Frontal Alpha Asymmetry, PeMyCreP
Reference at Infinity, REST (Reference Electrode Standardization Technique).
Alvaro Rivera-Rei
13 abril, 2023
cat("\014") # clean terminal
rm(list = ls()) # clean workspace
try(dev.off(), silent = TRUE) # close all plots
library(afex)
library(emmeans)
library(ggplot2)
library(ggridges)
library(ggdist)
library(dplyr)
library(reshape2)
library(GGally)
library(forcats)
library(readxl)theme_set(
theme_minimal()
)
a_posteriori <- function(afex_aov, sig_level = .05) {
factors <- as.list(rownames(afex_aov$anova_table))
for (j in 1:length(factors)) {
if (grepl(":", factors[[j]])) {
factors[[j]] <- unlist(strsplit(factors[[j]], ":"))
}
}
p_values <- afex_aov$anova_table$`Pr(>F)`
for (i in 1:length(p_values)) {
if (p_values[i] <= sig_level) {
print(emmeans(afex_aov, factors[[i]], contr = "pairwise"))
cat(rep("_", 100), '\n', sep = "")
}
}
}eeg_check <- read_excel(file.path('..', 'bad channels resting 2022.xlsx'))
eeg_check <- eeg_check %>%
mutate(badchan_num = ifelse(badchan == '0', 0, sapply(strsplit(badchan, " "), length)))
bad_eeg <- eeg_check$name[eeg_check$commentary != 'ok']
master_dir <- '~/Insync/OneDrive/LABWORKS_onedrive/Huepe/Fdcyt_2020/resting/processing'
data_dir <- paste(master_dir, 'results', sep = '/')
alpha_power_data_name <- paste(data_dir, 'foof_data_2_to_48_Hz.csv', sep='/')
alpha_power_data <- read.table(alpha_power_data_name, header = TRUE, strip.white = TRUE, sep = ",")
alpha_power_data$vulnerability[grepl("nVul", alpha_power_data$Dataset)] <- "Invulnerable"
alpha_power_data$vulnerability[!grepl("nVul", alpha_power_data$Dataset)] <- "Vulnerable"
alpha_power_data$belief[grepl("nCr", alpha_power_data$Dataset)] <- "Unbeliever"
alpha_power_data$belief[!grepl("nCr", alpha_power_data$Dataset)] <- "Believer"
alpha_power_data$sex[grepl("F", alpha_power_data$Dataset)] <- "Female"
alpha_power_data$sex[!grepl("F", alpha_power_data$Dataset)] <- "Male"
alpha_power_data$Dataset <- factor(alpha_power_data$Dataset)
alpha_power_data$Electrode <- factor(alpha_power_data$Electrode)
alpha_power_data$Subject <- factor(alpha_power_data$Subject)
alpha_power_data$vulnerability <- factor(alpha_power_data$vulnerability)
alpha_power_data$belief <- factor(alpha_power_data$belief)
alpha_power_data$sex <- factor(alpha_power_data$sex)
alpha_power_data$hemisphere[alpha_power_data$Electrode %in% c('Fp1', 'E092-AF3a', 'AF7', 'E089-F1a', 'E100-F3a', 'E101-F5a', 'F7', 'E088-FC1a')] <- 'Left'
alpha_power_data$hemisphere[alpha_power_data$Electrode %in% c('Fp2', 'E079-AF4a', 'AF8', 'E076-F2a', 'E068-F4a', 'E069-F6a', 'F8', 'E075-FC2a')] <- 'Right'
alpha_power_data$hemisphere <- factor(alpha_power_data$hemisphere)
alpha_power_data$electrode_pair[alpha_power_data$Electrode %in% c('Fp1' , 'Fp2')] <- 'Fp1-Fp2'
alpha_power_data$electrode_pair[alpha_power_data$Electrode %in% c('E092-AF3a', 'E079-AF4a')] <- 'AF3-AF4'
alpha_power_data$electrode_pair[alpha_power_data$Electrode %in% c('AF7' , 'AF8')] <- 'AF7-AF8'
alpha_power_data$electrode_pair[alpha_power_data$Electrode %in% c('E089-F1a' , 'E076-F2a')] <- 'F1-F2'
alpha_power_data$electrode_pair[alpha_power_data$Electrode %in% c('E100-F3a' , 'E068-F4a')] <- 'F3-F4'
alpha_power_data$electrode_pair[alpha_power_data$Electrode %in% c('E101-F5a' , 'E069-F6a')] <- 'F5-F6'
alpha_power_data$electrode_pair[alpha_power_data$Electrode %in% c('F7' , 'F8')] <- 'F7-F8'
alpha_power_data$electrode_pair[alpha_power_data$Electrode %in% c('E088-FC1a', 'E075-FC2a')] <- 'FC1-FC2'
alpha_power_data$electrode_pair <- factor(alpha_power_data$electrode_pair, levels = c('Fp1-Fp2', 'AF3-AF4', 'AF7-AF8', 'F1-F2', 'F3-F4','F5-F6', 'F7-F8', 'FC1-FC2'))
write.csv(alpha_power_data, paste(data_dir, '/alpha_power_data_clean.csv', sep = ''), row.names = FALSE)
asymmetry_Fp2_Fp1 <- c()
asymmetry_AF4_AF3 <- c()
asymmetry_AF8_AF7 <- c()
asymmetry_F2_F1 <- c()
asymmetry_F4_F3 <- c()
asymmetry_F6_F5 <- c()
asymmetry_F8_F7 <- c()
asymmetry_FC2_FC1 <- c()
subj_block <- unique(alpha_power_data[c("Subject" ,"vulnerability" ,"belief" ,"sex")])
for (subj in subj_block$Subject) {
subject_data <- subset(alpha_power_data, Subject == subj)
asymmetry_Fp2_Fp1 <- c(asymmetry_Fp2_Fp1, subject_data[which(subject_data$Electrode == 'Fp2') , 5] - subject_data[which(subject_data$Electrode=='Fp1') , 5])
asymmetry_AF4_AF3 <- c(asymmetry_AF4_AF3, subject_data[which(subject_data$Electrode == 'E079-AF4a'), 5] - subject_data[which(subject_data$Electrode=='E092-AF3a'), 5])
asymmetry_AF8_AF7 <- c(asymmetry_AF8_AF7, subject_data[which(subject_data$Electrode == 'AF8') , 5] - subject_data[which(subject_data$Electrode=='AF7') , 5])
asymmetry_F2_F1 <- c(asymmetry_F2_F1 , subject_data[which(subject_data$Electrode == 'E076-F2a') , 5] - subject_data[which(subject_data$Electrode=='E089-F1a') , 5])
asymmetry_F4_F3 <- c(asymmetry_F4_F3 , subject_data[which(subject_data$Electrode == 'E068-F4a') , 5] - subject_data[which(subject_data$Electrode=='E100-F3a') , 5])
asymmetry_F6_F5 <- c(asymmetry_F6_F5 , subject_data[which(subject_data$Electrode == 'E069-F6a') , 5] - subject_data[which(subject_data$Electrode=='E101-F5a') , 5])
asymmetry_F8_F7 <- c(asymmetry_F8_F7 , subject_data[which(subject_data$Electrode == 'F8') , 5] - subject_data[which(subject_data$Electrode=='F7') , 5])
asymmetry_FC2_FC1 <- c(asymmetry_FC2_FC1, subject_data[which(subject_data$Electrode == 'E075-FC2a'), 5] - subject_data[which(subject_data$Electrode=='E088-FC1a'), 5])
}
alpha_asymmetry_data <- data.frame(subj_block, asymmetry_Fp2_Fp1, asymmetry_AF4_AF3, asymmetry_AF8_AF7, asymmetry_F2_F1, asymmetry_F4_F3, asymmetry_F6_F5, asymmetry_F8_F7, asymmetry_FC2_FC1)
write.csv(alpha_asymmetry_data, paste(data_dir, '/alpha_asymmetry_data_clean.csv', sep = ''), row.names = FALSE)1 Spectral decomposition
options(width = 100)
mytable <- xtabs(~ sex + belief, data = alpha_asymmetry_data)
ftable(addmargins(mytable)) belief Believer Unbeliever Sum
sex
Female 24 24 48
Male 17 15 32
Sum 41 39 80- Infinity Reference or Reference Electrode Standardization Technique
(REST).
- 120 consecutive segments, 5 seconds each.
- PSD computed with Welch’s method.
1.1 Scalp Map, mean power 9-11 Hz
1.2 PSD topography, 1 to 55 Hz, grand average
1.3 PSD topography, 4 to 30 Hz, grand average
1.4 Frontal Electrodes, 4 to 30 Hz, grand average
- 2 standard error bands
2 Parameterization of neural power spectra
- Each individual PSD is regarded as a combination of an aperiodic component and putative periodic oscillatory peaks.
2.1 Individual Electrodes
2.2 Individual Subject
3 General Description
options(width = 100)
summary(alpha_asymmetry_data) Subject vulnerability belief sex asymmetry_Fp2_Fp1
1 : 1 Invulnerable:66 Believer :41 Female:48 Min. :-0.464384
3 : 1 Vulnerable :14 Unbeliever:39 Male :32 1st Qu.:-0.067196
6 : 1 Median :-0.010091
10 : 1 Mean :-0.004637
15 : 1 3rd Qu.: 0.050836
16 : 1 Max. : 0.332207
(Other):74 NA's :15
asymmetry_AF4_AF3 asymmetry_AF8_AF7 asymmetry_F2_F1 asymmetry_F4_F3 asymmetry_F6_F5
Min. :-0.287080 Min. :-0.34744 Min. :-0.29279 Min. :-0.35884 Min. :-0.25410
1st Qu.:-0.064502 1st Qu.:-0.06967 1st Qu.:-0.03297 1st Qu.:-0.05566 1st Qu.:-0.08199
Median :-0.009179 Median : 0.03051 Median : 0.01824 Median : 0.01913 Median : 0.05753
Mean :-0.004620 Mean : 0.03278 Mean : 0.00464 Mean : 0.03268 Mean : 0.06435
3rd Qu.: 0.064638 3rd Qu.: 0.09241 3rd Qu.: 0.05174 3rd Qu.: 0.09606 3rd Qu.: 0.14422
Max. : 0.378251 Max. : 0.50837 Max. : 0.23987 Max. : 0.55158 Max. : 0.52575
NA's :16 NA's :18 NA's :9 NA's :11 NA's :15
asymmetry_F8_F7 asymmetry_FC2_FC1
Min. :-0.30543 Min. :-0.441154
1st Qu.:-0.06689 1st Qu.:-0.052937
Median : 0.03141 Median : 0.000638
Mean : 0.05029 Mean :-0.008126
3rd Qu.: 0.12120 3rd Qu.: 0.061400
Max. : 0.61090 Max. : 0.238582
NA's :17 NA's :7 asymmetry_pairs <- c('asymmetry_Fp2_Fp1', 'asymmetry_AF4_AF3', 'asymmetry_AF8_AF7', 'asymmetry_F2_F1', 'asymmetry_F4_F3', 'asymmetry_F6_F5', 'asymmetry_F8_F7', 'asymmetry_FC2_FC1')
asymmetry_pairs_pairs <- ggpairs(alpha_asymmetry_data,
columns = asymmetry_pairs,
aes(colour = sex, alpha = .25),
progress = FALSE,
lower = list(continuous = wrap("points")))
suppressWarnings(print(asymmetry_pairs_pairs))
4 Alpha Asymmetry
4.1 Fp2-Fp1 pair
options(width = 100)
alpha_asymmetry_rep_anova = aov_ez("Subject", "asymmetry_Fp2_Fp1", alpha_asymmetry_data, between = c("belief", "sex"))Warning: Missing values for following ID(s):
3, 10, 16, 24, 29, 33, 41, 62, 64, 74, 77, 81, 83, 107, 115
Removing those cases from the analysis.Contrasts set to contr.sum for the following variables: belief, sexmytable <- xtabs(~ sex + belief, data = alpha_asymmetry_rep_anova$data$long)
ftable(addmargins(mytable)) belief Believer Unbeliever Sum
sex
Female 18 22 40
Male 12 13 25
Sum 30 35 65alpha_asymmetry_rain <- ggplot(alpha_asymmetry_rep_anova$data$long, aes(y = asymmetry_Fp2_Fp1, x = belief, color = sex, fill = sex)) +
# ggtitle("alpha_asymmetry") +
ylab("power") +
stat_halfeye(
trim = FALSE,
adjust = 1,
.width = 0,
justification = -.15,
alpha = .5,
point_colour = NA) +
# theme(legend.position='none')
# geom_boxplot(width = .15, alpha = .2, outlier.shape = NA) +
geom_point(size = 2, alpha = .4, position = position_jitter(width = .05, height = 0))
suppressWarnings(print(alpha_asymmetry_rain))
alpha_asymmetry_afex_plot <-
afex_plot(
alpha_asymmetry_rep_anova,
x = "belief",
trace = "sex",
error = "between",
error_arg = list(width = .1),
dodge = -.5,
mapping = c("color"),
point_arg = list(size = 4)
)
