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Script_Analysis_Studies1_2_ATG_EV.Rmd
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---
title: "Moral Anger or Disgust First? Exploring the Primacy Effect in Victim and Perpetrator Foci"
subtitle: "Eva L. Vives, Astrid Thébault Guiochon, Bastien Trémolière, Agnès Falco, Hakim Djeriouat"
author: "<i>Script: Astrid Thébault Guiochon & Eva L. Vives</i>"
output:
html_document:
toc: yes
toc_float: yes
toc_depth: 4
theme: lumen
highlight: kate
tags: [knitr, servr]
---
```{r setup, include = FALSE}
options(knitr.kable.NA = '', knitr.table.format = "html")
knitr::opts_chunk$set(warning = FALSE, message = FALSE)
```
# I -- Loading
## 1. Librairies
```{CRAN, include = FALSE}
if (!require("aod"))
install.packages("aod")
if (!require("broom.mixed"))
install.packages("broom.mixed")
if (!require("car"))
install.packages("car")
if (!require("data.table"))
install.packages("data.table")
if (!require("dplyr"))
install.packages("dplyr")
if (!require("effects"))
install.packages("effects")
if (!require("emmeans"))
install.packages("emmeans")
if (!require("fitdistrplus"))
install.packages("fitdistrplus")
if (!require("ggeffects"))
install.packages("ggeffects")
if (!require("ggplot2"))
install.packages("ggplot2")
if (!require("ggpubr"))
install.packages("ggpubr")
if (!require("ggthemes"))
install.packages("ggthemes")
if (!require("gridExtra"))
install.packages("gridExtra")
if (!require("knitr"))
install.packages("knitr")
if (!require("labelled"))
install.packages("labelled")
if (!require("lme4"))
install.packages("lme4")
if (!require("lmerTest"))
install.packages("lmerTest")
if (!require("lmtest"))
install.packages("lmtest")
if (!require("magrittr"))
install.packages("magrittr")
if (!require("MuMIn"))
install.packages("MuMIn")
if (!require("nortest"))
install.packages("nortest")
if (!require("performance"))
install.packages("performance")
if (!require("plyr"))
install.packages("plyr")
if (!require("plotly"))
install.packages("plotly")
if (!require("readr"))
install.packages("readr")
if (!require("readxl"))
install.packages("readxl")
if (!require("sjPlot"))
install.packages("sjPlot")
if (!require("tidyr"))
install.packages("tidyr")
if (!require("tidyverse"))
install.packages("tidyverse")
if (!require("tinytex"))
tinytex::install_tinytex()
remotes::install_github("mattansb/ggggeffects")
```
```{r libraries}
library(aod)
library(broom.mixed)
library(car)
library(data.table)
library(dplyr)
library(effects)
library(emmeans)
library(fitdistrplus)
library(ggeffects)
library(ggplot2)
library(ggpubr)
library(ggthemes)
library(gridExtra)
library(gtsummary)
library(labelled)
library(lme4)
library(lmerTest)
library(lmtest)
library(magrittr)
library(MuMIn)
library(nortest)
library(performance)
library(plyr)
library(plotly)
library(readr)
library(readxl)
library(sjPlot)
library(tidyr)
source('./ggplot_theme_Publication-2.R')
```
## 2. Working directory
⚠️ Adapt to your computer
```{r directory, include = FALSE}
# Eva
# data = read.csv("~/Moralem/Study 1/Dataset_s1_final_221223.csv", sep = ";", header = T, dec = ".", na.strings = "NA")
# data2 = read.csv("~/Moralem/Study 2/Dataset_s2_final_181223.csv", sep = ";", header = T, dec = ".", na.strings = "NA")
```
## 3. Dataset
💡 Opens an explorer window (or use file path)
```{r data, include = FALSE}
# Dataset study 1
data = read.csv("./Dataset_s1.csv", sep = ";", header = T, dec = ".", na.strings = "NA")
#f = file.choose()
#data = read.csv(f, sep = ";", header = T, dec = ".", na.strings = "NA")
