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behavioral_analyses.R
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behavioral_analyses.R
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####========================================= A.L.R.R.2019-2020
### DESCRIPTION
## This script is used for analyzing the effect of "scene
## ...complexity" or "scene object load" on behavioral
## ...performance during retrieval
####==========================================================
### REQUIRED PACKAGES
## Install possibly relevant packages
# install.packages("pacman")
# require(pacman)
# pacman::p_load(ggplot2, dplyr, devtools, ggthemes, ggvis,
# plotly, rio, rmarkdown, shiny, stringr,
# tidyr, readxl, ggpubr, pastecs, psych,
# car, tidyverse, rstatix, BayesFactor,
# sjstats, ggsci, scales)
####==========================================================
### WORKING DIRECTORY
setwd('/work/dir/')
####==========================================================
### FILES TO WORK WITH
# Create total
if (exists('total_div')) {
total <- total_div
} else {
total <- read.csv("./data/total_div.csv")
}
# Make sure SCD is factor
total$is_SCD <- factor(total$is_SCD, levels = c("SCD","CON"))
####==========================================================
### DATA EXTENSION
## Prepare data frames for statistical analyses
# Extend data frames
# Hit Rate All
HR_all <- total %>% pivot_longer(cols = c(
"hit_rate_hi_all", "hit_rate_lo_all"),
names_to = "HR_all_complex",
values_to = "value_HR_all_complex")
HR_all$subject <- factor(HR_all$subject,
levels=unique(HR_all$subject))
HR_all$HR_all_complex <- factor(HR_all$HR_all_complex,
levels=unique(
HR_all$HR_all_complex))
HR_all <- as.data.frame(HR_all)
# FA Rate All
FA_all <- total %>% pivot_longer(cols = c(
"FA_rate_hi_all", "FA_rate_lo_all"),
names_to = "FA_all_complex",
values_to = "value_FA_all_complex")
FA_all$subject <- factor(FA_all$subject,
levels=unique(FA_all$subject))
FA_all$FA_all_complex <- factor(FA_all$FA_all_complex,
levels=unique(
FA_all$FA_all_complex))
FA_all <- as.data.frame(FA_all)
# Recognition Accuracy All
Rec_acc_all <- total %>% pivot_longer(cols = c(
"rec_accuracy_hi_all", "rec_accuracy_lo_all"),
names_to = "rec_acc_all_complex",
values_to = "value_rec_acc_all_complex")
Rec_acc_all$subject <- factor(Rec_acc_all$subject,
levels=unique(
Rec_acc_all$subject))
Rec_acc_all$rec_acc_all_complex <-
factor(Rec_acc_all$rec_acc_all_complex,
levels=unique(
Rec_acc_all$rec_acc_all_complex))
Rec_acc_all <- as.data.frame(Rec_acc_all)
####==========================================================
### DATA INSPECTION
# Identify outliers
HR_all %>% group_by(HR_all_complex, is_SCD) %>%
identify_outliers(value_HR_all_complex)
FA_all %>% group_by(FA_all_complex, is_SCD) %>%
identify_outliers(value_FA_all_complex)
Rec_acc_all %>% group_by(rec_acc_all_complex, is_SCD) %>%
identify_outliers(value_rec_acc_all_complex)
# Test for normality
HR_all %>% group_by(HR_all_complex, is_SCD) %>%
shapiro_test(value_HR_all_complex)
FA_all %>% group_by(FA_all_complex, is_SCD) %>%
shapiro_test(value_FA_all_complex)
Rec_acc_all %>% group_by(rec_acc_all_complex, is_SCD) %>%
shapiro_test(value_rec_acc_all_complex)