suppressWarnings(print(alpha_asymmetry_afex_plot))
nice(alpha_asymmetry_rep_anova)Anova Table (Type 3 tests)
Response: asymmetry_Fp2_Fp1
Effect df MSE F ges p.value
1 belief 1, 61 0.01 0.09 .002 .759
2 sex 1, 61 0.01 0.01 <.001 .914
3 belief:sex 1, 61 0.01 0.01 <.001 .911
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘+’ 0.1 ‘ ’ 1a_posteriori(alpha_asymmetry_rep_anova)4.2 AF4-AF3 pair
options(width = 100)
alpha_asymmetry_rep_anova = aov_ez("Subject", "asymmetry_AF4_AF3", alpha_asymmetry_data, between = c("belief", "sex"))Warning: Missing values for following ID(s):
3, 16, 20, 24, 29, 31, 33, 41, 62, 64, 74, 77, 81, 83, 107, 115
Removing those cases from the analysis.Contrasts set to contr.sum for the following variables: belief, sexmytable <- xtabs(~ sex + belief, data = alpha_asymmetry_rep_anova$data$long)
ftable(addmargins(mytable)) belief Believer Unbeliever Sum
sex
Female 17 22 39
Male 13 12 25
Sum 30 34 64alpha_asymmetry_rain <- ggplot(alpha_asymmetry_rep_anova$data$long, aes(y = asymmetry_AF4_AF3, x = belief, color = sex, fill = sex)) +
# ggtitle("alpha_asymmetry") +
ylab("power") +
stat_halfeye(
trim = FALSE,
adjust = 1,
.width = 0,
justification = -.15,
alpha = .5,
point_colour = NA) +
# theme(legend.position='none')
# geom_boxplot(width = .15, alpha = .2, outlier.shape = NA) +
geom_point(size = 2, alpha = .4, position = position_jitter(width = .05, height = 0))
suppressWarnings(print(alpha_asymmetry_rain))
alpha_asymmetry_afex_plot <-
afex_plot(
alpha_asymmetry_rep_anova,
x = "belief",
trace = "sex",
error = "between",
error_arg = list(width = .1),
dodge = -.5,
mapping = c("color"),
point_arg = list(size = 4)
)
suppressWarnings(print(alpha_asymmetry_afex_plot))
nice(alpha_asymmetry_rep_anova)Anova Table (Type 3 tests)
Response: asymmetry_AF4_AF3
Effect df MSE F ges p.value
1 belief 1, 60 0.02 0.04 <.001 .850
2 sex 1, 60 0.02 0.01 <.001 .907
3 belief:sex 1, 60 0.02 0.02 <.001 .876
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘+’ 0.1 ‘ ’ 1a_posteriori(alpha_asymmetry_rep_anova)4.3 AF8-AF7 pair
options(width = 100)
alpha_asymmetry_rep_anova = aov_ez("Subject", "asymmetry_AF8_AF7", alpha_asymmetry_data, between = c("belief", "sex"))Warning: Missing values for following ID(s):
3, 10, 16, 24, 29, 33, 41, 60, 62, 64, 68, 74, 77, 81, 83, 98, 107, 115
Removing those cases from the analysis.Contrasts set to contr.sum for the following variables: belief, sexmytable <- xtabs(~ sex + belief, data = alpha_asymmetry_rep_anova$data$long)
ftable(addmargins(mytable)) belief Believer Unbeliever Sum
sex
Female 18 21 39
Male 11 12 23
Sum 29 33 62alpha_asymmetry_rain <- ggplot(alpha_asymmetry_rep_anova$data$long, aes(y = asymmetry_AF8_AF7, x = belief, color = sex, fill = sex)) +
# ggtitle("alpha_asymmetry") +
ylab("power") +
stat_halfeye(
trim = FALSE,
adjust = 1,
.width = 0,
justification = -.15,
alpha = .5,
point_colour = NA) +
# theme(legend.position='none')
# geom_boxplot(width = .15, alpha = .2, outlier.shape = NA) +
geom_point(size = 2, alpha = .4, position = position_jitter(width = .05, height = 0))
suppressWarnings(print(alpha_asymmetry_rain))alpha_asymmetry_afex_plot <-
afex_plot(
alpha_asymmetry_rep_anova,
x = "belief",
trace = "sex",
error = "between",
error_arg = list(width = .1),
dodge = -.5,
mapping = c("color"),
point_arg = list(size = 4)
)
suppressWarnings(print(alpha_asymmetry_afex_plot))
nice(alpha_asymmetry_rep_anova)Anova Table (Type 3 tests)
Response: asymmetry_AF8_AF7
Effect df MSE F ges p.value
1 belief 1, 58 0.03 0.01 <.001 .904
2 sex 1, 58 0.03 0.09 .001 .770
3 belief:sex 1, 58 0.03 0.11 .002 .740
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘+’ 0.1 ‘ ’ 1a_posteriori(alpha_asymmetry_rep_anova)4.4 F2-F1 pair
nice(alpha_asymmetry_rep_anova)Anova Table (Type 3 tests)
Response: asymmetry_F2_F1
Effect df MSE F ges p.value
1 belief 1, 67 0.01 0.13 .002 .719
2 sex 1, 67 0.01 0.63 .009 .430
3 belief:sex 1, 67 0.01 0.90 .013 .347
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘+’ 0.1 ‘ ’ 1a_posteriori(alpha_asymmetry_rep_anova)
alpha_asymmetry_afex_plot <-
afex_plot(
alpha_asymmetry_rep_anova,
x = "belief",
trace = "sex",
error = "between",
error_arg = list(width = .1),
dodge = -.5,
mapping = c("color"),
point_arg = list(size = 4)
)
suppressWarnings(print(alpha_asymmetry_afex_plot))options(width = 100)
alpha_asymmetry_rep_anova = aov_ez("Subject", "asymmetry_F2_F1", alpha_asymmetry_data, between = c("belief", "sex"))Warning: Missing values for following ID(s):
24, 29, 33, 43, 64, 74, 77, 81, 107
Removing those cases from the analysis.Contrasts set to contr.sum for the following variables: belief, sexmytable <- xtabs(~ sex + belief, data = alpha_asymmetry_rep_anova$data$long)
ftable(addmargins(mytable)) belief Believer Unbeliever Sum
sex
Female 20 23 43
Male 14 14 28
Sum 34 37 71alpha_asymmetry_rain <- ggplot(alpha_asymmetry_rep_anova$data$long, aes(y = asymmetry_F2_F1, x = belief, color = sex, fill = sex)) +
# ggtitle("alpha_asymmetry") +
ylab("power") +
stat_halfeye(
trim = FALSE,
adjust = 1,
.width = 0,
justification = -.15,
alpha = .5,
point_colour = NA) +
# theme(legend.position='none')
# geom_boxplot(width = .15, alpha = .2, outlier.shape = NA) +
geom_point(size = 2, alpha = .4, position = position_jitter(width = .05, height = 0))
suppressWarnings(print(alpha_asymmetry_rain))alpha_asymmetry_afex_plot <-
afex_plot(
alpha_asymmetry_rep_anova,
x = "belief",
trace = "sex",
error = "between",
error_arg = list(width = .1),
dodge = -.5,
mapping = c("color"),
point_arg = list(size = 4)
)
suppressWarnings(print(alpha_asymmetry_afex_plot))
4.5 F4-F3 pair
options(width = 100)
alpha_asymmetry_rep_anova = aov_ez("Subject", "asymmetry_F4_F3", alpha_asymmetry_data, between = c("belief", "sex"))Warning: Missing values for following ID(s):
24, 29, 33, 37, 43, 55, 62, 64, 77, 98, 107
Removing those cases from the analysis.Contrasts set to contr.sum for the following variables: belief, sexmytable <- xtabs(~ sex + belief, data = alpha_asymmetry_rep_anova$data$long)
ftable(addmargins(mytable)) belief Believer Unbeliever Sum
sex
Female 21 22 43
Male 14 12 26
Sum 35 34 69alpha_asymmetry_rain <- ggplot(alpha_asymmetry_rep_anova$data$long, aes(y = asymmetry_F4_F3, x = belief, color = sex, fill = sex)) +
# ggtitle("alpha_asymmetry") +
ylab("power") +
stat_halfeye(
trim = FALSE,
adjust = 1,
.width = 0,
justification = -.15,
alpha = .5,
point_colour = NA) +
# theme(legend.position='none')
# geom_boxplot(width = .15, alpha = .2, outlier.shape = NA) +
geom_point(size = 2, alpha = .4, position = position_jitter(width = .05, height = 0))
suppressWarnings(print(alpha_asymmetry_rain))
alpha_asymmetry_afex_plot <-
afex_plot(
alpha_asymmetry_rep_anova,
x = "belief",
trace = "sex",
error = "between",
error_arg = list(width = .1),
dodge = -.5,
mapping = c("color"),
point_arg = list(size = 4)
)
suppressWarnings(print(alpha_asymmetry_afex_plot))
nice(alpha_asymmetry_rep_anova)Anova Table (Type 3 tests)
Response: asymmetry_F4_F3
Effect df MSE F ges p.value
1 belief 1, 65 0.02 0.04 <.001 .837
2 sex 1, 65 0.02 0.71 .011 .404
3 belief:sex 1, 65 0.02 0.30 .005 .585
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘+’ 0.1 ‘ ’ 1a_posteriori(alpha_asymmetry_rep_anova)4.6 F6-F5 pair
options(width = 100)
alpha_asymmetry_rep_anova = aov_ez("Subject", "asymmetry_F6_F5", alpha_asymmetry_data, between = c("belief", "sex"))Warning: Missing values for following ID(s):
3, 24, 29, 33, 37, 43, 55, 62, 64, 74, 77, 81, 83, 107, 115
Removing those cases from the analysis.Contrasts set to contr.sum for the following variables: belief, sexmytable <- xtabs(~ sex + belief, data = alpha_asymmetry_rep_anova$data$long)
ftable(addmargins(mytable)) belief Believer Unbeliever Sum
sex
Female 19 21 40
Male 12 13 25
Sum 31 34 65alpha_asymmetry_rain <- ggplot(alpha_asymmetry_rep_anova$data$long, aes(y = asymmetry_F6_F5, x = belief, color = sex, fill = sex)) +
# ggtitle("alpha_asymmetry") +
ylab("power") +
stat_halfeye(
trim = FALSE,
adjust = 1,
.width = 0,
justification = -.15,
alpha = .5,
point_colour = NA) +
# theme(legend.position='none')
# geom_boxplot(width = .15, alpha = .2, outlier.shape = NA) +
geom_point(size = 2, alpha = .4, position = position_jitter(width = .05, height = 0))
suppressWarnings(print(alpha_asymmetry_rain))
alpha_asymmetry_afex_plot <-
afex_plot(
alpha_asymmetry_rep_anova,
x = "belief",
trace = "sex",
error = "between",
error_arg = list(width = .1),
dodge = -.5,
mapping = c("color"),
point_arg = list(size = 4)
)
suppressWarnings(print(alpha_asymmetry_afex_plot))
nice(alpha_asymmetry_rep_anova)Anova Table (Type 3 tests)
Response: asymmetry_F6_F5
Effect df MSE F ges p.value
1 belief 1, 61 0.03 0.04 <.001 .840
2 sex 1, 61 0.03 0.01 <.001 .944
3 belief:sex 1, 61 0.03 1.21 .020 .275
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘+’ 0.1 ‘ ’ 1a_posteriori(alpha_asymmetry_rep_anova)4.7 F8-F7 pair
options(width = 100)
alpha_asymmetry_rep_anova = aov_ez("Subject", "asymmetry_F8_F7", alpha_asymmetry_data, between = c("belief", "sex"))Warning: Missing values for following ID(s):
3, 10, 16, 24, 29, 33, 37, 60, 62, 63, 68, 74, 77, 81, 83, 107, 115
Removing those cases from the analysis.Contrasts set to contr.sum for the following variables: belief, sexmytable <- xtabs(~ sex + belief, data = alpha_asymmetry_rep_anova$data$long)
ftable(addmargins(mytable)) belief Believer Unbeliever Sum
sex
Female 18 21 39
Male 11 13 24
