -2.23      0.500
4 Fertilitylow:Relatio…    1.01      0.870      1.17 2.45e- 1   -0.699     2.73 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)               6.26     0.547     11.4  4.88e-23    5.18     7.33  
2 Fertilitylow             -1.32     0.703     -1.88 6.23e- 2   -2.71     0.0685
3 RelationshipStatusSi…    -1.57     0.725     -2.17 3.13e- 2   -3.00    -0.143 
4 Fertilitylow:Relatio…     2.26     0.961      2.36 1.96e- 2    0.367    4.16  
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              5.54      0.428    12.9   2.07e-27    4.70      6.39 
2 Fertilitylow            -0.641     0.567    -1.13  2.60e- 1   -1.76      0.478
3 RelationshipStatusSi…   -0.628     0.793    -0.792 4.29e- 1   -2.19      0.937
4 Fertilitylow:Relatio…    1.99      1.06      1.87  6.35e- 2   -0.113     4.09 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)               6.26     0.549     11.4  1.98e-21    5.17     7.34  
2 Fertilitylow             -1.32     0.706     -1.87 6.37e- 2   -2.71     0.0761
3 RelationshipStatusSi…    -1.34     0.864     -1.55 1.23e- 1   -3.05     0.367 
4 Fertilitylow:Relatio…     2.67     1.14       2.33 2.12e- 2    0.405    4.93  
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)               6.39     0.489     13.1  1.07e-28    5.42      7.35 
2 Fertilitylow             -1.44     0.642     -2.23 2.66e- 2   -2.70     -0.169
3 RelationshipStatusSi…    -1.55     0.664     -2.33 2.06e- 2   -2.86     -0.240
4 Fertilitylow:Relatio…     2.06     0.889      2.32 2.13e- 2    0.310     3.82 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              5.70      0.395    14.4   5.93e-33   4.92       6.48 
2 Fertilitylow            -0.843     0.525    -1.61  1.10e- 1  -1.88       0.191
3 RelationshipStatusSi…   -0.512     0.729    -0.703 4.83e- 1  -1.95       0.925
4 Fertilitylow:Relatio…    1.87      0.994     1.88  6.16e- 2  -0.0918     3.83 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)               6.39     0.489     13.1  1.17e-26    5.42      7.36 
2 Fertilitylow             -1.44     0.643     -2.23 2.70e- 2   -2.70     -0.166
3 RelationshipStatusSi…    -1.20     0.782     -1.54 1.26e- 1   -2.75      0.342
4 Fertilitylow:Relatio…     2.46     1.06       2.32 2.14e- 2    0.369     4.55 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)               6.41     0.505     12.7  1.71e-26    5.41      7.41 
2 Fertilitylow             -1.50     0.684     -2.20 2.93e- 2   -2.85     -0.153
3 RelationshipStatusSi…    -1.06     0.674     -1.57 1.19e- 1   -2.39      0.274
4 Fertilitylow:Relatio…     1.56     0.954      1.64 1.03e- 1   -0.318     3.45 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              5.85      0.399    14.7   3.59e-32    5.06      6.64 
2 Fertilitylow            -0.982     0.562    -1.75  8.24e- 2   -2.09      0.127
3 RelationshipStatusSi…   -0.121     0.738    -0.164 8.70e- 1   -1.58      1.34 
4 Fertilitylow:Relatio…    1.13      1.06      1.06  2.89e- 1   -0.963     3.21 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              6.41      0.507    12.7   1.59e-24    5.41      7.41 
2 Fertilitylow            -1.50      0.685    -2.19  3.01e- 2   -2.86     -0.147
3 RelationshipStatusSi…   -0.679     0.801    -0.848 3.98e- 1   -2.26      0.905
4 Fertilitylow:Relatio…    1.65      1.13      1.46  1.47e- 1   -0.584     3.88 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              6.18      0.571    10.8   2.41e-23    5.05     7.30  
2 Fertilitylow            -0.344     0.639    -0.539 5.90e- 1   -1.60     0.913 
3 RelationshipStatusSi…   -1.53      0.755    -2.02  4.40e- 2   -3.01    -0.0416
4 Fertilitylow:Relatio…    1.14      0.856     1.34  1.82e- 1   -0.540    2.83  
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)             5.55       0.448    12.4   6.83e-29    4.67      6.43 
2 Fertilitylow           -0.0868     0.504    -0.172 8.63e- 1   -1.08      0.905
3 RelationshipStatusSi…  -0.820      0.818    -1.00  3.16e- 1   -2.43      0.788
4 Fertilitylow:Relatio…   1.54       0.944     1.63  1.04e- 1   -0.318     3.40 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              6.18      0.568    10.9   2.08e-22    5.06      7.30 
2 Fertilitylow            -0.344     0.636    -0.542 5.88e- 1   -1.60      0.908
3 RelationshipStatusSi…   -1.45      0.884    -1.64  1.03e- 1   -3.19      0.296
4 Fertilitylow:Relatio…    1.80      1.02      1.77  7.81e- 2   -0.203     3.80 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              6.27      0.457    13.7   9.64e-34    5.37      7.17 
2 Fertilitylow            -0.537     0.553    -0.970 3.33e- 1   -1.63      0.552
3 RelationshipStatusSi…   -1.07      0.615    -1.75  8.17e- 2   -2.28      0.136
4 Fertilitylow:Relatio…    0.627     0.754     0.831 4.07e- 1   -0.857     2.11 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)             5.68       0.361   15.7    2.48e-41     4.97     6.40 
2 Fertilitylow           -0.300      0.440   -0.680  4.97e- 1    -1.17     0.567
3 RelationshipStatusSi…  -0.0288     0.687   -0.0419 9.67e- 1    -1.38     1.32 
4 Fertilitylow:Relatio…   0.524      0.857    0.612  5.41e- 1    -1.16     2.21 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              6.27      0.456    13.7   1.49e-31     5.37     7.17 
2 Fertilitylow            -0.537     0.552    -0.972 3.32e- 1    -1.62     0.551
3 RelationshipStatusSi…   -0.614     0.738    -0.832 4.06e- 1    -2.07     0.841