# Dataset study 2
data2 = read.csv("./Dataset_s2.csv", sep = ";", header = T, dec = ".", na.strings = "NA")
#f = file.choose()
#data2 = read.csv(f, sep = ";", header = T, dec = ".", na.strings = "NA")
#rm(f)
```
```{r data-prep-labels, include = FALSE, warnings = FALSE}
var_label(data) = list(condition = "Condition (Appraisal)",
newemo = "Emotion",
newclass = "Class of Transgression",
age = "Age",
newgender = "Sex",
political_orientation = "Political Orientation (1-Liberal to 7-Conservative")
data =
data |>
mutate(across(c(condition, newgender, newemo, newclass, political_orientation), as.factor)) |>
mutate(condition = recode(condition,
Agresseur = "Perpetrator",
Victime = "Victim"),
newgender = recode(newgender,
"0" = "Female",
"1" = "Male"),
newemo = recode(newemo,
colere = "Anger",
degout = "Disgust",
joie = "Joy",
neutre = "Neutral",
peur = "Fear",
tristesse = "Sadness"),
newclass = recode(newclass,
abandonment = "Abandonment",
betrayal = "Betrayal",
harm = "Harm",
humiliation = "Humiliation",
injustice = "Injustice")) |>
rename(item = newslide)
var_label(data2) = list(condition = "Condition (Appraisal)",
newemo = "Emotion",
newclass = "Class of Transgression",
scenarios = "Scenario",
age = "Age",
newgender = "Sex",
political_orientation = "Political Orientation (1-Liberal to 7-Conservative")
data2 =
data2 |>
mutate(across(c(condition, newgender, newemo, newclass, political_orientation, scenarios), as.factor)) |>
mutate(condition = recode(condition,
Agresseur = "Perpetrator",
Victime = "Victim"),
newgender = recode(newgender,
"0" = "Female",
"1" = "Male"),
newemo = recode(newemo,
colere = "Anger",
degout = "Disgust",
neutre = "Neutral",
tristesse = "Sadness"),
newclass = recode(newclass,
harm = "Harm",
humiliation = "Humiliation",
injustice = "Injustice",
norm = "Control"),
scenarios = recode(scenarios,
moral = "Transgression",
norm = "Control"))
#rename(item = newslide)
```
# II -- Preview
## 1. Demographics
### a) Study 1
```{r demographics1}
summary_data = data[,] %>%
select(prolific_ID, age, political_orientation, genre) %>%
group_by(prolific_ID) %>%
summarise(
age = first(age),
political_orientation = first(political_orientation),
genre = first(genre),
.groups = 'drop')
summary(summary_data)
count(summary_data$genre)
```
### b) Study 2
```{r demographics2}
summary_data2 = data2[,] %>%
select(prolific_ID, age, political_orientation, genre) %>%
group_by(prolific_ID) %>%
summarise(
age = first(age),
political_orientation = first(political_orientation),
genre = first(genre),
.groups = 'drop')
summary_data2
count(summary_data2$genre)
```
## 2. Distribution of reaction time
### a) Study 1
```{r plot-rt1}
fit_norm = fitdist(as.numeric(na.exclude(data$rt_net)), distr = "norm")
#shapiro.test(data$rt_net)
ad.test(data$rt_net) #Anderson Darling as shapiro test has a samplz size max
fit_log = fitdist(as.numeric(log((na.exclude(data$rt_net)))), distr = "norm")
plot(fit_norm)
plot(fit_log)
```
### b) Study 2
```{r plot-rt2}
fit_norm2 = fitdist(as.numeric(na.exclude(data2$rt_net)), distr = "norm")
#shapiro.test(data2$rt_net)
ad.test(data2$rt_net) #Anderson Darling as shapiro test has a samplz size max
fit_log2 = fitdist(as.numeric(log((na.exclude(data2$rt_net)))), distr = "norm")
plot(fit_norm2)
plot(fit_log2)
```
# III -- Models
## 1. Study 1
### a) Reaction Time
#### Full mixed-effect model
```{r s1-rt-lmer, results = 'hide'}
full = lmer(log(rt_net) ~ condition * newemo + (1|number_ID) + (1|item),
na.action = na.omit,
data = data)
```
Summary
```{r s1-rt-lmer-show}
tbl_regression(full,
label = list(condition ~ "Condition",
newemo ~ "Emotion")) %>%