Sum 29 34 63alpha_asymmetry_rain <- ggplot(alpha_asymmetry_rep_anova$data$long, aes(y = asymmetry_F8_F7, x = belief, color = sex, fill = sex)) +
# ggtitle("alpha_asymmetry") +
ylab("power") +
stat_halfeye(
trim = FALSE,
adjust = 1,
.width = 0,
justification = -.15,
alpha = .5,
point_colour = NA) +
# theme(legend.position='none')
# geom_boxplot(width = .15, alpha = .2, outlier.shape = NA) +
geom_point(size = 2, alpha = .4, position = position_jitter(width = .05, height = 0))
suppressWarnings(print(alpha_asymmetry_rain))
alpha_asymmetry_afex_plot <-
afex_plot(
alpha_asymmetry_rep_anova,
x = "belief",
trace = "sex",
error = "between",
error_arg = list(width = .1),
dodge = -.5,
mapping = c("color"),
point_arg = list(size = 4)
)
suppressWarnings(print(alpha_asymmetry_afex_plot))
nice(alpha_asymmetry_rep_anova)Anova Table (Type 3 tests)
Response: asymmetry_F8_F7
Effect df MSE F ges p.value
1 belief 1, 59 0.03 0.04 <.001 .837
2 sex 1, 59 0.03 0.86 .014 .359
3 belief:sex 1, 59 0.03 0.00 <.001 .958
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘+’ 0.1 ‘ ’ 1a_posteriori(alpha_asymmetry_rep_anova)4.8 FC2-FC1 pair
options(width = 100)
alpha_asymmetry_rep_anova = aov_ez("Subject", "asymmetry_FC2_FC1", alpha_asymmetry_data, between = c("belief", "sex"))Warning: Missing values for following ID(s):
24, 29, 33, 43, 63, 77, 107
Removing those cases from the analysis.Contrasts set to contr.sum for the following variables: belief, sexmytable <- xtabs(~ sex + belief, data = alpha_asymmetry_rep_anova$data$long)
ftable(addmargins(mytable)) belief Believer Unbeliever Sum
sex
Female 21 23 44
Male 15 14 29
Sum 36 37 73alpha_asymmetry_rain <- ggplot(alpha_asymmetry_rep_anova$data$long, aes(y = asymmetry_FC2_FC1, x = belief, color = sex, fill = sex)) +
# ggtitle("alpha_asymmetry") +
ylab("power") +
stat_halfeye(
trim = FALSE,
adjust = 1,
.width = 0,
justification = -.15,
alpha = .5,
point_colour = NA) +
# theme(legend.position='none')
# geom_boxplot(width = .15, alpha = .2, outlier.shape = NA) +
geom_point(size = 2, alpha = .4, position = position_jitter(width = .05, height = 0))
suppressWarnings(print(alpha_asymmetry_rain))
alpha_asymmetry_afex_plot <-
afex_plot(
alpha_asymmetry_rep_anova,
x = "belief",
trace = "sex",
error = "between",
error_arg = list(width = .1),
dodge = -.5,
mapping = c("color"),
point_arg = list(size = 4)
)
suppressWarnings(print(alpha_asymmetry_afex_plot))
nice(alpha_asymmetry_rep_anova)Anova Table (Type 3 tests)
Response: asymmetry_FC2_FC1
Effect df MSE F ges p.value
1 belief 1, 69 0.01 0.07 .001 .787
2 sex 1, 69 0.01 0.03 <.001 .855
3 belief:sex 1, 69 0.01 0.06 <.001 .811
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘+’ 0.1 ‘ ’ 1a_posteriori(alpha_asymmetry_rep_anova)5 Alpha peak parameters and aperiodic activity
options(width = 100)
summary(alpha_power_data) Dataset Subject Electrode Frequency
S001VulCrF20957113_psd.csv : 16 1 : 16 AF7 : 80 Min. : 7.038
S003nVulCrM18466555_psd.csv : 16 3 : 16 AF8 : 80 1st Qu.: 9.186
S006nVulCrM17923449_psd.csv : 16 6 : 16 E068-F4a : 80 Median :10.439
S010nVulCrM193177867_psd.csv: 16 10 : 16 E069-F6a : 80 Mean :10.148
S015nVulCrM18833005_psd.csv : 16 15 : 16 E075-FC2a: 80 3rd Qu.:10.998
S016VulCrF19423156_psd.csv : 16 16 : 16 E076-F2a : 80 Max. :12.817
(Other) :1184 (Other):1184 (Other) :800 NA's :154
Amplitude Width Offset Slope r_squared
Min. :0.03165 Min. :1.000 Min. :-0.8527 Min. :-0.4634 Min. :0.1005
1st Qu.:0.36521 1st Qu.:1.910 1st Qu.: 0.7246 1st Qu.: 0.9929 1st Qu.:0.9624
Median :0.61276 Median :2.465 Median : 1.0366 Median : 1.2708 Median :0.9866
Mean :0.64807 Mean :2.570 Mean : 1.0670 Mean : 1.2576 Mean :0.9572
3rd Qu.:0.91668 3rd Qu.:3.118 3rd Qu.: 1.3956 3rd Qu.: 1.5131 3rd Qu.:0.9942
Max. :1.59599 Max. :5.000 Max. : 3.1870 Max. : 2.6788 Max. :0.9990
NA's :154 NA's :154
error n_peaks f_inf f_sup alpha_inf alpha_sup
Min. :0.01270 Min. :0.000 Min. :2 Min. :48 Min. :7 Min. :13
1st Qu.:0.03031 1st Qu.:2.000 1st Qu.:2 1st Qu.:48 1st Qu.:7 1st Qu.:13
Median :0.04179 Median :3.000 Median :2 Median :48 Median :7 Median :13
Mean :0.05024 Mean :2.754 Mean :2 Mean :48 Mean :7 Mean :13
3rd Qu.:0.05967 3rd Qu.:4.000 3rd Qu.:2 3rd Qu.:48 3rd Qu.:7 3rd Qu.:13
Max. :0.24311 Max. :4.000 Max. :2 Max. :48 Max. :7 Max. :13
vulnerability belief sex hemisphere electrode_pair
Invulnerable:1056 Believer :656 Female:768 Left :640 Fp1-Fp2:160
Vulnerable : 224 Unbeliever:624 Male :512 Right:640 AF3-AF4:160
AF7-AF8:160
F1-F2 :160
F3-F4 :160
F5-F6 :160
(Other):320 spec_params <- c('Amplitude', 'Frequency', 'Width', 'Offset', 'Slope')
spec_params_pairs <- ggpairs(alpha_power_data,
columns = spec_params,
aes(colour = vulnerability, alpha = .25),
progress = FALSE,
lower = list(continuous = wrap("points")))
suppressWarnings(print(spec_params_pairs))
5.1 Alpha Power
options(width = 100)
alpha_param_rep_anova = aov_ez("Subject", "Amplitude", alpha_power_data, between = c("belief", "sex"), within = c("hemisphere", "electrode_pair"))Warning: Missing values for following ID(s):
3, 10, 16, 20, 24, 29, 31, 33, 37, 41, 43, 55, 60, 62, 63, 64, 68, 74, 77, 81, 83, 98, 107, 115
Removing those cases from the analysis.Contrasts set to contr.sum for the following variables: belief, sexmytable <- xtabs(~ sex + belief, data = alpha_param_rep_anova$data$long) / length(levels(alpha_param_rep_anova$data$long$electrode_pair)) / length(levels(alpha_param_rep_anova$data$long$hemisphere))
ftable(addmargins(mytable)) belief Believer Unbeliever Sum
sex
Female 17 20 37
Male 9 10 19
Sum 26 30 56alpha_param_rain <- ggplot(alpha_param_rep_anova$data$long, aes(y = Amplitude, x = belief, color = sex, fill = sex)) +
stat_halfeye(
trim = FALSE,
adjust = 1,
.width = 0,
justification = -.15,
alpha = .5,
point_colour = NA) +
geom_point(size = 2, alpha = .4, position = position_jitter(width = .05, height = 0))
suppressWarnings(print(alpha_param_rain))
alpha_param_afex_plot <-
afex_plot(
alpha_param_rep_anova,
x = "belief",
trace = "sex",
panel = "hemisphere",
error = "between",
error_arg = list(width = .1),
dodge = -.5,
mapping = c("color"),
point_arg = list(size = 4)
)
suppressWarnings(print(alpha_param_afex_plot))
nice(alpha_param_rep_anova)Anova Table (Type 3 tests)
Response: Amplitude
Effect df MSE F ges p.value
1 belief 1, 52 1.27 0.34 .006 .564
2 sex 1, 52 1.27 0.03 <.001 .864
3 belief:sex 1, 52 1.27 0.70 .012 .408
4 hemisphere 1, 52 0.04 2.81 + .002 .100
5 belief:hemisphere 1, 52 0.04 0.00 <.001 .958
6 sex:hemisphere 1, 52 0.04 0.05 <.001 .821
7 belief:sex:hemisphere 1, 52 0.04 0.05 <.001 .831
8 electrode_pair 3.66, 190.21 0.02 31.79 *** .032 <.001
9 belief:electrode_pair 3.66, 190.21 0.02 1.77 .002 .143
10 sex:electrode_pair 3.66, 190.21 0.02 1.78 .002 .140
11 belief:sex:electrode_pair 3.66, 190.21 0.02 0.33 <.001 .845
12 hemisphere:electrode_pair 4.22, 219.69 0.01 3.59 ** .002 .006
13 belief:hemisphere:electrode_pair 4.22, 219.69 0.01 0.11 <.001 .982
14 sex:hemisphere:electrode_pair 4.22, 219.69 0.01 0.53 <.001 .720
15 belief:sex:hemisphere:electrode_pair 4.22, 219.69 0.01 0.39 <.001 .827
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘+’ 0.1 ‘ ’ 1
Sphericity correction method: GG a_posteriori(alpha_param_rep_anova)$emmeans
electrode_pair emmean SE df lower.CL upper.CL
Fp1.Fp2 0.698 0.0405 52 0.617 0.780
AF3.AF4 0.727 0.0405 52 0.646 0.808
AF7.AF8 0.668 0.0388 52 0.590 0.746
F1.F2 0.794 0.0414 52 0.711 0.877
F3.F4 0.753 0.0424 52 0.668 0.838
F5.F6 0.704 0.0409 52 0.622 0.786
F7.F8 0.666 0.0399 52 0.586 0.746
FC1.FC2 0.831 0.0430 52 0.744 0.917
Results are averaged over the levels of: belief, sex, hemisphere
Confidence level used: 0.95
$contrasts
contrast estimate SE df t.ratio p.value
Fp1.Fp2 - AF3.AF4 -0.02871 0.00675 52 -4.254 0.0021
Fp1.Fp2 - AF7.AF8 0.03016 0.01170 52 2.578 0.1876
Fp1.Fp2 - F1.F2 -0.09547 0.01489 52 -6.411 <.0001
Fp1.Fp2 - F3.F4 -0.05509 0.01746 52 -3.156 0.0502
Fp1.Fp2 - F5.F6 -0.00589 0.01632 52 -0.361 1.0000
Fp1.Fp2 - F7.F8 0.03219 0.01535 52 2.098 0.4300
Fp1.Fp2 - FC1.FC2 -0.13214 0.01878 52 -7.038 <.0001
AF3.AF4 - AF7.AF8 0.05887 0.01403 52 4.197 0.0025
AF3.AF4 - F1.F2 -0.06676 0.01207 52 -5.529 <.0001
AF3.AF4 - F3.F4 -0.02638 0.01626 52 -1.622 0.7348
AF3.AF4 - F5.F6 0.02282 0.01540 52 1.481 0.8137
AF3.AF4 - F7.F8 0.06090 0.01516 52 4.017 0.0044
AF3.AF4 - FC1.FC2 -0.10343 0.01594 52 -6.487 <.0001
AF7.AF8 - F1.F2 -0.12563 0.01713 52 -7.334 <.0001
AF7.AF8 - F3.F4 -0.08525 0.01498 52 -5.689 <.0001
AF7.AF8 - F5.F6 -0.03605 0.01436 52 -2.512 0.2136
AF7.AF8 - F7.F8 0.00203 0.01292 52 0.157 1.0000
AF7.AF8 - FC1.FC2 -0.16230 0.01931 52 -8.403 <.0001
F1.F2 - F3.F4 0.04038 0.01230 52 3.282 0.0362
F1.F2 - F5.F6 0.08958 0.01649 52 5.432 <.0001
F1.F2 - F7.F8 0.12766 0.01649 52 7.743 <.0001
F1.F2 - FC1.FC2 -0.03667 0.00874 52 -4.196 0.0025
F3.F4 - F5.F6 0.04920 0.01166 52 4.221 0.0023
F3.F4 - F7.F8 0.08728 0.01409 52 6.193 <.0001
F3.F4 - FC1.FC2 -0.07705 0.01334 52 -5.777 <.0001
F5.F6 - F7.F8 0.03808 0.01021 52 3.731 0.0104
F5.F6 - FC1.FC2 -0.12625 0.01690 52 -7.471 <.0001
F7.F8 - FC1.FC2 -0.16433 0.01683 52 -9.766 <.0001
Results are averaged over the levels of: belief, sex, hemisphere
P value adjustment: tukey method for comparing a family of 8 estimates
____________________________________________________________________________________________________
$emmeans
hemisphere electrode_pair emmean SE df lower.CL upper.CL
Left Fp1.Fp2 0.700 0.0405 52 0.619 0.781
Right Fp1.Fp2 0.697 0.0424 52 0.612 0.782
Left AF3.AF4 0.725 0.0411 52 0.643 0.808
Right AF3.AF4 0.729 0.0418 52 0.645 0.813
Left AF7.AF8 0.648 0.0385 52 0.571 0.726