4 Fertilitylow:Relatio…    0.761     0.915     0.832 4.06e- 1    -1.04     2.56 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              6.41      0.501    12.8   1.56e-27    5.42      7.40 
2 Fertilitylow            -0.565     0.655    -0.863 3.89e- 1   -1.86      0.727
3 RelationshipStatusSi…   -1.64      0.645    -2.54  1.19e- 2   -2.91     -0.366
4 Fertilitylow:Relatio…    1.18      0.869     1.35  1.78e- 1   -0.539     2.89 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              5.84      0.389    15.0   2.21e-34    5.08     6.61  
2 Fertilitylow            -0.247     0.517    -0.478 6.33e- 1   -1.27     0.773 
3 RelationshipStatusSi…   -1.29      0.677    -1.90  5.88e- 2   -2.62     0.0486
4 Fertilitylow:Relatio…    1.31      0.951     1.38  1.70e- 1   -0.565    3.18  
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              6.41      0.496    12.9   1.19e-25    5.43      7.39 
2 Fertilitylow            -0.565     0.648    -0.871 3.85e- 1   -1.85      0.717
3 RelationshipStatusSi…   -1.85      0.736    -2.52  1.30e- 2   -3.31     -0.396
4 Fertilitylow:Relatio…    1.63      1.02      1.60  1.12e- 1   -0.382     3.64 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              6.50      0.465     14.0  2.24e-32   5.58       7.41 
2 Fertilitylow            -0.675     0.608     -1.11 2.68e- 1  -1.87       0.523
3 RelationshipStatusSi…   -1.83      0.598     -3.06 2.47e- 3  -3.01      -0.652
4 Fertilitylow:Relatio…    1.50      0.800      1.88 6.18e- 2  -0.0746     3.08 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)             5.75       0.358    16.0   3.42e-39    5.05      6.46 
2 Fertilitylow           -0.0852     0.477    -0.179 8.58e- 1   -1.03      0.855
3 RelationshipStatusSi…  -1.15       0.636    -1.80  7.32e- 2   -2.40      0.108
4 Fertilitylow:Relatio…   1.07       0.879     1.21  2.26e- 1   -0.665     2.80 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              6.50      0.457     14.2  2.56e-30    5.59      7.40 
2 Fertilitylow            -0.675     0.597     -1.13 2.60e- 1   -1.85      0.504
3 RelationshipStatusSi…   -1.89      0.684     -2.76 6.37e- 3   -3.24     -0.540
4 Fertilitylow:Relatio…    1.66      0.931      1.78 7.71e- 2   -0.182     3.49 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              6.28      0.412    15.2   3.66e-35     5.46    7.09  
2 Fertilitylow            -0.263     0.605    -0.434 6.64e- 1    -1.46    0.930 
3 RelationshipStatusSi…   -1.12      0.583    -1.92  5.62e- 2    -2.27    0.0301
4 Fertilitylow:Relatio…    0.371     0.846     0.439 6.61e- 1    -1.30    2.04  
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              5.88      0.339    17.3   1.42e-41     5.21     6.55 
2 Fertilitylow            -0.208     0.495    -0.421 6.74e- 1    -1.18     0.767
3 RelationshipStatusSi…   -0.661     0.685    -0.966 3.35e- 1    -2.01     0.689
4 Fertilitylow:Relatio…    0.483     0.983     0.491 6.24e- 1    -1.46     2.42 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              6.28      0.413    15.2   7.09e-32     5.46     7.09 
2 Fertilitylow            -0.263     0.606    -0.434 6.65e- 1    -1.46     0.935
3 RelationshipStatusSi…   -1.06      0.720    -1.47  1.44e- 1    -2.48     0.364
4 Fertilitylow:Relatio…    0.537     1.04      0.518 6.05e- 1    -1.51     2.59 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              6.61      0.529     12.5  1.61e-29    5.56      7.65 
2 Fertilitylow            -0.687     0.594     -1.16 2.48e- 1   -1.86      0.482
3 RelationshipStatusSi…   -1.68      0.688     -2.44 1.54e- 2   -3.03     -0.323
4 Fertilitylow:Relatio…    1.24      0.785      1.58 1.16e- 1   -0.306     2.78 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              6.04      0.409    14.8   4.71e-38    5.23     6.84  
2 Fertilitylow            -0.339     0.464    -0.732 4.65e- 1   -1.25     0.573 
3 RelationshipStatusSi…   -1.37      0.738    -1.86  6.41e- 2   -2.82     0.0810
4 Fertilitylow:Relatio…    1.34      0.861     1.56  1.21e- 1   -0.354    3.03  
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              6.60      0.518     12.8  1.22e-28    5.58      7.63 
2 Fertilitylow            -0.687     0.582     -1.18 2.39e- 1   -1.83      0.460
3 RelationshipStatusSi…   -1.94      0.791     -2.45 1.50e- 2   -3.50     -0.380
4 Fertilitylow:Relatio…    1.69      0.915      1.84 6.65e- 2   -0.116     3.49 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              6.24      0.404    15.4   9.95e-41    5.45     7.04  
2 Fertilitylow            -0.284     0.505    -0.562 5.75e- 1   -1.28     0.710 
3 RelationshipStatusSi…   -1.12      0.558    -2.01  4.49e- 2   -2.22    -0.0258
4 Fertilitylow:Relatio…    0.612     0.693     0.883 3.78e- 1   -0.752    1.98  
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              5.86      0.327    17.9   2.50e-50    5.21      6.50 
2 Fertilitylow            -0.126     0.405    -0.310 7.57e- 1   -0.923     0.672
3 RelationshipStatusSi…   -0.758     0.633    -1.20  2.32e- 1   -2.00      0.487
4 Fertilitylow:Relatio…    0.578     0.791     0.730 4.66e- 1   -0.979     2.13 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              6.24      0.397    15.7   1.72e-38    5.46      7.03 
2 Fertilitylow            -0.284     0.496    -0.572 5.68e- 1   -1.26      0.694
3 RelationshipStatusSi…   -1.15      0.661    -1.73  8.45e- 2   -2.45      0.157