modify_column_unhide(column = std.error) %>%
modify_column_unhide(column = statistic) %>%
modify_header(label = "**Variable**") %>%
bold_p() %>%
modify_fmt_fun(statistic ~ style_sigfig) %>%
add_significance_stars(hide_ci = FALSE,
hide_p = FALSE,
pattern = "{p.value}{stars}") %>%
add_glance_source_note(label = list(sigma ~ "\U03C3"),
include = c(AIC, sigma))
```
Anova & conditional and marginal coefficient of determination
```{r s1-rt-lmer-info}
Anova(full, type = 3, ddf = "Satterthwaite")
round(r.squaredGLMM(full, method = "marginal"), 4)
```
- Main effect of <i>Emotion</i> $$\chi^2(5) = 120.998, p < .001$$
- Interaction between <i>Condition</i> and <i>Emotion</i> $$\chi^2(5) = 23.82, p < .001$$
Variance explained by the model:
$$R^2_m = 0.98\% \text{ (fixed)}$$
$$R^2_c = 43.93\% \text{ (fixed + random)}$$
#### Pairwise comparisons & estimated marginal means
```{r s1-rt-lmer-cont}
emmeans(full, pairwise ~ newemo, lmer.df = "asymp", type = "response", adjust = "bonferroni")
emmeans(full, pairwise ~ condition, lmer.df = "asymp", type = "response", adjust = "bonferroni")
emmeans(full, pairwise ~ newemo|condition, lmer.df = "asymp", type = "response", adjust = "bonferroni")
```
#### Plot all interaction effects
```{r s1-rt-lmer-plot}
plotfull = ggpredict(full, terms = c("newemo", "condition"), ci_level = 0.95) %>%
plot() +
labs(x = "Emotion", y = "Reaction Time (ms)", colour = "Condition", title = "", element_text(size = 14)) +
scale_fill_Publication() +
scale_colour_Publication() +
theme_Publication() +
scale_y_continuous(breaks = c(800, 850, 900, 950, 1000, 1050, 1100, 1150))
plotfull
```
### b) Emotional Hits
#### Full generalized mixed-effect model
```{r s1-hit-lmer, results = 'hide'}
emo = glmer(score ~ newemo * condition + (1|number_ID) + (1|item),
data = data,
family = binomial,
na.action = na.omit,
control = glmerControl(optimizer = "bobyqa"))
```
Summary
```{r s1-hit-lmer-show}
tbl_regression(emo,
label = list(condition ~ "Condition",
newemo ~ "Emotion")) %>%
modify_column_unhide(column = std.error) %>%
modify_column_unhide(column = statistic) %>%
modify_header(label = "**Variable**") %>%
bold_p() %>%
modify_fmt_fun(statistic ~ style_sigfig) %>%
add_significance_stars(hide_ci = FALSE,
hide_p = FALSE,
pattern = "{p.value}{stars}") %>%
add_glance_source_note(label = list(sigma ~ "\U03C3"),
include = c(AIC, sigma))
```
Anova & conditional and marginal coefficient of determination
```{r s1-hit-lmer-info}
Anova(emo, type = 3, ddf = "Satterthwaite")
round(r.squaredGLMM(emo, method = "marginal"), 4)
```
- Main effect of <i>Emotion</i> $$\chi^2(5) = 264.81, p < .001$$
- Interaction between <i>Condition</i> and <i>Emotion</i> $$\chi^2(5) = 145.645, p < .001$$
Variance explained by the model:
$$R^2_m = 4.40\% \text{ (fixed)}$$
$$R^2_c = 35.74\% \text{ (fixed + random)}$$
#### Pairwise comparisons (estimated marginal means and pairwise comparisons)
```{r s1-hit-lmer-cont}
emmeans(emo, pairwise ~ newemo, lmer.df = "asymp", type = "response", adjust = "bonferroni")
emmeans(emo, pairwise ~ condition, lmer.df = "asymp", type = "response", adjust = "bonferroni")
emmeans(emo, pairwise ~ newemo|condition, lmer.df = "asymp", type = "response", adjust = "bonferroni")
```
#### Plot all interaction effects
```{r s1-hit-lmer-plot}
plotemo = ggpredict(emo, terms = c("newemo", "condition"), ci_level = 0.95) %>%
plot() +
labs(x = "Emotion", y = "Emotional Hits", colour = "Condition", title = "", element_text(size = 14)) +
scale_fill_Publication() +
scale_colour_Publication() +
theme_Publication() +
scale_y_continuous(breaks = c(0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95, 1))