Right AF7.AF8 0.688 0.0428 52 0.602 0.774
Left F1.F2 0.795 0.0426 52 0.709 0.880
Right F1.F2 0.793 0.0414 52 0.710 0.876
Left F3.F4 0.741 0.0454 52 0.650 0.832
Right F3.F4 0.766 0.0424 52 0.681 0.851
Left F5.F6 0.671 0.0427 52 0.586 0.757
Right F5.F6 0.737 0.0427 52 0.651 0.823
Left F7.F8 0.632 0.0406 52 0.551 0.714
Right F7.F8 0.700 0.0432 52 0.614 0.787
Left FC1.FC2 0.835 0.0441 52 0.746 0.923
Right FC1.FC2 0.826 0.0436 52 0.739 0.914
Results are averaged over the levels of: belief, sex
Confidence level used: 0.95
$contrasts
contrast estimate SE df t.ratio p.value
Left Fp1.Fp2 - Right Fp1.Fp2 0.002810 0.01741 52 0.161 1.0000
Left Fp1.Fp2 - Left AF3.AF4 -0.025639 0.00959 52 -2.674 0.3740
Left Fp1.Fp2 - Right AF3.AF4 -0.028965 0.01695 52 -1.708 0.9351
Left Fp1.Fp2 - Left AF7.AF8 0.051510 0.01688 52 3.052 0.1838
Left Fp1.Fp2 - Right AF7.AF8 0.011629 0.02113 52 0.550 1.0000
Left Fp1.Fp2 - Left F1.F2 -0.094748 0.01690 52 -5.607 0.0001
Left Fp1.Fp2 - Right F1.F2 -0.093373 0.01920 52 -4.863 0.0011
Left Fp1.Fp2 - Left F3.F4 -0.040856 0.02299 52 -1.777 0.9135
Left Fp1.Fp2 - Right F3.F4 -0.066514 0.02071 52 -3.212 0.1294
Left Fp1.Fp2 - Left F5.F6 0.028380 0.02192 52 1.295 0.9944
Left Fp1.Fp2 - Right F5.F6 -0.037349 0.02051 52 -1.821 0.8975
Left Fp1.Fp2 - Left F7.F8 0.067756 0.02091 52 3.241 0.1211
Left Fp1.Fp2 - Right F7.F8 -0.000561 0.02157 52 -0.026 1.0000
Left Fp1.Fp2 - Left FC1.FC2 -0.134957 0.02166 52 -6.230 <.0001
Left Fp1.Fp2 - Right FC1.FC2 -0.126512 0.02213 52 -5.718 0.0001
Right Fp1.Fp2 - Left AF3.AF4 -0.028449 0.01754 52 -1.622 0.9566
Right Fp1.Fp2 - Right AF3.AF4 -0.031775 0.01000 52 -3.177 0.1401
Right Fp1.Fp2 - Left AF7.AF8 0.048700 0.02375 52 2.050 0.7861
Right Fp1.Fp2 - Right AF7.AF8 0.008819 0.01336 52 0.660 1.0000
Right Fp1.Fp2 - Left F1.F2 -0.097558 0.01968 52 -4.957 0.0008
Right Fp1.Fp2 - Right F1.F2 -0.096183 0.01872 52 -5.137 0.0004
Right Fp1.Fp2 - Left F3.F4 -0.043666 0.02657 52 -1.643 0.9519
Right Fp1.Fp2 - Right F3.F4 -0.069324 0.01976 52 -3.508 0.0632
Right Fp1.Fp2 - Left F5.F6 0.025570 0.02549 52 1.003 0.9997
Right Fp1.Fp2 - Right F5.F6 -0.040159 0.02015 52 -1.993 0.8178
Right Fp1.Fp2 - Left F7.F8 0.064946 0.02369 52 2.741 0.3341
Right Fp1.Fp2 - Right F7.F8 -0.003371 0.02109 52 -0.160 1.0000
Right Fp1.Fp2 - Left FC1.FC2 -0.137767 0.02358 52 -5.842 <.0001
Right Fp1.Fp2 - Right FC1.FC2 -0.129322 0.02217 52 -5.834 <.0001
Left AF3.AF4 - Right AF3.AF4 -0.003326 0.01737 52 -0.191 1.0000
Left AF3.AF4 - Left AF7.AF8 0.077149 0.02061 52 3.743 0.0339
Left AF3.AF4 - Right AF7.AF8 0.037269 0.02123 52 1.755 0.9208
Left AF3.AF4 - Left F1.F2 -0.069109 0.01425 52 -4.850 0.0011
Left AF3.AF4 - Right F1.F2 -0.067734 0.01953 52 -3.468 0.0699
Left AF3.AF4 - Left F3.F4 -0.015217 0.02142 52 -0.710 1.0000
Left AF3.AF4 - Right F3.F4 -0.040875 0.01993 52 -2.051 0.7857
Left AF3.AF4 - Left F5.F6 0.054019 0.02099 52 2.573 0.4382
Left AF3.AF4 - Right F5.F6 -0.011710 0.01888 52 -0.620 1.0000
Left AF3.AF4 - Left F7.F8 0.093395 0.02193 52 4.258 0.0076
Left AF3.AF4 - Right F7.F8 0.025078 0.02052 52 1.222 0.9969
Left AF3.AF4 - Left FC1.FC2 -0.109318 0.01903 52 -5.743 0.0001
Left AF3.AF4 - Right FC1.FC2 -0.100872 0.02096 52 -4.812 0.0013
Right AF3.AF4 - Left AF7.AF8 0.080475 0.02428 52 3.314 0.1019
Right AF3.AF4 - Right AF7.AF8 0.040594 0.01570 52 2.585 0.4304
Right AF3.AF4 - Left F1.F2 -0.065783 0.01644 52 -4.000 0.0163
Right AF3.AF4 - Right F1.F2 -0.064408 0.01523 52 -4.229 0.0083
Right AF3.AF4 - Left F3.F4 -0.011891 0.02710 52 -0.439 1.0000
Right AF3.AF4 - Right F3.F4 -0.037550 0.01735 52 -2.164 0.7162
Right AF3.AF4 - Left F5.F6 0.057345 0.02599 52 2.207 0.6885
Right AF3.AF4 - Right F5.F6 -0.008384 0.01916 52 -0.438 1.0000
Right AF3.AF4 - Left F7.F8 0.096721 0.02402 52 4.026 0.0152
Right AF3.AF4 - Right F7.F8 0.028403 0.02013 52 1.411 0.9871
Right AF3.AF4 - Left FC1.FC2 -0.105993 0.02205 52 -4.806 0.0013
Right AF3.AF4 - Right FC1.FC2 -0.097547 0.01800 52 -5.420 0.0002
Left AF7.AF8 - Right AF7.AF8 -0.039880 0.02496 52 -1.598 0.9617
Left AF7.AF8 - Left F1.F2 -0.146258 0.02295 52 -6.372 <.0001
Left AF7.AF8 - Right F1.F2 -0.144883 0.02471 52 -5.864 <.0001
Left AF7.AF8 - Left F3.F4 -0.092366 0.02239 52 -4.126 0.0113
Left AF7.AF8 - Right F3.F4 -0.118024 0.02412 52 -4.893 0.0010
Left AF7.AF8 - Left F5.F6 -0.023130 0.02241 52 -1.032 0.9995
Left AF7.AF8 - Right F5.F6 -0.088859 0.02495 52 -3.562 0.0550
Left AF7.AF8 - Left F7.F8 0.016246 0.01989 52 0.817 1.0000
Left AF7.AF8 - Right F7.F8 -0.052071 0.02511 52 -2.074 0.7723
Left AF7.AF8 - Left FC1.FC2 -0.186467 0.02476 52 -7.531 <.0001
Left AF7.AF8 - Right FC1.FC2 -0.178022 0.02712 52 -6.564 <.0001
Right AF7.AF8 - Left F1.F2 -0.106377 0.02174 52 -4.893 0.0010
Right AF7.AF8 - Right F1.F2 -0.105002 0.01972 52 -5.325 0.0002
Right AF7.AF8 - Left F3.F4 -0.052486 0.02602 52 -2.017 0.8048
Right AF7.AF8 - Right F3.F4 -0.078144 0.01713 52 -4.562 0.0029
Right AF7.AF8 - Left F5.F6 0.016751 0.02477 52 0.676 1.0000
Right AF7.AF8 - Right F5.F6 -0.048978 0.01726 52 -2.838 0.2808
Right AF7.AF8 - Left F7.F8 0.056126 0.02520 52 2.227 0.6748
Right AF7.AF8 - Right F7.F8 -0.012191 0.01711 52 -0.712 1.0000
Right AF7.AF8 - Left FC1.FC2 -0.146587 0.02364 52 -6.200 <.0001
Right AF7.AF8 - Right FC1.FC2 -0.138141 0.02238 52 -6.172 <.0001
Left F1.F2 - Right F1.F2 0.001375 0.01421 52 0.097 1.0000
Left F1.F2 - Left F3.F4 0.053892 0.01910 52 2.821 0.2896
Left F1.F2 - Right F3.F4 0.028234 0.01649 52 1.712 0.9341
Left F1.F2 - Left F5.F6 0.123128 0.02223 52 5.540 0.0001
Left F1.F2 - Right F5.F6 0.057399 0.02027 52 2.832 0.2838
Left F1.F2 - Left F7.F8 0.162504 0.02136 52 7.606 <.0001
Left F1.F2 - Right F7.F8 0.094187 0.02217 52 4.249 0.0078
Left F1.F2 - Left FC1.FC2 -0.040210 0.01274 52 -3.155 0.1471
Left F1.F2 - Right FC1.FC2 -0.031764 0.01559 52 -2.037 0.7935
Right F1.F2 - Left F3.F4 0.052517 0.02334 52 2.250 0.6594
Right F1.F2 - Right F3.F4 0.026859 0.01257 52 2.137 0.7335
Right F1.F2 - Left F5.F6 0.121753 0.02562 52 4.752 0.0016
Right F1.F2 - Right F5.F6 0.056024 0.01782 52 3.144 0.1507
Right F1.F2 - Left F7.F8 0.161129 0.02389 52 6.745 <.0001
Right F1.F2 - Right F7.F8 0.092812 0.02084 52 4.454 0.0041
Right F1.F2 - Left FC1.FC2 -0.041584 0.01656 52 -2.511 0.4795
Right F1.F2 - Right FC1.FC2 -0.033139 0.01100 52 -3.012 0.1995
Left F3.F4 - Right F3.F4 -0.025658 0.02307 52 -1.112 0.9989
Left F3.F4 - Left F5.F6 0.069236 0.01588 52 4.360 0.0055
Left F3.F4 - Right F5.F6 0.003507 0.02427 52 0.145 1.0000
Left F3.F4 - Left F7.F8 0.108612 0.01966 52 5.526 0.0001
Left F3.F4 - Right F7.F8 0.040295 0.02527 52 1.595 0.9623
Left F3.F4 - Left FC1.FC2 -0.094101 0.01778 52 -5.292 0.0003
Left F3.F4 - Right FC1.FC2 -0.085656 0.02529 52 -3.386 0.0856
Right F3.F4 - Left F5.F6 0.094894 0.02583 52 3.674 0.0409
Right F3.F4 - Right F5.F6 0.029165 0.01249 52 2.335 0.6012
Right F3.F4 - Left F7.F8 0.134270 0.02650 52 5.067 0.0005
Right F3.F4 - Right F7.F8 0.065953 0.01621 52 4.068 0.0134
Right F3.F4 - Left FC1.FC2 -0.068443 0.01906 52 -3.590 0.0510
Right F3.F4 - Right FC1.FC2 -0.059997 0.01475 52 -4.068 0.0134
Left F5.F6 - Right F5.F6 -0.065729 0.02426 52 -2.709 0.3528
Left F5.F6 - Left F7.F8 0.039376 0.01672 52 2.356 0.5868
Left F5.F6 - Right F7.F8 -0.028941 0.02383 52 -1.214 0.9971
Left F5.F6 - Left FC1.FC2 -0.163338 0.02232 52 -7.318 <.0001
Left F5.F6 - Right FC1.FC2 -0.154892 0.02722 52 -5.691 0.0001
Right F5.F6 - Left F7.F8 0.105105 0.02633 52 3.992 0.0167
Right F5.F6 - Right F7.F8 0.036788 0.01133 52 3.246 0.1196
Right F5.F6 - Left FC1.FC2 -0.097609 0.02060 52 -4.739 0.0016
Right F5.F6 - Right FC1.FC2 -0.089163 0.01900 52 -4.693 0.0019
Left F7.F8 - Right F7.F8 -0.068317 0.02576 52 -2.652 0.3875
Left F7.F8 - Left FC1.FC2 -0.202713 0.02077 52 -9.760 <.0001
Left F7.F8 - Right FC1.FC2 -0.194267 0.02602 52 -7.466 <.0001
Right F7.F8 - Left FC1.FC2 -0.134396 0.02150 52 -6.250 <.0001
Right F7.F8 - Right FC1.FC2 -0.125950 0.02277 52 -5.533 0.0001
Left FC1.FC2 - Right FC1.FC2 0.008446 0.01712 52 0.493 1.0000
Results are averaged over the levels of: belief, sex
P value adjustment: tukey method for comparing a family of 16 estimates
____________________________________________________________________________________________________5.2 Alpha Frequency
options(width = 100)
alpha_param_rep_anova = aov_ez("Subject", "Frequency", alpha_power_data, between = c("belief", "sex"), within = c("hemisphere", "electrode_pair"))Warning: Missing values for following ID(s):
3, 10, 16, 20, 24, 29, 31, 33, 37, 41, 43, 55, 60, 62, 63, 64, 68, 74, 77, 81, 83, 98, 107, 115
Removing those cases from the analysis.Contrasts set to contr.sum for the following variables: belief, sexmytable <- xtabs(~ sex + belief, data = alpha_param_rep_anova$data$long) / length(levels(alpha_param_rep_anova$data$long$electrode_pair)) / length(levels(alpha_param_rep_anova$data$long$hemisphere))
ftable(addmargins(mytable)) belief Believer Unbeliever Sum
sex
Female 17 20 37
Male 9 10 19
Sum 26 30 56alpha_param_rain <- ggplot(alpha_param_rep_anova$data$long, aes(y = Frequency, x = belief, color = sex, fill = sex)) +
stat_halfeye(
trim = FALSE,
adjust = 1,
.width = 0,
justification = -.15,
alpha = .5,
point_colour = NA) +
geom_point(size = 2, alpha = .4, position = position_jitter(width = .05, height = 0))
suppressWarnings(print(alpha_param_rain))
alpha_param_afex_plot <-
afex_plot(
alpha_param_rep_anova,
x = "belief",
trace = "sex",
panel = "hemisphere",
error = "between",
error_arg = list(width = .1),
dodge = -.5,