4 Fertilitylow:Relatio…    0.736     0.829     0.888 3.76e- 1   -0.897     2.37 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)               6.47     0.461     14.1  8.75e-32    5.57      7.38 
2 Fertilitylow             -1.02     0.623     -1.65 1.01e- 1   -2.25      0.203
3 RelationshipStatusSi…    -1.76     0.606     -2.90 4.16e- 3   -2.95     -0.561
4 Fertilitylow:Relatio…     1.98     0.845      2.34 2.02e- 2    0.313     3.65 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)             5.63       0.358   15.7    5.47e-37     4.92     6.33 
2 Fertilitylow           -0.0228     0.506   -0.0452 9.64e- 1    -1.02     0.974
3 RelationshipStatusSi…  -0.614      0.683   -0.899  3.69e- 1    -1.96     0.732
4 Fertilitylow:Relatio…   0.473      0.948    0.498  6.19e- 1    -1.40     2.34 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)               6.47     0.463     14.0  5.93e-29    5.56    7.39   
2 Fertilitylow             -1.02     0.626     -1.64 1.04e- 1   -2.26    0.213  
3 RelationshipStatusSi…    -1.46     0.738     -1.98 4.95e- 2   -2.92   -0.00347
4 Fertilitylow:Relatio…     1.47     1.01       1.46 1.46e- 1   -0.520   3.47   
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)               6.57     0.410     16.0  1.06e-39    5.77     7.38  
2 Fertilitylow             -1.13     0.567     -1.99 4.74e- 2   -2.25    -0.0133
3 RelationshipStatusSi…    -1.79     0.550     -3.25 1.31e- 3   -2.87    -0.705 
4 Fertilitylow:Relatio…     1.89     0.772      2.45 1.51e- 2    0.368    3.41  
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              5.83      0.323    18.0   2.26e-46    5.20      6.47 
2 Fertilitylow            -0.282     0.459    -0.615 5.39e- 1   -1.19      0.621
3 RelationshipStatusSi…   -0.973     0.634    -1.54  1.26e- 1   -2.22      0.275
4 Fertilitylow:Relatio…    0.773     0.878     0.881 3.79e- 1   -0.955     2.50 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)               6.57     0.409     16.1  4.04e-36    5.76     7.38  
2 Fertilitylow             -1.13     0.566     -2.00 4.75e- 2   -2.25    -0.0126
3 RelationshipStatusSi…    -1.71     0.674     -2.54 1.19e- 2   -3.04    -0.382 
4 Fertilitylow:Relatio…     1.62     0.928      1.75 8.23e- 2   -0.210    3.45  
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)               6.54     0.429     15.2  3.96e-35    5.69     7.38  
2 Fertilitylow             -1.14     0.617     -1.85 6.63e- 2   -2.36     0.0773
3 RelationshipStatusSi…    -1.35     0.581     -2.32 2.15e- 2   -2.49    -0.201 
4 Fertilitylow:Relatio…     1.36     0.852      1.59 1.12e- 1   -0.322    3.04  
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              5.94      0.342    17.4   1.34e-41     5.27     6.62 
2 Fertilitylow            -0.464     0.509    -0.912 3.63e- 1    -1.47     0.540
3 RelationshipStatusSi…   -0.516     0.660    -0.782 4.35e- 1    -1.82     0.786
4 Fertilitylow:Relatio…    0.277     0.953     0.291 7.71e- 1    -1.60     2.16 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              6.54      0.431    15.2   6.32e-32     5.69    7.39  
2 Fertilitylow            -1.14      0.620    -1.84  6.80e- 2    -2.36    0.0854
3 RelationshipStatusSi…   -1.11      0.707    -1.57  1.18e- 1    -2.51    0.285 
4 Fertilitylow:Relatio…    0.952     1.01      0.941 3.48e- 1    -1.05    2.95  
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              6.57      0.477     13.8  2.49e-34    5.63      7.51 
2 Fertilitylow            -0.687     0.553     -1.24 2.15e- 1   -1.77      0.400
3 RelationshipStatusSi…   -1.91      0.639     -2.98 3.09e- 3   -3.16     -0.648
4 Fertilitylow:Relatio…    1.61      0.746      2.16 3.13e- 2    0.145     3.08 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)             5.73       0.377    15.2   7.59e-40    4.99      6.48 
2 Fertilitylow            0.0525     0.439     0.120 9.05e- 1   -0.811     0.916
3 RelationshipStatusSi…  -0.818      0.722    -1.13  2.58e- 1   -2.24      0.602
4 Fertilitylow:Relatio…   0.598      0.849     0.705 4.81e- 1   -1.07      2.27 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              6.57      0.470     14.0  1.04e-32    5.65      7.50 
2 Fertilitylow            -0.687     0.544     -1.26 2.08e- 1   -1.76      0.386
3 RelationshipStatusSi…   -1.66      0.761     -2.17 3.07e- 2   -3.16     -0.156
4 Fertilitylow:Relatio…    1.34      0.893      1.50 1.35e- 1   -0.421     3.10 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              6.56      0.403     16.3  5.14e-44    5.77      7.36 
2 Fertilitylow            -0.785     0.504     -1.56 1.20e- 1   -1.78      0.206
3 RelationshipStatusSi…   -1.41      0.552     -2.55 1.13e- 2   -2.49     -0.320
4 Fertilitylow:Relatio…    1.06      0.689      1.54 1.25e- 1   -0.295     2.42 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)             5.93       0.323    18.3   4.64e-52     5.29     6.57 
2 Fertilitylow           -0.245      0.403    -0.609 5.43e- 1    -1.04     0.548
3 RelationshipStatusSi…  -0.448      0.631    -0.709 4.79e- 1    -1.69     0.794
4 Fertilitylow:Relatio…   0.0886     0.790     0.112 9.11e- 1    -1.47     1.64 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              6.56      0.396    16.6   2.17e-41    5.78      7.34 
2 Fertilitylow            -0.785     0.494    -1.59  1.14e- 1   -1.76      0.189
3 RelationshipStatusSi…   -1.08      0.659    -1.64  1.02e- 1   -2.38      0.218
4 Fertilitylow:Relatio…    0.628     0.826     0.761 4.47e- 1   -0.998     2.25 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              6.25      0.531     11.8  4.04e-24    5.21      7.30 