plotemo
```
### c) Exploratory Analyses
#### Full mixed-effect model with condition
```{r s1-exp1-lmer, results = 'hide'}
class = lmer(log(rt_net) ~ condition * newclass * newemo + (1|number_ID) + (1|item),
na.action = na.omit,
data = data)
```
Summary
```{r s1-exp1-lmer-show}
tbl_regression(class,
label = list(condition ~ "Condition",
newemo ~ "Emotion",
newclass ~ "Class of Trangression")) %>%
modify_column_unhide(column = std.error) %>%
modify_column_unhide(column = statistic) %>%
modify_header(label = "**Variable**") %>%
bold_p() %>%
modify_fmt_fun(statistic ~ style_sigfig) %>%
add_significance_stars(hide_ci = FALSE,
hide_p = FALSE,
pattern = "{p.value}{stars}") %>%
add_glance_source_note(label = list(sigma ~ "\U03C3"),
include = c(AIC, sigma))
```
Anova & conditional and marginal coefficient of determination
```{r s1-exp1-lmer-info}
Anova(class, type = 3, ddf = "Satterthwaite")
round(r.squaredGLMM(class, method = "marginal"), 4)
```
- Main effect of <i>Emotion</i> $$\chi^2(5) = 11.8516, p = .037$$
Variance explained by the model:
$$R^2_m = 1.2\% \text{ (fixed)}$$
$$R^2_c = 44.07\% \text{ (fixed + random)}$$
Pairwise comparisons & estimated marginal means
```{r s1-exp1-lmer-cont}
emmeans(class, pairwise ~ newemo, lmer.df = "asymp", type = "response", adjust = "bonferroni")
```
Plot all interaction effects
```{r s1-exp1-lmer-plot}
plotclass = ggpredict(class, terms = c("newemo"), ci_level = 0.95) %>%
plot() +
labs(x = "Emotion", y = "Reaction Time (ms)", colour = "Emotion", title = "", element_text(size = 14)) +
scale_fill_Publication() +
scale_colour_Publication() +
theme_Publication()
plotclass
```
#### Full mixed-effect model without condition
```{r s1-exp2-lmer, results = 'hide'}
class2 = lmer(log(rt_net) ~ newclass * newemo + (1|number_ID) + (1|item),
na.action = na.omit,
data = data)
summary(class2)
```
Summary
```{r s1-exp2-lmer-show}
tbl_regression(class2,
label = list(newemo ~ "Emotion",
newclass ~ "Class of Trangression")) %>%
modify_column_unhide(column = std.error) %>%
modify_column_unhide(column = statistic) %>%
modify_header(label = "**Variable**") %>%
bold_p() %>%
modify_fmt_fun(statistic ~ style_sigfig) %>%
add_significance_stars(hide_ci = FALSE,
hide_p = FALSE,
pattern = "{p.value}{stars}") %>%
add_glance_source_note(label = list(sigma ~ "\U03C3"),
include = c(AIC, sigma))
```
Anova & conditional and marginal coefficient of determination
```{r s1-exp2-lmer-info}
Anova(class2, type = 3)
round(r.squaredGLMM(class2, method = "marginal"), 4)
```
- Main effect of <i>Emotion</i> $$\chi^2(5) = 43.6176, p < .001$$
- Interaction between <i>CLass</i> and <i>Emotion</i> $$\chi^2(20) = 43.6176, p < .001$$
Variance explained by the model:
$$R^2_m = 0.99\% \text{ (fixed)}$$
$$R^2_c = 43.83\% \text{ (fixed + random)}$$
#### Difference between the two models
```{r s1-exp1-2-lmer-comp}
anova(class, class2)
lrtest(class2, class)
```
- No significant difference between the 2 (ANOVA)
- Model with condition better than the one without: $$X^2(30) = 113.5, p < .001$$
#### RT with Condition and Class (preregistered)
```{r s1-exp3-rt-lmer}
exprt1 = lmer(log(rt_net) ~ condition * newclass + (1|number_ID) + (1|item),
na.action = na.omit,
data = data)
```
Summary
```{r s1-exp3-rt-lmer-show}
tbl_regression(exprt1,
label = list(condition ~ "Condition",
newclass ~ "Class of Trangression")) %>%
modify_column_unhide(column = std.error) %>%
modify_column_unhide(column = statistic) %>%
modify_header(label = "**Variable**") %>%
bold_p() %>%
modify_fmt_fun(statistic ~ style_sigfig) %>%