mapping = c("color"),
point_arg = list(size = 4)
)
suppressWarnings(print(alpha_param_afex_plot))
nice(alpha_param_rep_anova)Anova Table (Type 3 tests)
Response: Frequency
Effect df MSE F ges p.value
1 belief 1, 52 19.49 0.43 .007 .517
2 sex 1, 52 19.49 0.21 .004 .646
3 belief:sex 1, 52 19.49 0.39 .007 .534
4 hemisphere 1, 52 0.20 0.49 <.001 .488
5 belief:hemisphere 1, 52 0.20 0.75 <.001 .392
6 sex:hemisphere 1, 52 0.20 0.06 <.001 .803
7 belief:sex:hemisphere 1, 52 0.20 2.06 <.001 .157
8 electrode_pair 3.51, 182.77 0.26 1.54 .001 .198
9 belief:electrode_pair 3.51, 182.77 0.26 0.55 <.001 .675
10 sex:electrode_pair 3.51, 182.77 0.26 0.45 <.001 .746
11 belief:sex:electrode_pair 3.51, 182.77 0.26 1.06 <.001 .372
12 hemisphere:electrode_pair 2.58, 134.13 0.30 1.19 <.001 .315
13 belief:hemisphere:electrode_pair 2.58, 134.13 0.30 1.54 .001 .212
14 sex:hemisphere:electrode_pair 2.58, 134.13 0.30 0.11 <.001 .938
15 belief:sex:hemisphere:electrode_pair 2.58, 134.13 0.30 0.31 <.001 .788
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘+’ 0.1 ‘ ’ 1
Sphericity correction method: GG a_posteriori(alpha_param_rep_anova)5.3 Alpha Bandwidth
options(width = 100)
alpha_param_rep_anova = aov_ez("Subject", "Width", alpha_power_data, between = c("belief", "sex"), within = c("hemisphere", "electrode_pair"))Warning: Missing values for following ID(s):
3, 10, 16, 20, 24, 29, 31, 33, 37, 41, 43, 55, 60, 62, 63, 64, 68, 74, 77, 81, 83, 98, 107, 115
Removing those cases from the analysis.Contrasts set to contr.sum for the following variables: belief, sexmytable <- xtabs(~ sex + belief, data = alpha_param_rep_anova$data$long) / length(levels(alpha_param_rep_anova$data$long$electrode_pair)) / length(levels(alpha_param_rep_anova$data$long$hemisphere))
ftable(addmargins(mytable)) belief Believer Unbeliever Sum
sex
Female 17 20 37
Male 9 10 19
Sum 26 30 56alpha_param_rain <- ggplot(alpha_param_rep_anova$data$long, aes(y = Width, x = belief, color = sex, fill = sex)) +
stat_halfeye(
trim = FALSE,
adjust = 1,
.width = 0,
justification = -.15,
alpha = .5,
point_colour = NA) +
geom_point(size = 2, alpha = .4, position = position_jitter(width = .05, height = 0))
suppressWarnings(print(alpha_param_rain))
alpha_param_afex_plot <-
afex_plot(
alpha_param_rep_anova,
x = "belief",
trace = "sex",
panel = "hemisphere",
error = "between",
error_arg = list(width = .1),
dodge = -.5,
mapping = c("color"),
point_arg = list(size = 4)
)
suppressWarnings(print(alpha_param_afex_plot))
nice(alpha_param_rep_anova)Anova Table (Type 3 tests)
Response: Width
Effect df MSE F ges p.value
1 belief 1, 52 8.45 0.94 .013 .337
2 sex 1, 52 8.45 3.28 + .045 .076
3 belief:sex 1, 52 8.45 0.08 .001 .784
4 hemisphere 1, 52 0.52 2.67 .002 .108
5 belief:hemisphere 1, 52 0.52 0.01 <.001 .908
6 sex:hemisphere 1, 52 0.52 0.20 <.001 .659
7 belief:sex:hemisphere 1, 52 0.52 2.14 .002 .149
8 electrode_pair 3.81, 198.21 0.41 14.65 *** .037 <.001
9 belief:electrode_pair 3.81, 198.21 0.41 0.73 .002 .563
10 sex:electrode_pair 3.81, 198.21 0.41 0.38 <.001 .815
11 belief:sex:electrode_pair 3.81, 198.21 0.41 0.45 .001 .763
12 hemisphere:electrode_pair 4.68, 243.60 0.18 0.80 .001 .541
13 belief:hemisphere:electrode_pair 4.68, 243.60 0.18 1.01 .001 .408
14 sex:hemisphere:electrode_pair 4.68, 243.60 0.18 1.21 .002 .308
15 belief:sex:hemisphere:electrode_pair 4.68, 243.60 0.18 1.72 .002 .136
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘+’ 0.1 ‘ ’ 1
Sphericity correction method: GG a_posteriori(alpha_param_rep_anova)$emmeans
electrode_pair emmean SE df lower.CL upper.CL
Fp1.Fp2 2.42 0.1069 52 2.21 2.64
AF3.AF4 2.53 0.1153 52 2.30 2.76
AF7.AF8 2.42 0.1100 52 2.20 2.64
F1.F2 2.79 0.1246 52 2.54 3.04
F3.F4 2.63 0.1037 52 2.42 2.83
F5.F6 2.52 0.1069 52 2.31 2.73
F7.F8 2.43 0.0991 52 2.24 2.63
FC1.FC2 2.90 0.1248 52 2.65 3.15
Results are averaged over the levels of: belief, sex, hemisphere
Confidence level used: 0.95
$contrasts
contrast estimate SE df t.ratio p.value
Fp1.Fp2 - AF3.AF4 -0.1126 0.0417 52 -2.700 0.1456
Fp1.Fp2 - AF7.AF8 -0.0021 0.0609 52 -0.035 1.0000
Fp1.Fp2 - F1.F2 -0.3672 0.0684 52 -5.368 <.0001
Fp1.Fp2 - F3.F4 -0.2057 0.0691 52 -2.976 0.0781
Fp1.Fp2 - F5.F6 -0.0995 0.0705 52 -1.411 0.8483
Fp1.Fp2 - F7.F8 -0.0130 0.0762 52 -0.170 1.0000
Fp1.Fp2 - FC1.FC2 -0.4818 0.0899 52 -5.360 0.0001
AF3.AF4 - AF7.AF8 0.1105 0.0742 52 1.490 0.8093
AF3.AF4 - F1.F2 -0.2546 0.0487 52 -5.228 0.0001
AF3.AF4 - F3.F4 -0.0931 0.0648 52 -1.436 0.8361
AF3.AF4 - F5.F6 0.0131 0.0672 52 0.196 1.0000
AF3.AF4 - F7.F8 0.0996 0.0667 52 1.495 0.8067
AF3.AF4 - FC1.FC2 -0.3692 0.0720 52 -5.124 0.0001
AF7.AF8 - F1.F2 -0.3651 0.0825 52 -4.424 0.0012
AF7.AF8 - F3.F4 -0.2036 0.0645 52 -3.159 0.0498
AF7.AF8 - F5.F6 -0.0974 0.0646 52 -1.506 0.8007
AF7.AF8 - F7.F8 -0.0109 0.0806 52 -0.135 1.0000
AF7.AF8 - FC1.FC2 -0.4797 0.0955 52 -5.021 0.0002
F1.F2 - F3.F4 0.1615 0.0583 52 2.768 0.1258
F1.F2 - F5.F6 0.2677 0.0596 52 4.490 0.0010
F1.F2 - F7.F8 0.3542 0.0634 52 5.590 <.0001
F1.F2 - FC1.FC2 -0.1146 0.0504 52 -2.275 0.3268
F3.F4 - F5.F6 0.1063 0.0359 52 2.959 0.0814
F3.F4 - F7.F8 0.1928 0.0529 52 3.643 0.0134
F3.F4 - FC1.FC2 -0.2760 0.0616 52 -4.482 0.0010
F5.F6 - F7.F8 0.0865 0.0423 52 2.043 0.4643
F5.F6 - FC1.FC2 -0.3823 0.0714 52 -5.352 0.0001
F7.F8 - FC1.FC2 -0.4688 0.0688 52 -6.818 <.0001
Results are averaged over the levels of: belief, sex, hemisphere
P value adjustment: tukey method for comparing a family of 8 estimates
____________________________________________________________________________________________________5.4 Aperiodic Offset
options(width = 100)
aperiodic_offset_rep_anova = aov_ez("Subject", "Offset", alpha_power_data, between = c("belief", "sex"), within = c("hemisphere", "electrode_pair"))Contrasts set to contr.sum for the following variables: belief, sexmytable <- xtabs(~ sex + belief, data = aperiodic_offset_rep_anova$data$long) / length(levels(aperiodic_offset_rep_anova$data$long$electrode_pair)) / length(levels(aperiodic_offset_rep_anova$data$long$hemisphere))
ftable(addmargins(mytable)) belief Believer Unbeliever Sum
sex
Female 24 24 48
Male 17 15 32
Sum 41 39 80aperiodic_param_rain <- ggplot(aperiodic_offset_rep_anova$data$long, aes(y = Offset, x = belief, color = sex, fill = sex)) +
stat_halfeye(
trim = FALSE,
adjust = 1,
.width = 0,
justification = -.15,
alpha = .5,
point_colour = NA) +
geom_point(size = 2, alpha = .4, position = position_jitter(width = .05, height = 0))
suppressWarnings(print(aperiodic_param_rain))
aperiodic_param_afex_plot <-
afex_plot(
aperiodic_offset_rep_anova,
x = "belief",
trace = "sex",
panel = "hemisphere",
error = "between",
error_arg = list(width = .1),
dodge = -.5,
mapping = c("color"),
point_arg = list(size = 4)
)
suppressWarnings(print(aperiodic_param_afex_plot))
nice(aperiodic_offset_rep_anova)Anova Table (Type 3 tests)
Response: Offset
Effect df MSE F ges p.value
1 belief 1, 76 3.35 0.73 .007 .394
2 sex 1, 76 3.35 0.81 .008 .371
3 belief:sex 1, 76 3.35 0.15 .001 .696
4 hemisphere 1, 76 0.34 5.80 * .006 .018
5 belief:hemisphere 1, 76 0.34 2.88 + .003 .094
6 sex:hemisphere 1, 76 0.34 0.99 <.001 .322
7 belief:sex:hemisphere 1, 76 0.34 0.01 <.001 .938
8 electrode_pair 2.00, 151.80 0.41 40.49 *** .084 <.001
9 belief:electrode_pair 2.00, 151.80 0.41 0.72 .002 .486
10 sex:electrode_pair 2.00, 151.80 0.41 0.74 .002 .477
11 belief:sex:electrode_pair 2.00, 151.80 0.41 0.90 .002 .409
12 hemisphere:electrode_pair 4.40, 334.35 0.05 3.48 ** .002 .007
13 belief:hemisphere:electrode_pair 4.40, 334.35 0.05 1.55 <.001 .181
14 sex:hemisphere:electrode_pair 4.40, 334.35 0.05 1.04 <.001 .389
15 belief:sex:hemisphere:electrode_pair 4.40, 334.35 0.05 1.49 <.001 .200
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘+’ 0.1 ‘ ’ 1
Sphericity correction method: GG a_posteriori(aperiodic_offset_rep_anova)$emmeans
hemisphere emmean SE df lower.CL upper.CL
Left 1.10 0.0570 76 0.984 1.21
Right 1.02 0.0528 76 0.911 1.12
Results are averaged over the levels of: belief, sex, electrode_pair
Confidence level used: 0.95
$contrasts
contrast estimate SE df t.ratio p.value
Left - Right 0.0807 0.0335 76 2.408 0.0184
Results are averaged over the levels of: belief, sex, electrode_pair
____________________________________________________________________________________________________
$emmeans
electrode_pair emmean SE df lower.CL upper.CL
Fp1.Fp2 1.286 0.0748 76 1.136 1.435
AF3.AF4 1.243 0.0707 76 1.102 1.384
AF7.AF8 1.079 0.0623 76 0.955 1.203
F1.F2 1.190 0.0509 76 1.088 1.291
F3.F4 0.893 0.0486 76 0.796 0.990
F5.F6 0.822 0.0513 76 0.720 0.924
F7.F8 0.891 0.0523 76 0.787 0.996
FC1.FC2 1.051 0.0488 76 0.954 1.148
Results are averaged over the levels of: belief, sex, hemisphere
Confidence level used: 0.95
$contrasts
contrast estimate SE df t.ratio p.value
Fp1.Fp2 - AF3.AF4 0.04222 0.0138 76 3.070 0.0566
Fp1.Fp2 - AF7.AF8 0.20632 0.0240 76 8.579 <.0001
Fp1.Fp2 - F1.F2 0.09574 0.0502 76 1.906 0.5510
Fp1.Fp2 - F3.F4 0.39248 0.0514 76 7.637 <.0001
Fp1.Fp2 - F5.F6 0.46373 0.0464 76 9.991 <.0001
Fp1.Fp2 - F7.F8 0.39407 0.0456 76 8.647 <.0001
Fp1.Fp2 - FC1.FC2 0.23423 0.0654 76 3.582 0.0133
AF3.AF4 - AF7.AF8 0.16410 0.0245 76 6.688 <.0001
AF3.AF4 - F1.F2 0.05352 0.0451 76 1.186 0.9335
AF3.AF4 - F3.F4 0.35026 0.0452 76 7.745 <.0001
AF3.AF4 - F5.F6 0.42151 0.0403 76 10.459 <.0001
AF3.AF4 - F7.F8 0.35186 0.0408 76 8.622 <.0001
AF3.AF4 - FC1.FC2 0.19201 0.0603 76 3.184 0.0418
AF7.AF8 - F1.F2 -0.11058 0.0422 76 -2.620 0.1651
AF7.AF8 - F3.F4 0.18617 0.0381 76 4.885 0.0001