2 Fertilitylow            -0.728     0.679     -1.07 2.85e- 1   -2.07      0.611
3 RelationshipStatusSi…   -1.51      0.696     -2.18 3.09e- 2   -2.89     -0.141
4 Fertilitylow:Relatio…    1.35      0.915      1.48 1.41e- 1   -0.454     3.16 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              5.55      0.401    13.8   3.66e-30     4.76     6.35 
2 Fertilitylow            -0.198     0.539    -0.368 7.14e- 1    -1.26     0.866
3 RelationshipStatusSi…   -0.720     0.797    -0.903 3.68e- 1    -2.29     0.853
4 Fertilitylow:Relatio…    1.01      1.03      0.979 3.29e- 1    -1.02     3.04 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              6.25      0.523     12.0  8.20e-23    5.22      7.29 
2 Fertilitylow            -0.728     0.669     -1.09 2.79e- 1   -2.05      0.596
3 RelationshipStatusSi…   -1.42      0.852     -1.67 9.81e- 2   -3.10      0.266
4 Fertilitylow:Relatio…    1.54      1.09       1.42 1.59e- 1   -0.612     3.69 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              6.29      0.463     13.6  1.60e-30   5.38       7.21 
2 Fertilitylow            -0.800     0.608     -1.32 1.89e- 1  -2.00       0.398
3 RelationshipStatusSi…   -1.54      0.631     -2.44 1.55e- 2  -2.78      -0.295
4 Fertilitylow:Relatio…    1.55      0.835      1.86 6.41e- 2  -0.0922     3.20 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              5.67      0.368    15.4   3.01e-36    4.95      6.40 
2 Fertilitylow            -0.225     0.493    -0.457 6.48e- 1   -1.20      0.747
3 RelationshipStatusSi…   -0.817     0.731    -1.12  2.65e- 1   -2.26      0.625
4 Fertilitylow:Relatio…    1.01      0.952     1.06  2.91e- 1   -0.869     2.88 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              6.29      0.458     13.8  1.38e-28    5.39     7.20  
2 Fertilitylow            -0.800     0.601     -1.33 1.85e- 1   -1.99     0.387 
3 RelationshipStatusSi…   -1.44      0.769     -1.87 6.34e- 2   -2.96     0.0811
4 Fertilitylow:Relatio…    1.58      0.998      1.59 1.15e- 1   -0.388    3.55  
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              6.35      0.464    13.7   1.12e-29    5.43     7.27  
2 Fertilitylow            -0.792     0.642    -1.23  2.19e- 1   -2.06     0.475 
3 RelationshipStatusSi…   -1.17      0.627    -1.86  6.42e- 2   -2.40     0.0697
4 Fertilitylow:Relatio…    0.809     0.889     0.910 3.64e- 1   -0.946    2.56  
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              5.79      0.358    16.2   6.64e-37     5.08     6.49 
2 Fertilitylow            -0.438     0.526    -0.832 4.06e- 1    -1.48     0.600
3 RelationshipStatusSi…   -0.343     0.753    -0.455 6.50e- 1    -1.83     1.14 
4 Fertilitylow:Relatio…    0.441     1.02      0.434 6.65e- 1    -1.57     2.45 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              6.35      0.457    13.9   1.01e-27     5.45     7.25 
2 Fertilitylow            -0.792     0.632    -1.25  2.13e- 1    -2.04     0.459
3 RelationshipStatusSi…   -0.905     0.792    -1.14  2.55e- 1    -2.47     0.661
4 Fertilitylow:Relatio…    0.795     1.06      0.751 4.54e- 1    -1.30     2.89 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              6.27      0.541    11.6   6.44e-26    5.20      7.33 
2 Fertilitylow            -0.270     0.608    -0.444 6.57e- 1   -1.47      0.926
3 RelationshipStatusSi…   -1.74      0.709    -2.46  1.45e- 2   -3.14     -0.348
4 Fertilitylow:Relatio…    1.45      0.807     1.79  7.39e- 2   -0.141     3.04 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              5.49      0.403    13.6   3.01e-33    4.69      6.28 
2 Fertilitylow             0.396     0.463     0.856 3.93e- 1   -0.514     1.31 
3 RelationshipStatusSi…   -0.994     0.830    -1.20  2.32e- 1   -2.63      0.639
4 Fertilitylow:Relatio…    0.860     0.940     0.915 3.61e- 1   -0.990     2.71 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              6.27      0.535    11.7   6.13e-25    5.21     7.32  
2 Fertilitylow            -0.270     0.601    -0.449 6.54e- 1   -1.45     0.914 
3 RelationshipStatusSi…   -1.78      0.890    -2.00  4.72e- 2   -3.53    -0.0225
4 Fertilitylow:Relatio…    1.53      1.00      1.52  1.29e- 1   -0.450    3.50  
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              6.26      0.435    14.4   4.26e-36    5.40     7.11  
2 Fertilitylow            -0.300     0.529    -0.566 5.71e- 1   -1.34     0.742 
3 RelationshipStatusSi…   -1.16      0.586    -1.99  4.78e- 2   -2.32    -0.0114
4 Fertilitylow:Relatio…    0.776     0.720     1.08  2.82e- 1   -0.641    2.19  
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)             5.68       0.334   17.0    4.55e-46    5.02      6.34 
2 Fertilitylow            0.165      0.415    0.398  6.91e- 1   -0.652     0.982
3 RelationshipStatusSi…  -0.290      0.698   -0.416  6.78e- 1   -1.66      1.08 
4 Fertilitylow:Relatio…  -0.0394     0.844   -0.0467 9.63e- 1   -1.70      1.62 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              6.26      0.430    14.6   4.55e-34     5.41     7.10 
2 Fertilitylow            -0.300     0.523    -0.574 5.67e- 1    -1.33     0.730
3 RelationshipStatusSi…   -0.867     0.739    -1.17  2.42e- 1    -2.32     0.590
4 Fertilitylow:Relatio…    0.426     0.890     0.478 6.33e- 1    -1.33     2.18 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)               6.84     0.553     12.4  4.47e-25    5.75      7.93 