add_significance_stars(hide_ci = FALSE,
hide_p = FALSE,
pattern = "{p.value}{stars}") %>%
add_glance_source_note(label = list(sigma ~ "\U03C3"),
include = c(AIC, sigma))
```
Anova & conditional and marginal coefficient of determination
```{r s1-exp3-rt-lmer-info}
Anova(exprt1, type = 3)
round(r.squaredGLMM(exprt1, method = "marginal"), 4)
```
No significant effect
Variance explained by the model:
$$R^2_m = 0.14\% \text{ (fixed)}$$
$$R^2_c = 43.00\% \text{ (fixed + random)}$$
#### Emotional Hits with Condition and Class (preregistered)
```{r s1-exp3-hit-lmer}
exphit1 = glmer(score ~ condition * newclass + (1|number_ID) + (1|item),
na.action = na.omit,
data = data,
family = binomial,
control = glmerControl(optimizer = "bobyqa"))
```
Summary
```{r s1-exp3-hit-lmer-show}
tbl_regression(exphit1,
label = list(condition ~ "Condition",
newclass ~ "Class of Transgression")) %>%
modify_column_unhide(column = std.error) %>%
modify_column_unhide(column = statistic) %>%
modify_header(label = "**Variable**") %>%
bold_p() %>%
modify_fmt_fun(statistic ~ style_sigfig) %>%
add_significance_stars(hide_ci = FALSE,
hide_p = FALSE,
pattern = "{p.value}{stars}") %>%
add_glance_source_note(label = list(sigma ~ "\U03C3"),
include = c(AIC, sigma))
```
Anova & conditional and marginal coefficient of determination
```{r s1-exp3-hit-lmer-info}
Anova(exphit1, type = 3)
round(r.squaredGLMM(exphit1, method = "marginal"), 4)
```
- Main effect of <i>Class</i> $$\chi^2(4) = 20.194, p < .001$$
Variance explained by the model:
$$R^2_m = 0.69\% \text{ (fixed)}$$
$$R^2_c = 30.83\% \text{ (fixed + random)}$$
Pairwise comparisons (estimated marginal means and pairwise comparisons)
```{r s1-exp3-hit-lmer-cont}
emmeans(exphit1, pairwise ~ newclass, lmer.df = "asymp", type = "response", adjust = "bonferroni")
```
Plot all interaction effects
```{r s1-exp3-hit-lmer-plot}
plotexphit1 = ggpredict(exphit1, terms = c("newclass"), ci_level = 0.95) %>%
plot() +
labs(x = "Class of Transgression", y = "Emotional Hits", colour = "Class of Transgression", title = "", element_text(size = 14)) +
scale_fill_Publication() +
scale_colour_Publication() +
theme_Publication() +
scale_y_continuous(breaks = c(0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95, 1))
plotexphit1
```
#### RT with Condition and Class + covariates (preregistered)
Full mixed-effect model
```{r s1-exp4-rt-lmer, results = 'hide'}
fullcov = lmer(log(rt_net) ~ condition * newemo + age + newgender + political_orientation + (1|number_ID) + (1|item),
na.action = na.omit,
data = data)
```
Summary
```{r s1-exp4-rt-lmer-show}
tbl_regression(fullcov,
label = list(condition ~ "Condition",
newemo ~ "Emotion",
age ~ "Age",
newgender ~ "Sex",
political_orientation ~ "Political Orientation (1-Liberal to 7-Conservative)")) %>%
modify_column_unhide(column = std.error) %>%
modify_column_unhide(column = statistic) %>%
modify_header(label = "**Variable**") %>%
bold_p() %>%
modify_fmt_fun(statistic ~ style_sigfig) %>%
add_significance_stars(hide_ci = FALSE,
hide_p = FALSE,
pattern = "{p.value}{stars}") %>%
add_glance_source_note(label = list(sigma ~ "\U03C3"),
include = c(AIC, sigma))
```
Anova & conditional and marginal coefficient of determination
```{r s1-exp4-rt-lmer-info}
Anova(fullcov, type = 3, ddf = "Satterthwaite")
round(r.squaredGLMM(fullcov, method = "marginal"), 4)
```
- Main effect of <i>Emotion</i> $$\chi^2(5) = 117.204, p < .001$$
- Interaction of <i>Emotion</i> and <i>Condition</i> $$\chi^2(5) = 23.854, p < .001$$
Variance explained by the model:
$$R^2_m = 2.77\% \text{ (fixed)}$$