# Homogeneity of variance
HR_all %>% group_by(HR_all_complex) %>%
levene_test(value_HR_all_complex ~ is_SCD)
FA_all %>% group_by(FA_all_complex) %>%
levene_test(value_FA_all_complex ~ is_SCD)
Rec_acc_all %>% group_by(rec_acc_all_complex) %>%
levene_test(value_rec_acc_all_complex ~ is_SCD)
# Homogeneity of covariances
box_m(HR_all[, "value_HR_all_complex", drop = F],
HR_all$is_SCD)
box_m(FA_all[, "value_FA_all_complex", drop = F],
FA_all$is_SCD)
box_m(Rec_acc_all[, "value_rec_acc_all_complex",
drop = F],
Rec_acc_all$is_SCD)
####==========================================================
### Mixed ANOVA Hit Rate
## 2x2 mixed: IV between: is_SCD; IV within: complexity;
## ...DV: recognition accuracy/hit rate/FA rate
## Mixed ANOVA: y ~ b1*b2*w1 + Error(id/w1) -> [?anova_test]
res.aov.HR_all <- anova_test(data = HR_all,
dv = value_HR_all_complex,
wid = subject,
between = is_SCD,
within = HR_all_complex,
effect.size = "pes")
get_anova_table(res.aov.HR_all, correction = "auto")
####==========================================================
### Mixed ANOVA FA Rate
res.aov.FA_all <- anova_test(data = FA_all,
dv = value_FA_all_complex,
wid = subject,
between = is_SCD,
within = FA_all_complex,
detailed = F,
effect.size = "pes")
get_anova_table(res.aov.FA_all)
# Obtain means across load level
describeBy(FA_all$value_FA_all_complex,
FA_all$FA_all_complex)
####==========================================================
### Mixed ANOVA Recognition Accuracy
res.aov.rec_all <- anova_test(data = Rec_acc_all,
dv = value_rec_acc_all_complex,
wid = subject,
between = is_SCD,
within = rec_acc_all_complex,
effect.size = "pes")
get_anova_table(res.aov.rec_all)
# Obtain means across load levels
describeBy(Rec_acc_all$value_rec_acc_all_complex,
Rec_acc_all$rec_acc_all_complex)
####==========================================================
### One-way ANOVA to study significant interaction effects
## Within each WS group, comparing between-subject variable
bs <- HR_all %>%
group_by(HR_all_complex) %>%
anova_test(dv = value_HR_all_complex,
wid = subject,
between = is_SCD) %>%
get_anova_table() %>% adjust_pvalue(method = "bonferroni")
# Save P value for plotting later on
sig_pval_bs_HR_all <- bs$p[which(
bs$HR_all_complex=='hit_rate_hi_all')]
pvalhi_hr_all <- paste("~italic(P) ==",
sig_pval_bs_HR_all)
sig_pval_bs_HR_all <- bs$p[which(
bs$HR_all_complex=='hit_rate_lo_all')]
pvallo_hr_all <- paste("~italic(P) ==",
sig_pval_bs_HR_all)
## Within each BS group, comparing within-subject variable
ws <- FA_all %>%
group_by(is_SCD) %>%
anova_test(dv = value_FA_all_complex, wid = subject,
within = FA_all_complex,
effect.size = "pes") %>%
get_anova_table() %>% adjust_pvalue(method = "bonferroni")
# Save P-value for plotting later on
sig_pval_ws_FA_all <- ws$p[which(ws$is_SCD=='CON')]
pvalcon_fa_all <- paste("~italic(P) ==",
sig_pval_ws_FA_all)
sig_pval_ws_FA_all <- ws$p[which(ws$is_SCD=='SCD')]
pvalscd_fa_all <- paste("~italic(P) ==",
sig_pval_ws_FA_all)
####==========================================================
### PLOTTING
# Hit rate
bxp_HR_all <- ggboxplot(HR_all, x = "HR_all_complex",
y = "value_HR_all_complex",
fill = "is_SCD", palette = "npg",
#ylab = "Hit Rate",
xlab = "Scene object load",
notch = F,