2 Fertilitylow             -1.10     0.722     -1.52 1.31e- 1   -2.52      0.329
3 RelationshipStatusSi…    -2.13     0.713     -2.99 3.24e- 3   -3.54     -0.722
4 Fertilitylow:Relatio…     1.62     0.974      1.67 9.73e- 2   -0.299     3.55 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              6.15      0.432     14.2  3.22e-30    5.29      7.00 
2 Fertilitylow            -0.664     0.578     -1.15 2.52e- 1   -1.80      0.477
3 RelationshipStatusSi…   -1.78      0.748     -2.37 1.88e- 2   -3.25     -0.298
4 Fertilitylow:Relatio…    1.82      1.09       1.67 9.68e- 2   -0.332     3.98 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)               6.84     0.550     12.4  3.77e-23   5.75       7.93 
2 Fertilitylow             -1.10     0.718     -1.53 1.29e- 1  -2.52       0.325
3 RelationshipStatusSi…    -2.47     0.814     -3.03 3.01e- 3  -4.08      -0.855
4 Fertilitylow:Relatio…     2.26     1.16       1.95 5.39e- 2  -0.0384     4.55 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)               6.88     0.507     13.6  9.63e-30    5.88      7.88 
2 Fertilitylow             -1.15     0.663     -1.73 8.47e- 2   -2.46      0.159
3 RelationshipStatusSi…    -2.28     0.653     -3.49 5.94e- 4   -3.57     -0.992
4 Fertilitylow:Relatio…     2.00     0.886      2.26 2.50e- 2    0.254     3.75 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              6.00      0.394    15.2   1.02e-34    5.22      6.77 
2 Fertilitylow            -0.409     0.526    -0.777 4.38e- 1   -1.45      0.629
3 RelationshipStatusSi…   -1.55      0.698    -2.22  2.79e- 2   -2.92     -0.170
4 Fertilitylow:Relatio…    1.57      1.00      1.56  1.19e- 1   -0.411     3.55 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)               6.88     0.500     13.8  1.54e-27    5.89      7.87 
2 Fertilitylow             -1.15     0.653     -1.76 8.09e- 2   -2.44      0.143
3 RelationshipStatusSi…    -2.43     0.749     -3.25 1.46e- 3   -3.91     -0.951
4 Fertilitylow:Relatio…     2.31     1.05       2.19 3.01e- 2    0.226     4.40 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              6.61      0.460    14.4   1.19e-30     5.70     7.52 
2 Fertilitylow            -0.676     0.675    -1.00  3.18e- 1    -2.01     0.657
3 RelationshipStatusSi…   -1.49      0.650    -2.29  2.34e- 2    -2.77    -0.205
4 Fertilitylow:Relatio…    0.613     0.965     0.635 5.27e- 1    -1.29     2.52 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              6.11      0.383    16.0   4.84e-35     5.36     6.87 
2 Fertilitylow            -0.552     0.562    -0.983 3.27e- 1    -1.66     0.557
3 RelationshipStatusSi…   -0.984     0.757    -1.30  1.95e- 1    -2.48     0.511
4 Fertilitylow:Relatio…    0.754     1.15      0.657 5.12e- 1    -1.51     3.02 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              6.61      0.464    14.2   1.49e-27     5.69     7.53 
2 Fertilitylow            -0.676     0.681    -0.993 3.23e- 1    -2.02     0.672
3 RelationshipStatusSi…   -1.48      0.798    -1.85  6.66e- 2    -3.06     0.103
4 Fertilitylow:Relatio…    0.879     1.21      0.728 4.68e- 1    -1.51     3.27 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              6.92      0.574     12.0  3.57e-27    5.79      8.05 
2 Fertilitylow            -0.870     0.646     -1.35 1.79e- 1   -2.14      0.401
3 RelationshipStatusSi…   -2.02      0.753     -2.68 7.75e- 3   -3.50     -0.537
4 Fertilitylow:Relatio…    1.39      0.859      1.61 1.07e- 1   -0.304     3.08 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              6.31      0.452     14.0  5.24e-34   5.42       7.20 
2 Fertilitylow            -0.579     0.510     -1.14 2.57e- 1  -1.58       0.425
3 RelationshipStatusSi…   -1.82      0.807     -2.26 2.49e- 2  -3.41      -0.232
4 Fertilitylow:Relatio…    1.79      0.946      1.89 5.99e- 2  -0.0754     3.65 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              6.92      0.562     12.3  2.55e-26    5.81      8.03 
2 Fertilitylow            -0.870     0.632     -1.38 1.70e- 1   -2.12      0.376
3 RelationshipStatusSi…   -2.43      0.858     -2.83 5.20e- 3   -4.12     -0.733
4 Fertilitylow:Relatio…    2.08      0.999      2.08 3.87e- 2    0.109     4.05 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              6.55      0.445    14.7   9.08e-37    5.68      7.43 
2 Fertilitylow            -0.500     0.552    -0.907 3.65e- 1   -1.59      0.586
3 RelationshipStatusSi…   -1.47      0.611    -2.41  1.66e- 2   -2.67     -0.270
4 Fertilitylow:Relatio…    0.807     0.759     1.06  2.89e- 1   -0.688     2.30 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              6.07      0.363    16.7   5.12e-44    5.36      6.78 
2 Fertilitylow            -0.320     0.446    -0.717 4.74e- 1   -1.20      0.558
3 RelationshipStatusSi…   -1.06      0.685    -1.54  1.24e- 1   -2.41      0.291
4 Fertilitylow:Relatio…    0.866     0.870     0.995 3.21e- 1   -0.847     2.58 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              6.55      0.437    15.0   1.24e-34    5.69      7.42 
2 Fertilitylow            -0.500     0.542    -0.923 3.57e- 1   -1.57      0.569
3 RelationshipStatusSi…   -1.54      0.715    -2.16  3.22e- 2   -2.95     -0.132
4 Fertilitylow:Relatio…    1.05      0.907     1.15  2.50e- 1   -0.743     2.83 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)               6.90     0.502     13.7  1.73e-29    5.91     7.89  