AF7.AF8 - F5.F6 0.25741 0.0334 76 7.701 <.0001
AF7.AF8 - F7.F8 0.18776 0.0325 76 5.780 <.0001
AF7.AF8 - FC1.FC2 0.02791 0.0518 76 0.539 0.9994
F1.F2 - F3.F4 0.29674 0.0287 76 10.342 <.0001
F1.F2 - F5.F6 0.36799 0.0299 76 12.314 <.0001
F1.F2 - F7.F8 0.29833 0.0359 76 8.310 <.0001
F1.F2 - FC1.FC2 0.13849 0.0309 76 4.478 0.0007
F3.F4 - F5.F6 0.07125 0.0155 76 4.607 0.0004
F3.F4 - F7.F8 0.00159 0.0232 76 0.069 1.0000
F3.F4 - FC1.FC2 -0.15826 0.0278 76 -5.689 <.0001
F5.F6 - F7.F8 -0.06966 0.0174 76 -3.994 0.0036
F5.F6 - FC1.FC2 -0.22950 0.0326 76 -7.048 <.0001
F7.F8 - FC1.FC2 -0.15985 0.0375 76 -4.265 0.0014
Results are averaged over the levels of: belief, sex, hemisphere
P value adjustment: tukey method for comparing a family of 8 estimates
____________________________________________________________________________________________________
$emmeans
hemisphere electrode_pair emmean SE df lower.CL upper.CL
Left Fp1.Fp2 1.322 0.0794 76 1.164 1.480
Right Fp1.Fp2 1.249 0.0755 76 1.099 1.399
Left AF3.AF4 1.291 0.0741 76 1.143 1.439
Right AF3.AF4 1.196 0.0724 76 1.052 1.340
Left AF7.AF8 1.176 0.0730 76 1.030 1.321
Right AF7.AF8 0.983 0.0627 76 0.858 1.108
Left F1.F2 1.233 0.0548 76 1.124 1.342
Right F1.F2 1.147 0.0535 76 1.040 1.253
Left F3.F4 0.910 0.0519 76 0.806 1.013
Right F3.F4 0.877 0.0532 76 0.771 0.983
Left F5.F6 0.830 0.0582 76 0.714 0.945
Right F5.F6 0.814 0.0538 76 0.707 0.921
Left F7.F8 0.929 0.0609 76 0.808 1.051
Right F7.F8 0.854 0.0543 76 0.746 0.962
Left FC1.FC2 1.088 0.0493 76 0.990 1.187
Right FC1.FC2 1.014 0.0540 76 0.907 1.122
Results are averaged over the levels of: belief, sex
Confidence level used: 0.95
$contrasts
contrast estimate SE df t.ratio p.value
Left Fp1.Fp2 - Right Fp1.Fp2 0.0730 0.0401 76 1.820 0.9011
Left Fp1.Fp2 - Left AF3.AF4 0.0310 0.0180 76 1.723 0.9338
Left Fp1.Fp2 - Right AF3.AF4 0.1264 0.0393 76 3.219 0.1170
Left Fp1.Fp2 - Left AF7.AF8 0.1463 0.0234 76 6.262 <.0001
Left Fp1.Fp2 - Right AF7.AF8 0.3394 0.0538 76 6.310 <.0001
Left Fp1.Fp2 - Left F1.F2 0.0892 0.0510 76 1.749 0.9259
Left Fp1.Fp2 - Right F1.F2 0.1753 0.0631 76 2.779 0.3033
Left Fp1.Fp2 - Left F3.F4 0.4125 0.0518 76 7.957 <.0001
Left Fp1.Fp2 - Right F3.F4 0.4455 0.0657 76 6.779 <.0001
Left Fp1.Fp2 - Left F5.F6 0.4925 0.0485 76 10.160 <.0001
Left Fp1.Fp2 - Right F5.F6 0.5080 0.0612 76 8.300 <.0001
Left Fp1.Fp2 - Left F7.F8 0.3929 0.0468 76 8.394 <.0001
Left Fp1.Fp2 - Right F7.F8 0.4683 0.0649 76 7.217 <.0001
Left Fp1.Fp2 - Left FC1.FC2 0.2336 0.0662 76 3.529 0.0520
Left Fp1.Fp2 - Right FC1.FC2 0.3079 0.0742 76 4.151 0.0078
Right Fp1.Fp2 - Left AF3.AF4 -0.0420 0.0412 76 -1.020 0.9996
Right Fp1.Fp2 - Right AF3.AF4 0.0534 0.0157 76 3.409 0.0720
Right Fp1.Fp2 - Left AF7.AF8 0.0733 0.0457 76 1.604 0.9628
Right Fp1.Fp2 - Right AF7.AF8 0.2663 0.0377 76 7.073 <.0001
Right Fp1.Fp2 - Left F1.F2 0.0162 0.0596 76 0.272 1.0000
Right Fp1.Fp2 - Right F1.F2 0.1023 0.0540 76 1.894 0.8702
Right Fp1.Fp2 - Left F3.F4 0.3395 0.0588 76 5.773 <.0001
Right Fp1.Fp2 - Right F3.F4 0.3725 0.0576 76 6.466 <.0001
Right Fp1.Fp2 - Left F5.F6 0.4195 0.0579 76 7.250 <.0001
Right Fp1.Fp2 - Right F5.F6 0.4350 0.0530 76 8.212 <.0001
Right Fp1.Fp2 - Left F7.F8 0.3199 0.0535 76 5.974 <.0001
Right Fp1.Fp2 - Right F7.F8 0.3953 0.0548 76 7.210 <.0001
Right Fp1.Fp2 - Left FC1.FC2 0.1606 0.0711 76 2.259 0.6535
Right Fp1.Fp2 - Right FC1.FC2 0.2348 0.0705 76 3.330 0.0886
Left AF3.AF4 - Right AF3.AF4 0.0954 0.0380 76 2.513 0.4733
Left AF3.AF4 - Left AF7.AF8 0.1153 0.0281 76 4.110 0.0089
Left AF3.AF4 - Right AF7.AF8 0.3083 0.0522 76 5.910 <.0001
Left AF3.AF4 - Left F1.F2 0.0582 0.0483 76 1.206 0.9976
Left AF3.AF4 - Right F1.F2 0.1443 0.0578 76 2.496 0.4857
Left AF3.AF4 - Left F3.F4 0.3815 0.0447 76 8.527 <.0001
Left AF3.AF4 - Right F3.F4 0.4145 0.0606 76 6.838 <.0001
Left AF3.AF4 - Left F5.F6 0.4615 0.0409 76 11.285 <.0001
Left AF3.AF4 - Right F5.F6 0.4770 0.0564 76 8.454 <.0001
Left AF3.AF4 - Left F7.F8 0.3619 0.0405 76 8.929 <.0001
Left AF3.AF4 - Right F7.F8 0.4373 0.0606 76 7.217 <.0001
Left AF3.AF4 - Left FC1.FC2 0.2026 0.0618 76 3.278 0.1011
Left AF3.AF4 - Right FC1.FC2 0.2769 0.0696 76 3.976 0.0137
Right AF3.AF4 - Left AF7.AF8 0.0199 0.0450 76 0.442 1.0000
Right AF3.AF4 - Right AF7.AF8 0.2129 0.0366 76 5.817 <.0001
Right AF3.AF4 - Left F1.F2 -0.0372 0.0544 76 -0.684 1.0000
Right AF3.AF4 - Right F1.F2 0.0488 0.0480 76 1.018 0.9997
Right AF3.AF4 - Left F3.F4 0.2860 0.0528 76 5.417 0.0001
Right AF3.AF4 - Right F3.F4 0.3191 0.0526 76 6.066 <.0001
Right AF3.AF4 - Left F5.F6 0.3661 0.0526 76 6.960 <.0001
Right AF3.AF4 - Right F5.F6 0.3815 0.0484 76 7.889 <.0001
Right AF3.AF4 - Left F7.F8 0.2664 0.0499 76 5.341 0.0001
Right AF3.AF4 - Right F7.F8 0.3418 0.0516 76 6.630 <.0001
Right AF3.AF4 - Left FC1.FC2 0.1072 0.0656 76 1.633 0.9569
Right AF3.AF4 - Right FC1.FC2 0.1814 0.0649 76 2.795 0.2943
Left AF7.AF8 - Right AF7.AF8 0.1930 0.0550 76 3.512 0.0545
Left AF7.AF8 - Left F1.F2 -0.0571 0.0460 76 -1.241 0.9968
Left AF7.AF8 - Right F1.F2 0.0290 0.0593 76 0.489 1.0000
Left AF7.AF8 - Left F3.F4 0.2662 0.0473 76 5.629 <.0001
Left AF7.AF8 - Right F3.F4 0.2992 0.0617 76 4.849 0.0007
Left AF7.AF8 - Left F5.F6 0.3462 0.0441 76 7.851 <.0001
Left AF7.AF8 - Right F5.F6 0.3617 0.0577 76 6.266 <.0001
Left AF7.AF8 - Left F7.F8 0.2466 0.0406 76 6.074 <.0001
Left AF7.AF8 - Right F7.F8 0.3220 0.0619 76 5.205 0.0002
Left AF7.AF8 - Left FC1.FC2 0.0873 0.0590 76 1.479 0.9817
Left AF7.AF8 - Right FC1.FC2 0.1616 0.0672 76 2.405 0.5502
Right AF7.AF8 - Left F1.F2 -0.2501 0.0572 76 -4.372 0.0037
Right AF7.AF8 - Right F1.F2 -0.1641 0.0509 76 -3.223 0.1159
Right AF7.AF8 - Left F3.F4 0.0731 0.0525 76 1.393 0.9896
Right AF7.AF8 - Right F3.F4 0.1062 0.0405 76 2.622 0.3994
Right AF7.AF8 - Left F5.F6 0.1532 0.0551 76 2.781 0.3024
Right AF7.AF8 - Right F5.F6 0.1686 0.0347 76 4.862 0.0006
Right AF7.AF8 - Left F7.F8 0.0535 0.0520 76 1.029 0.9996
Right AF7.AF8 - Right F7.F8 0.1289 0.0376 76 3.427 0.0687
Right AF7.AF8 - Left FC1.FC2 -0.1057 0.0594 76 -1.781 0.9156
Right AF7.AF8 - Right FC1.FC2 -0.0315 0.0586 76 -0.538 1.0000
Left F1.F2 - Right F1.F2 0.0861 0.0367 76 2.345 0.5925
Left F1.F2 - Left F3.F4 0.3233 0.0331 76 9.761 <.0001
Left F1.F2 - Right F3.F4 0.3563 0.0444 76 8.033 <.0001
Left F1.F2 - Left F5.F6 0.4033 0.0371 76 10.872 <.0001
Left F1.F2 - Right F5.F6 0.4188 0.0458 76 9.141 <.0001
Left F1.F2 - Left F7.F8 0.3037 0.0431 76 7.040 <.0001
Left F1.F2 - Right F7.F8 0.3791 0.0508 76 7.461 <.0001
Left F1.F2 - Left FC1.FC2 0.1444 0.0337 76 4.287 0.0049
Left F1.F2 - Right FC1.FC2 0.2186 0.0454 76 4.818 0.0007
Right F1.F2 - Left F3.F4 0.2372 0.0438 76 5.414 0.0001
Right F1.F2 - Right F3.F4 0.2702 0.0356 76 7.600 <.0001
Right F1.F2 - Left F5.F6 0.3172 0.0453 76 6.997 <.0001
Right F1.F2 - Right F5.F6 0.3327 0.0376 76 8.857 <.0001
Right F1.F2 - Left F7.F8 0.2176 0.0508 76 4.285 0.0050
Right F1.F2 - Right F7.F8 0.2930 0.0429 76 6.834 <.0001
Right F1.F2 - Left FC1.FC2 0.0583 0.0427 76 1.365 0.9915
Right F1.F2 - Right FC1.FC2 0.1326 0.0367 76 3.610 0.0414
Left F3.F4 - Right F3.F4 0.0330 0.0401 76 0.824 1.0000
Left F3.F4 - Left F5.F6 0.0800 0.0224 76 3.573 0.0461
Left F3.F4 - Right F5.F6 0.0955 0.0418 76 2.287 0.6342
Left F3.F4 - Left F7.F8 -0.0196 0.0312 76 -0.629 1.0000
Left F3.F4 - Right F7.F8 0.0558 0.0456 76 1.225 0.9972
Left F3.F4 - Left FC1.FC2 -0.1789 0.0324 76 -5.516 0.0001
Left F3.F4 - Right FC1.FC2 -0.1046 0.0480 76 -2.179 0.7085
Right F3.F4 - Left F5.F6 0.0470 0.0449 76 1.046 0.9995
Right F3.F4 - Right F5.F6 0.0625 0.0180 76 3.469 0.0614
Right F3.F4 - Left F7.F8 -0.0526 0.0478 76 -1.101 0.9991
Right F3.F4 - Right F7.F8 0.0228 0.0281 76 0.810 1.0000
Right F3.F4 - Left FC1.FC2 -0.2119 0.0382 76 -5.552 <.0001
Right F3.F4 - Right FC1.FC2 -0.1376 0.0330 76 -4.177 0.0071
Left F5.F6 - Right F5.F6 0.0155 0.0450 76 0.344 1.0000
Left F5.F6 - Left F7.F8 -0.0996 0.0267 76 -3.735 0.0288
Left F5.F6 - Right F7.F8 -0.0242 0.0493 76 -0.491 1.0000
Left F5.F6 - Left FC1.FC2 -0.2589 0.0399 76 -6.493 <.0001
Left F5.F6 - Right FC1.FC2 -0.1846 0.0518 76 -3.566 0.0469
Right F5.F6 - Left F7.F8 -0.1151 0.0455 76 -2.530 0.4615
Right F5.F6 - Right F7.F8 -0.0397 0.0196 76 -2.022 0.8052
Right F5.F6 - Left FC1.FC2 -0.2744 0.0424 76 -6.468 <.0001
Right F5.F6 - Right FC1.FC2 -0.2001 0.0373 76 -5.367 0.0001
Left F7.F8 - Right F7.F8 0.0754 0.0485 76 1.553 0.9717
Left F7.F8 - Left FC1.FC2 -0.1593 0.0455 76 -3.504 0.0558
Left F7.F8 - Right FC1.FC2 -0.0850 0.0563 76 -1.511 0.9779
Right F7.F8 - Left FC1.FC2 -0.2347 0.0466 76 -5.040 0.0003
Right F7.F8 - Right FC1.FC2 -0.1604 0.0421 76 -3.814 0.0227
Left FC1.FC2 - Right FC1.FC2 0.0743 0.0346 76 2.147 0.7290
Results are averaged over the levels of: belief, sex
P value adjustment: tukey method for comparing a family of 16 estimates
____________________________________________________________________________________________________5.5 Aperiodic Slope
options(width = 100)
aperiodic_slope_rep_anova = aov_ez("Subject", "Slope", alpha_power_data, between = c("belief", "sex"), within = c("hemisphere", "electrode_pair"))Contrasts set to contr.sum for the following variables: belief, sexmytable <- xtabs(~ sex + belief, data = aperiodic_slope_rep_anova$data$long) / length(levels(aperiodic_slope_rep_anova$data$long$electrode_pair)) / length(levels(aperiodic_slope_rep_anova$data$long$hemisphere))
ftable(addmargins(mytable)) belief Believer Unbeliever Sum
sex
Female 24 24 48
Male 17 15 32