$$R^2_c = 45.24\% \text{ (fixed)}$$
Pairwise comparisons (estimated marginal means and pairwise comparisons)
```{r s1-exp4-rt-lmer-cont}
emmeans(fullcov, pairwise ~ newemo, lmer.df = "asymp", type = "response", adjust = "bonferroni")
emmeans(fullcov, pairwise ~ newemo|condition, lmer.df = "asymp", type = "response", adjust = "bonferroni")
```
Plot all interaction effects
```{r s1-exp4-rt-lmer-plot}
plotfullcov = ggpredict(fullcov, terms = c("newemo", "condition"), ci_level = 0.95) %>%
plot() +
labs(x = "Emotion", y = "Reaction Time (ms)", colour = "Condition", title = "", element_text(size = 14)) +
scale_fill_Publication() +
scale_colour_Publication() +
theme_Publication() +
scale_y_continuous(breaks = c(800, 850, 900, 950, 1000, 1050, 1100, 1150, 1200, 1250))
plotfullcov
```
#### Emotional Hits with Condition and Class + covariates (preregistered)
Full mixed-effect model
```{r s1-exp4-hit-lmer, results = 'hide'}
emocov = glmer(score ~ newemo * condition + age + newgender + political_orientation + (1|number_ID) + (1|item),
data = data,
family = binomial,
na.action = na.omit,
control = glmerControl(optimizer = "bobyqa"))
```
Summary
```{r s1-exp4-hit-lmer-show}
tbl_regression(emocov,
label = list(condition ~ "Condition",
newemo ~ "Emotion",
age ~ "Age",
newgender ~ "Sex",
political_orientation ~ "Political Orientation (1-Liberal to 7-Conservative)")) %>%
modify_column_unhide(column = std.error) %>%
modify_column_unhide(column = statistic) %>%
modify_header(label = "**Variable**") %>%
bold_p() %>%
modify_fmt_fun(statistic ~ style_sigfig) %>%
add_significance_stars(hide_ci = FALSE,
hide_p = FALSE,
pattern = "{p.value}{stars}") %>%
add_glance_source_note(label = list(sigma ~ "\U03C3"),
include = c(AIC, sigma))
```
Anova & conditional and marginal coefficient of determination
```{r s1-exp4-hit-lmer-info}
Anova(emocov, type = 3, ddf = "Satterthwaite")
round(r.squaredGLMM(emocov, method = "marginal"), 4)
```
- Main effect of <i>Emotion</i> $$\chi^2(5) = 265.849, p < .001$$
- Main effect of <i>Age</i> $$\chi^2(1) = 5.378, p = .020$$
- Interaction of <i>Emotion</i> and <i>Condition</i> $$\chi^2(5) = 149.011, p < .001$$
Variance explained by the model:
$$R^2_m = 7.16\% \text{ (fixed)}$$
$$R^2_c = 35.59\% \text{ (fixed + random)}$$
Pairwise comparisons (estimated marginal means and pairwise comparisons)
```{r s1-exp4-hit-lmer-cont}
emmeans(emocov, pairwise ~ newemo, lmer.df = "asymp", type = "response", adjust = "bonferroni")
emmeans(emocov, pairwise ~ condition, lmer.df = "asymp", type = "response", adjust = "bonferroni")
emmeans(emocov, pairwise ~ newemo|condition, lmer.df = "asymp", type = "response", adjust = "bonferroni")
```
Plot all interaction effects
```{r s1-exp4-hit-lmer-plot}
plotemocov = ggpredict(emocov, terms = c("age", "condition", "newemo"), ci_level = 0.95) %>%
plot() +
labs(x = "Age", y = "Emotional Hits", colour = "Condition", title = "", element_text(size = 14)) +
scale_fill_Publication() +
scale_colour_Publication() +
theme_Publication() +
scale_x_continuous(breaks = c(20, 30, 40, 50, 60, 70)) +
scale_y_continuous(breaks = c(0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1))
plotemocov
```
## 2. Study 2
### a) Reaction Time
#### Full mixed-effect model
```{r s2-rt-lmer, results = 'hide'}
full2 = lmer(log(rt_net) ~ condition * newemo * scenarios + (1|number_ID) + (1|item),
na.action = na.omit,
data = data2)
```
Summary
```{r s2-rt-lmer-show}
tbl_regression(full2,
label = list(condition ~ "Condition",
newemo ~ "Emotion",
scenarios ~ "Scenario")) %>%
modify_column_unhide(column = std.error) %>%