2 Fertilitylow             -1.39     0.674     -2.07 4.02e- 2   -2.73    -0.0627
3 RelationshipStatusSi…    -2.25     0.660     -3.41 8.17e- 4   -3.55    -0.945 
4 Fertilitylow:Relatio…     2.31     0.927      2.49 1.36e- 2    0.481    4.14  
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              5.86      0.393    14.9   6.74e-33     5.09     6.64 
2 Fertilitylow            -0.302     0.555    -0.545 5.86e- 1    -1.40     0.793
3 RelationshipStatusSi…   -0.975     0.752    -1.30  1.96e- 1    -2.46     0.508
4 Fertilitylow:Relatio…    0.885     1.06      0.833 4.06e- 1    -1.21     2.98 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)               6.90     0.507     13.6  1.37e-26    5.90     7.90  
2 Fertilitylow             -1.39     0.681     -2.05 4.27e- 2   -2.74    -0.0468
3 RelationshipStatusSi…    -2.01     0.808     -2.49 1.41e- 2   -3.61    -0.413 
4 Fertilitylow:Relatio…     1.98     1.12       1.76 8.01e- 2   -0.240    4.19  
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)               6.92     0.440     15.7  7.12e-37    6.05      7.79 
2 Fertilitylow             -1.36     0.613     -2.21 2.81e- 2   -2.56     -0.147
3 RelationshipStatusSi…    -2.19     0.592     -3.69 2.87e- 4   -3.35     -1.02 
4 Fertilitylow:Relatio…     2.06     0.845      2.44 1.57e- 2    0.392     3.73 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              6.06      0.350     17.3  1.05e-41    5.37     6.75  
2 Fertilitylow            -0.520     0.501     -1.04 3.01e- 1   -1.51     0.468 
3 RelationshipStatusSi…   -1.33      0.690     -1.92 5.58e- 2   -2.69     0.0331
4 Fertilitylow:Relatio…    1.20      0.986      1.22 2.24e- 1   -0.742    3.15  
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)               6.92     0.440     15.7  3.27e-33  6.05        7.79 
2 Fertilitylow             -1.36     0.613     -2.21 2.87e- 2 -2.57       -0.143
3 RelationshipStatusSi…    -2.19     0.729     -3.01 3.09e- 3 -3.63       -0.753
4 Fertilitylow:Relatio…     2.04     1.03       1.97 5.02e- 2 -0.00177     4.08 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)               6.73     0.476     14.1  3.26e-30    5.79     7.67  
2 Fertilitylow             -1.27     0.681     -1.87 6.36e- 2   -2.62     0.0730
3 RelationshipStatusSi…    -1.51     0.648     -2.33 2.08e- 2   -2.79    -0.233 
4 Fertilitylow:Relatio…     1.32     0.956      1.38 1.68e- 1   -0.564    3.21  
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              6.12      0.384    15.9   4.21e-35     5.36     6.88 
2 Fertilitylow            -0.681     0.567    -1.20  2.32e- 1    -1.80     0.439
3 RelationshipStatusSi…   -0.748     0.734    -1.02  3.09e- 1    -2.20     0.700
4 Fertilitylow:Relatio…    0.483     1.08      0.445 6.57e- 1    -1.66     2.63 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)               6.73     0.482    14.0   3.53e-27     5.78    7.69  
2 Fertilitylow             -1.27     0.691    -1.84  6.79e- 2    -2.64    0.0952
3 RelationshipStatusSi…    -1.36     0.790    -1.72  8.82e- 2    -2.92    0.206 
4 Fertilitylow:Relatio…     1.07     1.15      0.930 3.54e- 1    -1.21    3.36  
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              6.93      0.519     13.4  8.04e-32    5.91      7.95 
2 Fertilitylow            -0.942     0.600     -1.57 1.18e- 1   -2.12      0.240
3 RelationshipStatusSi…   -2.35      0.702     -3.36 9.01e- 4   -3.74     -0.973
4 Fertilitylow:Relatio…    1.91      0.818      2.34 2.02e- 2    0.301     3.52 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              5.97      0.415    14.4   1.70e-35    5.15      6.79 
2 Fertilitylow            -0.150     0.482    -0.312 7.55e- 1   -1.10      0.799
3 RelationshipStatusSi…   -1.22      0.803    -1.52  1.30e- 1   -2.80      0.362
4 Fertilitylow:Relatio…    0.975     0.944     1.03  3.03e- 1   -0.884     2.83 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              6.93      0.511     13.6  3.34e-30    5.92      7.94 
2 Fertilitylow            -0.942     0.591     -1.59 1.13e- 1   -2.11      0.224
3 RelationshipStatusSi…   -2.18      0.839     -2.60 1.01e- 2   -3.84     -0.526
4 Fertilitylow:Relatio…    1.77      0.983      1.80 7.40e- 2   -0.173     3.71 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              6.86      0.443     15.5  1.87e-39    5.99      7.73 
2 Fertilitylow            -0.969     0.550     -1.76 7.89e- 2   -2.05      0.113
3 RelationshipStatusSi…   -1.68      0.608     -2.76 6.07e- 3   -2.88     -0.485
4 Fertilitylow:Relatio…    1.13      0.756      1.49 1.36e- 1   -0.359     2.62 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              6.16      0.360    17.1   2.34e-45     5.45     6.87 
2 Fertilitylow            -0.460     0.445    -1.03  3.02e- 1    -1.34     0.416
3 RelationshipStatusSi…   -0.716     0.693    -1.03  3.03e- 1    -2.08     0.649
4 Fertilitylow:Relatio…    0.296     0.874     0.338 7.35e- 1    -1.42     2.02 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              6.86      0.435    15.8   5.29e-37    6.00    7.72   
2 Fertilitylow            -0.969     0.540    -1.80  7.39e- 2   -2.03    0.0947 
3 RelationshipStatusSi…   -1.41      0.720    -1.96  5.09e- 2   -2.83    0.00546
4 Fertilitylow:Relatio…    0.805     0.907     0.888 3.76e- 1   -0.983   2.59   
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              6.62      0.573     11.6  1.98e-22    5.49      7.75 
2 Fertilitylow            -0.947     0.741     -1.28 2.03e- 1   -2.41      0.518