add = "jitter",
panel.labs = list(
"is_SCD" = c("SCD", "CON")))
ggpar(bxp_HR_all, ylim = c(0, 1), font.x = 18, font.y = 18,
font.tickslab = 14, legend.title = "Group",
font.legend = 18,
legend = "top", font.title = c(20,"bold")
) + scale_x_discrete(labels=c("High", "Low")
) + grids(axis = "y") +
#geom_segment(aes(x = 1.8, xend = 2.2, y = 1, yend = 1),
#size=0.5, show.legend=F, lineend = "square",
#color = "black") + #"#4DBBD5FF" for blue
#geom_segment(aes(x = 0.8, xend = 1.2, y = 1, yend = 1),
#size=0.5, show.legend=F, lineend = "square",
#color = "black") + #"#E64B35FF" for red
#annotate(geom="text", x=c(1, 2), y = c(1.03, 1.03),
#label = c(pvalhi_hr_all, pvallo_hr_all),
#color="black", size = 5, parse = T) +
ggtitle("Hit Rate") +
theme(plot.title = element_text(hjust = 0.5),
axis.title.y=element_blank())
ggsave('/work/dir/figures/bxp_HR_all_no_pval.png',
dpi = 500, width = 20,
height = 15, units = "cm")
# FA rate
bxp_FA_all <- ggboxplot(FA_all, x = "FA_all_complex",
y = "value_FA_all_complex",
fill = "is_SCD", palette = "npg",
#ylab = "False Alarm Rate",
xlab = "Scene object load",
notch = F,
add = "jitter",
panel.labs = list(
"is_SCD" = c("SCD", "CON")))
ggpar(bxp_FA_all, ylim = c(0, 1), font.x = 18,
font.y = 18,
font.tickslab = 14, legend.title = "Group",
font.legend = 18,
legend = "top", font.title = c(20,"bold")
) + scale_x_discrete(labels=c("High", "Low")
) + grids(axis = "y") +
geom_segment(aes(x = 1.2, xend = 2.2, y = 0.83,
yend = 0.83),
size=0.5, show.legend=F, lineend = "square",
color = "black") + #"#4DBBD5FF" for blue
geom_segment(aes(x = 0.8, xend = 1.8, y = 0.97,
yend = 0.97),
size=0.5, show.legend=F, lineend = "square",
color = "black") + #"#E64B35FF" for red
annotate(geom="text", x=c(1.69, 1.3), y = c(0.86, 1),
label = c(pvalcon_fa_all, pvalscd_fa_all),
color="black", size = 5, parse = T) +
ggtitle("False Alarm Rate") +
theme(plot.title = element_text(hjust = 0.5),
axis.title.y=element_blank())
ggsave("/work/dir/figures/bxp_FA_all.png",
dpi = 500, width = 20,
height = 15, units = "cm")
# Recognition Accuracy
bxp_rec_acc_all <- ggboxplot(Rec_acc_all,
x = "rec_acc_all_complex",
y = "value_rec_acc_all_complex",
fill = "is_SCD",
palette = "npg",
#ylab = "Recognition Accuracy",
xlab = "Scene object load",
notch = F,
add = "jitter",
panel.labs = list(
"is_SCD" = c("SCD", "CON"))) +
geom_hline(yintercept=0, size=0.3)
pval_sig_RA_all_mixed <-
as.character(res.aov.rec_all$p[which(
res.aov.rec_all$Effect=='rec_acc_all_complex')])
pvalraallcomplex <- paste("~italic(P) ==",
pval_sig_RA_all_mixed)
ggpar(bxp_rec_acc_all, ylim = c(-0.25, 0.75),
font.x = 18, font.y = 18,
font.tickslab = 14, legend.title = "Group",
font.legend = 18,
legend = "top", font.title = c(20,"bold")
) + scale_x_discrete(labels=c("High", "Low")
) + grids(axis = "y") +
geom_segment(aes(x = 1, xend = 2, y = 0.66, yend = 0.66),
size=0.4, show.legend=F, lineend = "square",
color = "black") +
annotate(geom="text", x=1.5, y=0.69,
label=pvalraallcomplex,
color="black", size = 5, parse = T) +
ggtitle("Recognition Accuracy") +
theme(plot.title = element_text(hjust = 0.5),
axis.title.y=element_blank())
ggsave("/work/dir/figures/bxp_rec_acc_all.png",
dpi = 500, width = 20,
height = 15, units = "cm")