modify_column_unhide(column = statistic) %>%
modify_header(label = "**Variable**") %>%
bold_p() %>%
modify_fmt_fun(statistic ~ style_sigfig) %>%
add_significance_stars(hide_ci = FALSE,
hide_p = FALSE,
pattern = "{p.value}{stars}") %>%
add_glance_source_note(label = list(sigma ~ "\U03C3"),
include = c(AIC, sigma))
```
Conditional and marginal coefficient of determination
```{r s2-rt-lmer-info}
Anova(full2, type = 3)
round(r.squaredGLMM(full2, method = "marginal"), 4)
```
- Main effect of <i>Emotion</i> $$\chi^2(3) = 58.033, p < .001$$
- Interaction of <i>Condition</i> and <i>Emotion</i> $$\chi^2(3) = 8.263, p = .041$$
- Interaction of <i>Emotion</i> and <i>Scenario</i> $$\chi^2(3) = 13.005, p = .005$$
Variance explained by the model:
$$R^2_m = 1.55\% \text{ (fixed)}$$
$$R^2_c = 33.90\% \text{ (fixed + random)}$$
#### Pairwise comparisons & estimated marginal means
```{r s2-rt-lmer-cont}
emmeans(full2, pairwise ~ newemo, lmer.df = "asymp", type = "response", adjust = "bonferroni")
emmeans(full2, pairwise ~ scenarios, lmer.df = "asymp", type = "response", adjust = "bonferroni")
emmeans(full2, pairwise ~ newemo|condition, lmer.df = "asymp", type = "response", adjust = "bonferroni")
emmeans(full2, pairwise ~ newemo|scenarios, lmer.df = "asymp", type = "response", adjust = "bonferroni")
emmeans(full2, pairwise ~ scenarios|newemo, lmer.df = "asymp", type = "response", adjust = "bonferroni")
```
#### Plot all interaction effects
```{r s2-rt-lmer-plot}
plotfull2 = ggpredict(full2, terms = c("scenarios", "condition", "newemo"), ci_level = 0.95) %>%
plot() +
labs(x = "Scenario", y = "Reaction Time (ms)", colour = "Condition", title = "", element_text(size = 14)) +
scale_fill_Publication() +
scale_colour_Publication() +
theme_Publication() +
scale_y_continuous(breaks = c(800, 850, 900, 950, 1000, 1050, 1100, 1150, 1200, 1250, 1300, 1350, 1400))
plotfull2
```
### b) Emotional Hits
#### Full generalized mixed-effect model
```{r s2-hit-lmer, results = 'hide'}
emo2 = glmer(score ~ scenarios * newemo * condition + (1|number_ID) + (1|item),
data = data2,
family = binomial,
control = glmerControl(optimizer = "bobyqa"))
```
Summary
```{r s2-hit-lmer-show}
tbl_regression(emo2,
label = list(condition ~ "Condition",
newemo ~ "Emotion",
scenarios ~ "Scenario")) %>%
modify_column_unhide(column = std.error) %>%
modify_column_unhide(column = statistic) %>%
modify_header(label = "**Variable**") %>%
bold_p() %>%
modify_fmt_fun(statistic ~ style_sigfig) %>%
add_significance_stars(hide_ci = FALSE,
hide_p = FALSE,
pattern = "{p.value}{stars}") %>%
add_glance_source_note(label = list(sigma ~ "\U03C3"),
include = c(AIC, sigma))
```
Anova & conditional and marginal coefficient of determination
```{r s2-hit-lmer-info}
Anova(emo2, type = 3)
round(r.squaredGLMM(emo2, method = "marginal"), 4)
```
- Main effect of <i>Emotion</i> $$\chi^2(3) = 28.663, p < .001$$
- Interaction between <i>Emotion</i> and <i>Scenario</i> $$\chi^2(3) = 46.567, p < .001$$
- Interaction between <i>Emotion</i> and <i>Condition</i> $$\chi^2(3) = 25.566, p < .001$$
- Interaction between <i>Emotion</i>, <i>Condition</i> and <i>Scenario</i> $$\chi^2(3) = 10.004, p = .019$$
Variance explained by the model:
$$R^2_m = 1.86\% \text{ (fixed)}$$
$$R^2_c = 22.64\% \text{ (fixed + random)}$$
#### Pairwise comparisons (estimated marginal means and pairwise comparisons)
```{r s2-hit-lmer-cont}
emmeans(emo2, pairwise ~ newemo, lmer.df = "asymp", type = "response", adjust = "bonferroni")
emmeans(emo2, pairwise ~ scenarios, lmer.df = "asymp", type = "response", adjust = "bonferroni")