3 RelationshipStatusSi…   -2.10      0.756     -2.78 6.23e- 3   -3.59     -0.604
4 Fertilitylow:Relatio…    1.81      1.01       1.79 7.52e- 2   -0.186     3.82 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              5.73      0.445    12.9   6.04e-26    4.85      6.61 
2 Fertilitylow            -0.300     0.602    -0.498 6.19e- 1   -1.49      0.890
3 RelationshipStatusSi…   -1.17      0.864    -1.35  1.78e- 1   -2.88      0.537
4 Fertilitylow:Relatio…    1.55      1.15      1.35  1.80e- 1   -0.725     3.82 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              6.62      0.566     11.7  5.01e-21    5.50      7.74 
2 Fertilitylow            -0.947     0.732     -1.29 1.99e- 1   -2.40      0.505
3 RelationshipStatusSi…   -2.06      0.915     -2.26 2.60e- 2   -3.88     -0.252
4 Fertilitylow:Relatio…    2.20      1.20       1.83 7.00e- 2   -0.182     4.57 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              6.58      0.495     13.3  4.20e-28   5.60       7.56 
2 Fertilitylow            -0.976     0.663     -1.47 1.43e- 1  -2.28       0.332
3 RelationshipStatusSi…   -2.04      0.687     -2.97 3.38e- 3  -3.40      -0.686
4 Fertilitylow:Relatio…    1.91      0.930      2.05 4.19e- 2   0.0710     3.74 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              5.84      0.405    14.4   2.49e-31    5.04      6.64 
2 Fertilitylow            -0.351     0.549    -0.640 5.23e- 1   -1.44      0.733
3 RelationshipStatusSi…   -1.25      0.804    -1.56  1.21e- 1   -2.84      0.334
4 Fertilitylow:Relatio…    1.45      1.08      1.34  1.83e- 1   -0.688     3.58 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              6.58      0.492     13.4  5.03e-26    5.61      7.55 
2 Fertilitylow            -0.976     0.658     -1.48 1.41e- 1   -2.28      0.327
3 RelationshipStatusSi…   -2.00      0.837     -2.38 1.86e- 2   -3.65     -0.339
4 Fertilitylow:Relatio…    2.07      1.12       1.85 6.73e- 2   -0.149     4.29 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              6.54      0.512    12.8   1.31e-25     5.53    7.55  
2 Fertilitylow            -0.801     0.714    -1.12  2.64e- 1    -2.21    0.611 
3 RelationshipStatusSi…   -1.47      0.701    -2.10  3.75e- 2    -2.86   -0.0861
4 Fertilitylow:Relatio…    0.893     1.00      0.892 3.74e- 1    -1.09    2.87  
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              5.91      0.406    14.6   2.56e-30     5.11     6.71 
2 Fertilitylow            -0.493     0.593    -0.833 4.06e- 1    -1.66     0.678
3 RelationshipStatusSi…   -0.669     0.839    -0.797 4.27e- 1    -2.33     0.989
4 Fertilitylow:Relatio…    0.798     1.15      0.691 4.91e- 1    -1.48     3.08 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              6.54      0.508    12.9   1.06e-23     5.53     7.54 
2 Fertilitylow            -0.801     0.708    -1.13  2.61e- 1    -2.20     0.603
3 RelationshipStatusSi…   -1.30      0.879    -1.47  1.43e- 1    -3.04     0.446
4 Fertilitylow:Relatio…    1.11      1.20      0.921 3.59e- 1    -1.27     3.48 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              6.47      0.577    11.2   4.65e-24   5.33       7.61 
2 Fertilitylow            -0.303     0.650    -0.466 6.42e- 1  -1.58       0.978
3 RelationshipStatusSi…   -2.23      0.768    -2.90  3.99e- 3  -3.74      -0.718
4 Fertilitylow:Relatio…    1.78      0.876     2.03  4.33e- 2   0.0540     3.50 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              5.56      0.443    12.6   1.46e-28    4.69      6.43 
2 Fertilitylow             0.406     0.507     0.801 4.24e- 1   -0.592     1.40 
3 RelationshipStatusSi…   -1.45      0.901    -1.61  1.09e- 1   -3.22      0.325
4 Fertilitylow:Relatio…    1.34      1.03      1.30  1.95e- 1   -0.689     3.36 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              6.47      0.571    11.3   5.15e-23   5.34       7.60 
2 Fertilitylow            -0.303     0.644    -0.470 6.39e- 1  -1.57       0.967
3 RelationshipStatusSi…   -2.36      0.956    -2.47  1.45e- 2  -4.24      -0.473
4 Fertilitylow:Relatio…    2.04      1.09      1.88  6.11e- 2  -0.0962     4.18 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              6.41      0.473    13.5   5.40e-32    5.48      7.34 
2 Fertilitylow            -0.259     0.575    -0.451 6.52e- 1   -1.39      0.872
3 RelationshipStatusSi…   -1.43      0.644    -2.23  2.68e- 2   -2.70     -0.166
4 Fertilitylow:Relatio…    0.822     0.790     1.04  2.99e- 1   -0.734     2.38 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              5.77      0.371    15.5   6.41e-39    5.03      6.50 
2 Fertilitylow             0.161     0.458     0.353 7.25e- 1   -0.740     1.06 
3 RelationshipStatusSi…   -0.558     0.764    -0.730 4.66e- 1   -2.06      0.947
4 Fertilitylow:Relatio…    0.200     0.932     0.215 8.30e- 1   -1.63      2.04 
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
  broom::tidy(conf.int = TRUE)
summary_RelComp
# A tibble: 4 × 7
  term                  estimate std.error statistic  p.value conf.low conf.high
  <chr>                    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)              6.41      0.468    13.7   4.83e-30     5.48     7.33 
2 Fertilitylow            -0.259     0.569    -0.456 6.49e- 1    -1.38     0.863
3 RelationshipStatusSi…   -1.20      0.804    -1.49  1.37e- 1    -2.79     0.386
4 Fertilitylow:Relatio…    0.621     0.977     0.635 5.26e- 1    -1.31     2.55