Sum 41 39 80aperiodic_param_rain <- ggplot(aperiodic_slope_rep_anova$data$long, aes(y = Slope, x = belief, color = sex, fill = sex)) +
stat_halfeye(
trim = FALSE,
adjust = 1,
.width = 0,
justification = -.15,
alpha = .5,
point_colour = NA) +
geom_point(size = 2, alpha = .4, position = position_jitter(width = .05, height = 0))
suppressWarnings(print(aperiodic_param_rain))
aperiodic_param_afex_plot <-
afex_plot(
aperiodic_slope_rep_anova,
x = "belief",
trace = "sex",
panel = "hemisphere",
error = "between",
error_arg = list(width = .1),
dodge = -.5,
mapping = c("color"),
point_arg = list(size = 4)
)
suppressWarnings(print(aperiodic_param_afex_plot))
nice(aperiodic_slope_rep_anova)Anova Table (Type 3 tests)
Response: Slope
Effect df MSE F ges p.value
1 belief 1, 76 2.00 0.57 .005 .452
2 sex 1, 76 2.00 1.35 .012 .248
3 belief:sex 1, 76 2.00 0.34 .003 .560
4 hemisphere 1, 76 0.17 0.01 <.001 .921
5 belief:hemisphere 1, 76 0.17 2.90 + .002 .093
6 sex:hemisphere 1, 76 0.17 0.62 <.001 .434
7 belief:sex:hemisphere 1, 76 0.17 0.00 <.001 .989
8 electrode_pair 2.21, 168.00 0.23 51.96 *** .108 <.001
9 belief:electrode_pair 2.21, 168.00 0.23 0.45 .001 .658
10 sex:electrode_pair 2.21, 168.00 0.23 0.11 <.001 .914
11 belief:sex:electrode_pair 2.21, 168.00 0.23 1.26 .003 .288
12 hemisphere:electrode_pair 4.79, 364.00 0.03 4.06 ** .003 .002
13 belief:hemisphere:electrode_pair 4.79, 364.00 0.03 1.14 <.001 .340
14 sex:hemisphere:electrode_pair 4.79, 364.00 0.03 0.54 <.001 .737
15 belief:sex:hemisphere:electrode_pair 4.79, 364.00 0.03 1.23 <.001 .295
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘+’ 0.1 ‘ ’ 1
Sphericity correction method: GG a_posteriori(aperiodic_slope_rep_anova)$emmeans
electrode_pair emmean SE df lower.CL upper.CL
Fp1.Fp2 1.38 0.0579 76 1.267 1.50
AF3.AF4 1.39 0.0539 76 1.284 1.50
AF7.AF8 1.18 0.0502 76 1.082 1.28
F1.F2 1.45 0.0379 76 1.373 1.52
F3.F4 1.17 0.0401 76 1.089 1.25
F5.F6 1.07 0.0405 76 0.989 1.15
F7.F8 1.09 0.0404 76 1.005 1.17
FC1.FC2 1.41 0.0352 76 1.337 1.48
Results are averaged over the levels of: belief, sex, hemisphere
Confidence level used: 0.95
$contrasts
contrast estimate SE df t.ratio p.value
Fp1.Fp2 - AF3.AF4 -0.00934 0.0115 76 -0.808 0.9922
Fp1.Fp2 - AF7.AF8 0.20038 0.0209 76 9.579 <.0001
Fp1.Fp2 - F1.F2 -0.06630 0.0368 76 -1.800 0.6221
Fp1.Fp2 - F3.F4 0.21359 0.0400 76 5.340 <.0001
Fp1.Fp2 - F5.F6 0.31205 0.0364 76 8.562 <.0001
Fp1.Fp2 - F7.F8 0.29672 0.0356 76 8.343 <.0001
Fp1.Fp2 - FC1.FC2 -0.02506 0.0485 76 -0.517 0.9995
AF3.AF4 - AF7.AF8 0.20972 0.0224 76 9.349 <.0001
AF3.AF4 - F1.F2 -0.05696 0.0324 76 -1.758 0.6497
AF3.AF4 - F3.F4 0.22293 0.0353 76 6.322 <.0001
AF3.AF4 - F5.F6 0.32139 0.0315 76 10.207 <.0001
AF3.AF4 - F7.F8 0.30606 0.0318 76 9.637 <.0001
AF3.AF4 - FC1.FC2 -0.01572 0.0439 76 -0.358 1.0000
AF7.AF8 - F1.F2 -0.26668 0.0339 76 -7.877 <.0001
AF7.AF8 - F3.F4 0.01321 0.0348 76 0.380 0.9999
AF7.AF8 - F5.F6 0.11167 0.0301 76 3.707 0.0090
AF7.AF8 - F7.F8 0.09634 0.0293 76 3.287 0.0314
AF7.AF8 - FC1.FC2 -0.22544 0.0435 76 -5.181 <.0001
F1.F2 - F3.F4 0.27989 0.0211 76 13.258 <.0001
F1.F2 - F5.F6 0.37835 0.0220 76 17.177 <.0001
F1.F2 - F7.F8 0.36302 0.0255 76 14.250 <.0001
F1.F2 - FC1.FC2 0.04124 0.0195 76 2.118 0.4130
F3.F4 - F5.F6 0.09846 0.0162 76 6.063 <.0001
F3.F4 - F7.F8 0.08313 0.0218 76 3.819 0.0063
F3.F4 - FC1.FC2 -0.23865 0.0208 76 -11.462 <.0001
F5.F6 - F7.F8 -0.01533 0.0182 76 -0.842 0.9900
F5.F6 - FC1.FC2 -0.33711 0.0256 76 -13.150 <.0001
F7.F8 - FC1.FC2 -0.32178 0.0293 76 -10.989 <.0001
Results are averaged over the levels of: belief, sex, hemisphere
P value adjustment: tukey method for comparing a family of 8 estimates
____________________________________________________________________________________________________
$emmeans
hemisphere electrode_pair emmean SE df lower.CL upper.CL
Left Fp1.Fp2 1.40 0.0632 76 1.271 1.52
Right Fp1.Fp2 1.37 0.0563 76 1.255 1.48
Left AF3.AF4 1.42 0.0582 76 1.300 1.53
Right AF3.AF4 1.37 0.0541 76 1.259 1.47
Left AF7.AF8 1.21 0.0590 76 1.093 1.33
Right AF7.AF8 1.15 0.0490 76 1.055 1.25
Left F1.F2 1.46 0.0416 76 1.378 1.54
Right F1.F2 1.44 0.0374 76 1.361 1.51
Left F3.F4 1.15 0.0456 76 1.059 1.24
Right F3.F4 1.19 0.0408 76 1.106 1.27
Left F5.F6 1.03 0.0474 76 0.935 1.12
Right F5.F6 1.11 0.0423 76 1.026 1.19
Left F7.F8 1.07 0.0473 76 0.972 1.16
Right F7.F8 1.10 0.0418 76 1.021 1.19
Left FC1.FC2 1.41 0.0376 76 1.339 1.49
Right FC1.FC2 1.40 0.0360 76 1.328 1.47
Results are averaged over the levels of: belief, sex
Confidence level used: 0.95
$contrasts
contrast estimate SE df t.ratio p.value
Left Fp1.Fp2 - Right Fp1.Fp2 0.029911 0.0307 76 0.974 0.9998
Left Fp1.Fp2 - Left AF3.AF4 -0.019110 0.0179 76 -1.067 0.9994
Left Fp1.Fp2 - Right AF3.AF4 0.030350 0.0309 76 0.981 0.9998
Left Fp1.Fp2 - Left AF7.AF8 0.186781 0.0251 76 7.432 <.0001
Left Fp1.Fp2 - Right AF7.AF8 0.243895 0.0416 76 5.861 <.0001
Left Fp1.Fp2 - Left F1.F2 -0.063890 0.0394 76 -1.624 0.9588
Left Fp1.Fp2 - Right F1.F2 -0.038791 0.0471 76 -0.824 1.0000
Left Fp1.Fp2 - Left F3.F4 0.247253 0.0439 76 5.630 <.0001
Left Fp1.Fp2 - Right F3.F4 0.209839 0.0507 76 4.138 0.0081
Left Fp1.Fp2 - Left F5.F6 0.367571 0.0431 76 8.530 <.0001
Left Fp1.Fp2 - Right F5.F6 0.286449 0.0465 76 6.157 <.0001
Left Fp1.Fp2 - Left F7.F8 0.330958 0.0432 76 7.668 <.0001
Left Fp1.Fp2 - Right F7.F8 0.292402 0.0475 76 6.154 <.0001
Left Fp1.Fp2 - Left FC1.FC2 -0.017298 0.0517 76 -0.334 1.0000
Left Fp1.Fp2 - Right FC1.FC2 -0.002904 0.0550 76 -0.053 1.0000
Right Fp1.Fp2 - Left AF3.AF4 -0.049021 0.0324 76 -1.511 0.9778
Right Fp1.Fp2 - Right AF3.AF4 0.000439 0.0114 76 0.039 1.0000
Right Fp1.Fp2 - Left AF7.AF8 0.156869 0.0350 76 4.477 0.0026
Right Fp1.Fp2 - Right AF7.AF8 0.213983 0.0282 76 7.578 <.0001
Right Fp1.Fp2 - Left F1.F2 -0.093801 0.0402 76 -2.332 0.6019
Right Fp1.Fp2 - Right F1.F2 -0.068702 0.0387 76 -1.777 0.9167
Right Fp1.Fp2 - Left F3.F4 0.217342 0.0454 76 4.790 0.0008
Right Fp1.Fp2 - Right F3.F4 0.179927 0.0430 76 4.184 0.0070
Right Fp1.Fp2 - Left F5.F6 0.337659 0.0469 76 7.206 <.0001
Right Fp1.Fp2 - Right F5.F6 0.256538 0.0396 76 6.481 <.0001
Right Fp1.Fp2 - Left F7.F8 0.301047 0.0430 76 7.000 <.0001
Right Fp1.Fp2 - Right F7.F8 0.262491 0.0385 76 6.821 <.0001
Right Fp1.Fp2 - Left FC1.FC2 -0.047210 0.0500 76 -0.944 0.9999
Right Fp1.Fp2 - Right FC1.FC2 -0.032815 0.0510 76 -0.643 1.0000
Left AF3.AF4 - Right AF3.AF4 0.049460 0.0314 76 1.578 0.9676
Left AF3.AF4 - Left AF7.AF8 0.205891 0.0286 76 7.194 <.0001
Left AF3.AF4 - Right AF7.AF8 0.263005 0.0417 76 6.314 <.0001
Left AF3.AF4 - Left F1.F2 -0.044780 0.0363 76 -1.234 0.9970
Left AF3.AF4 - Right F1.F2 -0.019681 0.0425 76 -0.463 1.0000
Left AF3.AF4 - Left F3.F4 0.266363 0.0386 76 6.898 <.0001
Left AF3.AF4 - Right F3.F4 0.228948 0.0466 76 4.909 0.0005
Left AF3.AF4 - Left F5.F6 0.386680 0.0386 76 10.029 <.0001
Left AF3.AF4 - Right F5.F6 0.305559 0.0421 76 7.264 <.0001
Left AF3.AF4 - Left F7.F8 0.350068 0.0397 76 8.820 <.0001
Left AF3.AF4 - Right F7.F8 0.311512 0.0439 76 7.089 <.0001
Left AF3.AF4 - Left FC1.FC2 0.001811 0.0474 76 0.038 1.0000
Left AF3.AF4 - Right FC1.FC2 0.016206 0.0502 76 0.323 1.0000
Right AF3.AF4 - Left AF7.AF8 0.156431 0.0359 76 4.361 0.0038
Right AF3.AF4 - Right AF7.AF8 0.213545 0.0292 76 7.321 <.0001
Right AF3.AF4 - Left F1.F2 -0.094240 0.0375 76 -2.515 0.4722
Right AF3.AF4 - Right F1.F2 -0.069141 0.0340 76 -2.034 0.7984
Right AF3.AF4 - Left F3.F4 0.216903 0.0425 76 5.103 0.0003
Right AF3.AF4 - Right F3.F4 0.179488 0.0394 76 4.561 0.0019
Right AF3.AF4 - Left F5.F6 0.337221 0.0438 76 7.691 <.0001
Right AF3.AF4 - Right F5.F6 0.256099 0.0360 76 7.110 <.0001
Right AF3.AF4 - Left F7.F8 0.300608 0.0410 76 7.326 <.0001
Right AF3.AF4 - Right F7.F8 0.262052 0.0357 76 7.339 <.0001
Right AF3.AF4 - Left FC1.FC2 -0.047648 0.0469 76 -1.016 0.9997
Right AF3.AF4 - Right FC1.FC2 -0.033254 0.0466 76 -0.714 1.0000
Left AF7.AF8 - Right AF7.AF8 0.057114 0.0412 76 1.387 0.9900
Left AF7.AF8 - Left F1.F2 -0.250670 0.0396 76 -6.327 <.0001
Left AF7.AF8 - Right F1.F2 -0.225572 0.0471 76 -4.786 0.0008
Left AF7.AF8 - Left F3.F4 0.060473 0.0434 76 1.395 0.9895
Left AF7.AF8 - Right F3.F4 0.023058 0.0502 76 0.459 1.0000
Left AF7.AF8 - Left F5.F6 0.180790 0.0409 76 4.421 0.0031
Left AF7.AF8 - Right F5.F6 0.099668 0.0465 76 2.145 0.7305
Left AF7.AF8 - Left F7.F8 0.144177 0.0397 76 3.634 0.0387
Left AF7.AF8 - Right F7.F8 0.105621 0.0474 76 2.226 0.6762
Left AF7.AF8 - Left FC1.FC2 -0.204079 0.0507 76 -4.026 0.0117
Left AF7.AF8 - Right FC1.FC2 -0.189685 0.0538 76 -3.528 0.0522
Right AF7.AF8 - Left F1.F2 -0.307784 0.0398 76 -7.727 <.0001
Right AF7.AF8 - Right F1.F2 -0.282686 0.0375 76 -7.532 <.0001
Right AF7.AF8 - Left F3.F4 0.003358 0.0449 76 0.075 1.0000
Right AF7.AF8 - Right F3.F4 -0.034056 0.0344 76 -0.990 0.9998
Right AF7.AF8 - Left F5.F6 0.123676 0.0469 76 2.637 0.3901
Right AF7.AF8 - Right F5.F6 0.042554 0.0287 76 1.484 0.9812
Right AF7.AF8 - Left F7.F8 0.087063 0.0424 76 2.055 0.7863
Right AF7.AF8 - Right F7.F8 0.048507 0.0310 76 1.565 0.9697
Right AF7.AF8 - Left FC1.FC2 -0.261193 0.0464 76 -5.628 <.0001
Right AF7.AF8 - Right FC1.FC2 -0.246799 0.0460 76 -5.365 0.0001
Left F1.F2 - Right F1.F2 0.025099 0.0225 76 1.116 0.9990
Left F1.F2 - Left F3.F4 0.311143 0.0259 76 12.020 <.0001
Left F1.F2 - Right F3.F4 0.273728 0.0321 76 8.530 <.0001
Left F1.F2 - Left F5.F6 0.431460 0.0297 76 14.543 <.0001
Left F1.F2 - Right F5.F6 0.350339 0.0335 76 10.466 <.0001
Left F1.F2 - Left F7.F8 0.394848 0.0327 76 12.083 <.0001
Left F1.F2 - Right F7.F8 0.356292 0.0337 76 10.584 <.0001