emmeans(emo2, pairwise ~ newemo|scenarios, lmer.df = "asymp", type = "response", adjust = "bonferroni")
emmeans(emo2, pairwise ~ scenarios|newemo, lmer.df = "asymp", type = "response", adjust = "bonferroni")
emmeans(emo2, pairwise ~ newemo|condition, lmer.df = "asymp", type = "response", adjust = "bonferroni")
emmeans(emo2, pairwise ~ scenarios*condition*newemo, type = "response", adjust = "bonferroni")
```
#### Plot all interaction effects
```{r s2-hit-lmer-plot}
plotemo2 = ggpredict(emo2, terms = c("newemo", "condition", "scenarios"), ci_level = 0.95) %>%
plot() +
labs(x = "Emotion", y = "Emotional Hits", colour = "Condition", title = "", element_text(size = 14)) +
scale_fill_Publication() +
scale_colour_Publication() +
theme_Publication() +
scale_y_continuous(breaks = c(0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95, 1))
plotemo2
```
### c) Exploratory Analyses
Preparation
```{r s2-exp1-prep}
subdata = data2 %>%
filter(newclass %in% c("Harm", "Injustice", "Humiliation"))
```
#### RT with Condition and Class (preregistered)
```{r s2-exp1-rt-lmer}
exprt2 = lmer(log(rt_net) ~ condition * newclass + (1|number_ID) + (1|item),
na.action = na.omit,
data = subdata)
```
Summary
```{r s2-exp1-rt-lmer-show}
tbl_regression(exprt2,
label = list(condition ~ "Condition",
newclass ~ "Class of Transgression")) %>%
modify_column_unhide(column = std.error) %>%
modify_column_unhide(column = statistic) %>%
modify_header(label = "**Variable**") %>%
bold_p() %>%
modify_fmt_fun(statistic ~ style_sigfig) %>%
add_significance_stars(hide_ci = FALSE,
hide_p = FALSE,
pattern = "{p.value}{stars}") %>%
add_glance_source_note(label = list(sigma ~ "\U03C3"),
include = c(AIC, sigma))
```
Anova & conditional and marginal coefficient of determination
```{r s2-exp1-rt-lmer-info}
Anova(exprt2, type = 3)
round(r.squaredGLMM(exprt2, method = "marginal"), 4)
```
- Main effect of <i>Class</i> $$\chi^2(2) = 11.381, p = .003$$
Variance explained by the model:
$$R^2_m = 1.15\% \text{ (fixed)}$$
$$R^2_c = 34.58\% \text{ (fixed + random)}$$
Pairwise comparisons & estimated marginal means
```{r s2-exp1-rt-lmer-cont}
emmeans(exprt2, pairwise ~ newclass, lmer.df = "asymp", type = "response", adjust = "bonferroni")
```
Plot all interaction effects
```{r s2-exp1-rt-lmer-plot}
plotexprt2 = ggpredict(exprt2, terms = c("newclass"), ci_level = 0.95) %>%
plot() +
labs(x = "Class of Trangression", y = "Reaction Time (ms)", title = "", element_text(size = 14)) +
scale_fill_Publication() +
scale_colour_Publication() +
theme_Publication()
plotexprt2
```
#### Emotional Hits with Condition and Class (preregistered)
```{r s2-exp1-hit-lmer}
exphit2 = glmer(score ~ condition * newclass + (1|number_ID) + (1|item),
data = subdata,
family = binomial,
control = glmerControl(optimizer = "bobyqa"))
```
Summary
```{r s2-exp1-hit-lmer-show}
tbl_regression(exphit2,
label = list(condition ~ "Condition",
newclass ~ "Class of Transgression")) %>%
modify_column_unhide(column = std.error) %>%
modify_column_unhide(column = statistic) %>%
modify_header(label = "**Variable**") %>%
bold_p() %>%
modify_fmt_fun(statistic ~ style_sigfig) %>%
add_significance_stars(hide_ci = FALSE,
hide_p = FALSE,
pattern = "{p.value}{stars}") %>%
add_glance_source_note(label = list(sigma ~ "\U03C3"),
include = c(AIC, sigma))
```
Anova & conditional and marginal coefficient of determination
```{r s2-exp1-hit-lmer-info}
Anova(exphit2, type = 3)
round(r.squaredGLMM(exphit2, method = "marginal"), 4)
```
- Main effect of <i>Class</i> $$\chi^2(2) = 30.594, p < .001$$
Variance explained by the model:
$$R^2_m = 0.017\% \text{ (fixed)}$$