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Exporting the output of a multiverse to JSON for Milliways, a multiverse visualisation tool

Abhraneel Sarma

2025-10-12

Source: vignettes/export-milliways.Rmd
export-milliways.Rmd

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:

  • results: a JSON file which contains the estimates from each universe in the multiverse analysis.
  • code: a JSON file which contains the code used to generate the multiverse object.
  • data: a JSON file which contains the dataset used in the multiverse analysis.
  • analysis: an HTML file which contains the entire analysis as an Explorable Multiverse Analysis Report (EMAR).

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 knit_as_emar() function is declared, and
  • the document YAML (at the top of the markdown file) is set to output: html_document

The actual EMAR document which is generated from this file (provided the YAML is changed) can be found here.

Note #1: Creating an EMAR document is not a perfect process unfortunately, and relies on the HTML tags that knitr uses to convert a RMarkdown file to an HTML file. We created the knit_as_emar() function assuming users will compile using the default YAML (output: html_document) that is generated when a new RMarkdown file is created using RStudio. However, the knit_as_emar() function may not be compatible with other output formats, such as this one which uses the html_vignette format. I have also marked the multiverse code chunks using a comment to distinguish them from regular R code chunks. However, if you are knitting as an EMAR, the multiverse code chunks will appear with a different background colour to the regular code chunks making them easily distinguishable.

Note #2: 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 #3: 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 #4: 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.

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 #5: 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. We have removed them in this document as they do not get rendered properly when compiling to a vignette.

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. We use broom::tidy to extract the results of the linear model into a tidy data frame.

fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_RelComp
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
summary_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

Visualising the Results

We visualise the results by showing the predicted values of the model. The plot below shows the predicted Religiosity Score for each level of Fertility (high or low) and RelationshipStatus (Single or Relationship):

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()

Note #6: due to the issues with compiling in the RMarkdown vignette output format, we only present the results from the default universe.

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():

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., 𝔼(μ)\mathbb{E}(\mu) for any parameter μ\mu.
  • std.error: standard error for the point estimate i.e., var(μ)\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: μ∼N(0,1)\mu \sim \text{N}(0, 1), a normally distributed random variable and σ∼exp(1)\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 
##        <int> <chr>
##  1         1 mu   
##  2         1 sigma
##  3         2 mu   
##  4         2 sigma
##  5         3 mu   
##  6         3 sigma
##  7         4 mu   
##  8         4 sigma
##  9         5 mu   
## 10         5 sigma
## # ℹ 3 more variables: dist <dist>, cdf.x <list>, cdf.y <list>

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.