Visualising the Results

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

p_val <- round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val <- paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
  group_by(Fertility, RelationshipStatus) %>%
  mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
    "Single"
  )) %>%
  summarise(
    .fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
  geom_bar(
    stat = "identity",
    position = position_dodge2(preserve = "single"), width = 0.5
  ) +
  geom_linerange(aes(
    ymin = .lower,
    ymax = .upper
  ), position = position_dodge(width = 0.5)) +
  geom_text(label = paste0(
    "Interaction:",
    p_val
  ), x = 1.5, y = 7.5, colour = "#666666") +
  labs(
    x = "RelationshipStatus",
    y = "ReligiosityCompositeScore"
  ) +
  ylim(c(0, 8.1)) +
  theme_minimal()

Exporting the JSON files

The current section describes how the remaining three JSON files can be created. If you haven’t already, the first step would be to execute all the analyses declared in the multiverse using execute_multiverse():

execute_multiverse(M)

We can then use dedicated functions for each of the JSON files: - export_results_json or export_results_dist_json. - export_code_json

Exporting the results

We provide two functions to export the results: export_results_json and export_results_dist_json. export_results_json requires the user to specify the following arguments:

  • term: column name which contains the names of the outcome variables. For example, in the case of a regression, you could use the output of broom::tidy() and thus the argument would be the column which contains the coefficient names.
  • estimate: column name containing the mean / median point estimates for each outcome.
  • standard error: column name containing the standard errors for each outcome.
  • dist: column name containing distributional objects for each outcome variable. Optional only if estimate and standard error arguments are provided. See below for more details.
  • (optional) filename: if specified, the function will create a file in the specified path; if not specified, it will return the dataframe as shown below:
expand(M) %>%
  extract_variables(summary_RelComp) %>%
  unnest( cols = c(summary_RelComp) ) %>%
  mutate( term = recode( term, 
                 "RelationshipStatusSingle" = "Single",
                 "Fertilitylow:RelationshipStatusSingle" = "Single:Fertility_low"
  )) %>%
  export_results_json(term, estimate, std.error) |> 
  unnest(results) |> 
  select(.universe, term, estimate, std.error, cdf.x, cdf.y)
## # A tibble: 960 × 6
##    .universe term                 estimate std.error cdf.x       cdf.y      
##        <int> <chr>                   <dbl>     <dbl> <list>      <list>     
##  1         1 (Intercept)             6.37      0.405 <dbl [101]> <dbl [101]>
##  2         1 Fertilitylow           -1.16      0.534 <dbl [101]> <dbl [101]>
##  3         1 Single                 -1.51      0.538 <dbl [101]> <dbl [101]>
##  4         1 Single:Fertility_low    2.05      0.714 <dbl [101]> <dbl [101]>
##  5         2 (Intercept)             5.78      0.322 <dbl [101]> <dbl [101]>
##  6         2 Fertilitylow           -0.583     0.428 <dbl [101]> <dbl [101]>
##  7         2 Single                 -0.859     0.583 <dbl [101]> <dbl [101]>
##  8         2 Single:Fertility_low    1.85      0.772 <dbl [101]> <dbl [101]>
##  9         3 (Intercept)             6.37      0.402 <dbl [101]> <dbl [101]>
## 10         3 Fertilitylow           -1.16      0.531 <dbl [101]> <dbl [101]>
## # ℹ 950 more rows

The resultant JSON file consists of a list of objects (where each object corresponds to one analysis in the multiverse). Within this object, the results attribute contains a(nother) list of objects corresponding to each outcome variable. For e.g., here we have four coefficients (see the results of the regression model), and thus the results attribute will contain four objects. Each object has the following attributes:

  • term: name of the outcome variable
  • estimate: mean / median point estimate i.e., \(\mathbb{E}(\mu)\) for any parameter \(\mu\).
  • std.error: standard error for the point estimate i.e., \(\sqrt{\text{var}(\mu)}\)
  • cdf.x: a list of quantiles
  • cdf.y: a list of cumulative probability density estimates corresponding to the quantiles

In addition, it also contains the following attributes, but these are not currently used by Milliways:

  • statistic
  • p.value
  • conf.low
  • conf.high

A Note on Distributional Assumptions

For simplicity, we assume that each of the outcome variables follow a normal distribution. However, this may not always be the case. In this case, we recommend that you should specify the dist argument to export_results_json or use the export_results_dist_json which allows you to specify distributional objects for each outcome. We demonstrate how a user can do this with the following example of a multiverse analysis where the results consists of two parameters: \(\mu \sim \text{N}(0, 1)\), a normally distributed random variable and \(\sigma \sim \text{exp}(1)\), a random variable which follows the exponential distribution.

expand_grid(
  .universe = seq(1:5),
  nesting(
    term = c("mu", "sigma"),
    dist = c(dist_normal(0, 1), dist_exponential(1))
  )
) |> 
  export_results_dist_json(term, dist) |> 
  unnest(results)
## # A tibble: 10 × 5
##    .universe term     dist cdf.x       cdf.y      
##        <int> <chr>  <dist> <list>      <list>     
##  1         1 mu    N(0, 1) <dbl [101]> <dbl [101]>
##  2         1 sigma  Exp(1) <dbl [101]> <dbl [101]>
##  3         2 mu    N(0, 1) <dbl [101]> <dbl [101]>
##  4         2 sigma  Exp(1) <dbl [101]> <dbl [101]>
##  5         3 mu    N(0, 1) <dbl [101]> <dbl [101]>
##  6         3 sigma  Exp(1) <dbl [101]> <dbl [101]>
##  7         4 mu    N(0, 1) <dbl [101]> <dbl [101]>
##  8         4 sigma  Exp(1) <dbl [101]> <dbl [101]>
##  9         5 mu    N(0, 1) <dbl [101]> <dbl [101]>
## 10         5 sigma  Exp(1) <dbl [101]> <dbl [101]>

Exporting the Code

Exporting the code file is relatively simple as the only arguments that need to be provided are the multiverse object and file path:

export_code_json(M, "code.json")

The JSON file consists of two attributes: code and parameters. code is a list of strings consisting of the R and multiverse syntax used to implement the analysis. For readability, we use styler to break up the declared code. parameters is an object listing the parameter names and the corresponding options for each of the parameters declared in the analysis.

Exporting the Dataset

This function is used to export the (unmodified) dataset that is used in the analysis, and is a simple wrapper around the write_json function.

 export_data_json(durante, "data.json")

The JSON file consists of a list of objects, each with two attributes: field and values. field is the name of a column corresponding to a variable in the dataset. values are a list of values for that variable in the dataset.