Left F1.F2 - Left FC1.FC2 0.046591 0.0224 76 2.080 0.7717
Left F1.F2 - Right FC1.FC2 0.060985 0.0275 76 2.214 0.6849
Right F1.F2 - Left F3.F4 0.286044 0.0321 76 8.920 <.0001
Right F1.F2 - Right F3.F4 0.248630 0.0250 76 9.942 <.0001
Right F1.F2 - Left F5.F6 0.406362 0.0346 76 11.728 <.0001
Right F1.F2 - Right F5.F6 0.325240 0.0278 76 11.700 <.0001
Right F1.F2 - Left F7.F8 0.369749 0.0383 76 9.655 <.0001
Right F1.F2 - Right F7.F8 0.331193 0.0297 76 11.152 <.0001
Right F1.F2 - Left FC1.FC2 0.021493 0.0268 76 0.801 1.0000
Right F1.F2 - Right FC1.FC2 0.035887 0.0224 76 1.601 0.9633
Left F3.F4 - Right F3.F4 -0.037415 0.0326 76 -1.146 0.9986
Left F3.F4 - Left F5.F6 0.120317 0.0235 76 5.111 0.0002
Left F3.F4 - Right F5.F6 0.039196 0.0369 76 1.064 0.9994
Left F3.F4 - Left F7.F8 0.083705 0.0281 76 2.974 0.2054
Left F3.F4 - Right F7.F8 0.045149 0.0387 76 1.167 0.9984
Left F3.F4 - Left FC1.FC2 -0.264552 0.0226 76 -11.718 <.0001
Left F3.F4 - Right FC1.FC2 -0.250157 0.0330 76 -7.579 <.0001
Right F3.F4 - Left F5.F6 0.157732 0.0368 76 4.285 0.0050
Right F3.F4 - Right F5.F6 0.076611 0.0195 76 3.930 0.0159
Right F3.F4 - Left F7.F8 0.121120 0.0370 76 3.270 0.1032
Right F3.F4 - Right F7.F8 0.082563 0.0273 76 3.023 0.1846
Right F3.F4 - Left FC1.FC2 -0.227137 0.0304 76 -7.475 <.0001
Right F3.F4 - Right FC1.FC2 -0.212743 0.0272 76 -7.830 <.0001
Left F5.F6 - Right F5.F6 -0.081122 0.0391 76 -2.076 0.7741
Left F5.F6 - Left F7.F8 -0.036613 0.0288 76 -1.270 0.9959
Left F5.F6 - Right F7.F8 -0.075169 0.0410 76 -1.832 0.8967
Left F5.F6 - Left FC1.FC2 -0.384869 0.0312 76 -12.345 <.0001
Left F5.F6 - Right FC1.FC2 -0.370475 0.0367 76 -10.083 <.0001
Right F5.F6 - Left F7.F8 0.044509 0.0366 76 1.218 0.9974
Right F5.F6 - Right F7.F8 0.005953 0.0212 76 0.281 1.0000
Right F5.F6 - Left FC1.FC2 -0.303747 0.0354 76 -8.582 <.0001
Right F5.F6 - Right FC1.FC2 -0.289353 0.0323 76 -8.958 <.0001
Left F7.F8 - Right F7.F8 -0.038556 0.0381 76 -1.013 0.9997
Left F7.F8 - Left FC1.FC2 -0.348256 0.0338 76 -10.312 <.0001
Left F7.F8 - Right FC1.FC2 -0.333862 0.0417 76 -8.009 <.0001
Right F7.F8 - Left FC1.FC2 -0.309700 0.0358 76 -8.649 <.0001
Right F7.F8 - Right FC1.FC2 -0.295306 0.0343 76 -8.601 <.0001
Left FC1.FC2 - Right FC1.FC2 0.014394 0.0215 76 0.670 1.0000
Results are averaged over the levels of: belief, sex
P value adjustment: tukey method for comparing a family of 16 estimates
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rode %in% c('E100-F3a' , 'E068-F4a')]  <- 'F3-F4'
alpha_power_data$electrode_pair[alpha_power_data$Electrode %in% c('E101-F5a' , 'E069-F6a')]  <- 'F5-F6'
alpha_power_data$electrode_pair[alpha_power_data$Electrode %in% c('F7'  , 'F8')]   <- 'F7-F8'
alpha_power_data$electrode_pair[alpha_power_data$Electrode %in% c('E088-FC1a', 'E075-FC2a')] <- 'FC1-FC2'
alpha_power_data$electrode_pair <- factor(alpha_power_data$electrode_pair, levels = c('Fp1-Fp2', 'AF3-AF4', 'AF7-AF8', 'F1-F2', 'F3-F4','F5-F6', 'F7-F8', 'FC1-FC2'))
write.csv(alpha_power_data,  paste(data_dir, '/alpha_power_data_clean.csv', sep = ''),  row.names = FALSE)
asymmetry_Fp2_Fp1 <- c()
asymmetry_AF4_AF3 <- c()
asymmetry_AF8_AF7 <- c()
asymmetry_F2_F1   <- c()
asymmetry_F4_F3   <- c()
asymmetry_F6_F5   <- c()
asymmetry_F8_F7   <- c()
asymmetry_FC2_FC1 <- c()
subj_block <- unique(alpha_power_data[c("Subject" ,"vulnerability" ,"belief" ,"sex")])
for (subj in subj_block$Subject) {
  subject_data <- subset(alpha_power_data, Subject == subj)
  asymmetry_Fp2_Fp1 <- c(asymmetry_Fp2_Fp1, subject_data[which(subject_data$Electrode == 'Fp2') ,      5] - subject_data[which(subject_data$Electrode=='Fp1') ,      5])
  asymmetry_AF4_AF3 <- c(asymmetry_AF4_AF3, subject_data[which(subject_data$Electrode == 'E079-AF4a'), 5] - subject_data[which(subject_data$Electrode=='E092-AF3a'), 5])
  asymmetry_AF8_AF7 <- c(asymmetry_AF8_AF7, subject_data[which(subject_data$Electrode == 'AF8') ,      5] - subject_data[which(subject_data$Electrode=='AF7') ,      5])
  asymmetry_F2_F1   <- c(asymmetry_F2_F1  , subject_data[which(subject_data$Electrode == 'E076-F2a') , 5] - subject_data[which(subject_data$Electrode=='E089-F1a') , 5])
  asymmetry_F4_F3   <- c(asymmetry_F4_F3  , subject_data[which(subject_data$Electrode == 'E068-F4a') , 5] - subject_data[which(subject_data$Electrode=='E100-F3a') , 5])
  asymmetry_F6_F5   <- c(asymmetry_F6_F5  , subject_data[which(subject_data$Electrode == 'E069-F6a') , 5] - subject_data[which(subject_data$Electrode=='E101-F5a') , 5])
  asymmetry_F8_F7   <- c(asymmetry_F8_F7  , subject_data[which(subject_data$Electrode == 'F8')  ,      5] - subject_data[which(subject_data$Electrode=='F7')  ,      5])
  asymmetry_FC2_FC1 <- c(asymmetry_FC2_FC1, subject_data[which(subject_data$Electrode == 'E075-FC2a'), 5] - subject_data[which(subject_data$Electrode=='E088-FC1a'), 5])
}
alpha_asymmetry_data <- data.frame(subj_block, asymmetry_Fp2_Fp1, asymmetry_AF4_AF3, asymmetry_AF8_AF7, asymmetry_F2_F1, asymmetry_F4_F3, asymmetry_F6_F5, asymmetry_F8_F7, asymmetry_FC2_FC1)
write.csv(alpha_asymmetry_data,  paste(data_dir, '/alpha_asymmetry_data_clean.csv', sep = ''),  row.names = FALSE)
```
# Spectral decomposition

```{r participants, fig.width = 12}
options(width = 100)
mytable <- xtabs(~ sex + belief, data = alpha_asymmetry_data)
ftable(addmargins(mytable))
```

- Infinity Reference or Reference Electrode Standardization Technique (REST).\
- 120 consecutive segments, 5 seconds each.\
- PSD computed with Welch's method.

## Scalp Map, mean power 9-11 Hz
![](scalp_resting_pemycrep_psd.png)

## PSD topography, 1 to 55 Hz, grand average
![](topo_resting_pemycrep_psd.png)

## PSD topography, 4 to 30 Hz, grand average
![](topo_4_to_30_Hz_resting_pemycrep_psd.png)

## Frontal Electrodes, 4 to 30 Hz, grand average
- 2 standard error bands
![Frontal electrodes](frontal_4_to_30_Hz_resting_pemycrep_psd.png)

# Parameterization of neural power spectra
- Each individual PSD is regarded as a combination of an aperiodic component and putative periodic oscillatory peaks.

## Individual Electrodes
![](../frontal_fooof_plots_by_electrode/S032VulnCrM19322811_psd_Fp1.png)
![](../frontal_fooof_plots_by_electrode/S032VulnCrM19322811_psd_Fp2.png)

## Individual Subject
![](../frontal_fooof_plots_by_subject/S032VulnCrM19322811_psd.png)

# General Description
```{r general, fig.width = 12}
options(width = 100)
summary(alpha_asymmetry_data)
asymmetry_pairs <- c('asymmetry_Fp2_Fp1', 'asymmetry_AF4_AF3', 'asymmetry_AF8_AF7', 'asymmetry_F2_F1', 'asymmetry_F4_F3', 'asymmetry_F6_F5', 'asymmetry_F8_F7', 'asymmetry_FC2_FC1')
asymmetry_pairs_pairs <- ggpairs(alpha_asymmetry_data,
                       columns = asymmetry_pairs,
                       aes(colour = sex, alpha = .25),
                       progress = FALSE,
                       lower = list(continuous = wrap("points")))
suppressWarnings(print(asymmetry_pairs_pairs))
```

# Alpha Asymmetry
## Fp2-Fp1 pair

```{r asymmetry_Fp2_Fp1, fig.width = 12}
options(width = 100)
alpha_asymmetry_rep_anova = aov_ez("Subject", "asymmetry_Fp2_Fp1", alpha_asymmetry_data, between = c("belief", "sex"))
mytable <- xtabs(~ sex + belief, data = alpha_asymmetry_rep_anova$data$long)
ftable(addmargins(mytable))
alpha_asymmetry_rain <- ggplot(alpha_asymmetry_rep_anova$data$long, aes(y = asymmetry_Fp2_Fp1, x = belief, color = sex, fill = sex)) +
  # ggtitle("alpha_asymmetry") +
  ylab("power") +
  stat_halfeye(
    trim   = FALSE, 
    adjust = 1, 
    .width = 0, 
    justification = -.15, 
    alpha  = .5,
    point_colour = NA) + 
  # theme(legend.position='none')
  # geom_boxplot(width = .15, alpha = .2, outlier.shape = NA) +
  geom_point(size = 2, alpha = .4, position = position_jitter(width = .05, height = 0)) 
suppressWarnings(print(alpha_asymmetry_rain))
alpha_asymmetry_afex_plot <-
  afex_plot(
    alpha_asymmetry_rep_anova,
    x = "belief",
    trace = "sex",
    error = "between",
    error_arg = list(width = .1),
    dodge = -.5,
    mapping = c("color"),
    point_arg = list(size = 4)
  )
suppressWarnings(print(alpha_asymmetry_afex_plot))
nice(alpha_asymmetry_rep_anova)
a_posteriori(alpha_asymmetry_rep_anova)
```

## AF4-AF3 pair

```{r asymmetry_AF4_AF3, fig.width = 12}
options(width = 100)
alpha_asymmetry_rep_anova = aov_ez("Subject", "asymmetry_AF4_AF3", alpha_asymmetry_data, between = c("belief", "sex"))
mytable <- xtabs(~ sex + belief, data = alpha_asymmetry_rep_anova$data$long)
ftable(addmargins(mytable))
alpha_asymmetry_rain <- ggplot(alpha_asymmetry_rep_anova$data$long, aes(y = asymmetry_AF4_AF3, x = belief, color = sex, fill = sex)) +
  # ggtitle("alpha_asymmetry") +
  ylab("power") +
  stat_halfeye(
    trim   = FALSE, 
    adjust = 1, 
    .width = 0, 
    justification = -.15, 
    alpha  = .5,
    point_colour = NA) + 
  # theme(legend.position='none')
  # geom_boxplot(width = .15, alpha = .2, outlier.shape = NA) +
  geom_point(size = 2, alpha = .4, position = position_jitter(width = .05, height = 0)) 
suppressWarnings(print(alpha_asymmetry_rain))
alpha_asymmetry_afex_plot <-
  afex_plot(
    alpha_asymmetry_rep_anova,
    x = "belief",
    trace = "sex",
    error = "between",
    error_arg = list(width = .1),
    dodge = -.5,
    mapping = c("color"),
    point_arg = list(size = 4)
  )
suppressWarnings(print(alpha_asymmetry_afex_plot))
nice(alpha_asymmetry_rep_anova)
a_posteriori(alpha_asymmetry_rep_anova)
```

## AF8-AF7 pair

```{r asymmetry_AF8_AF7, fig.width = 12}
options(width = 100)
alpha_asymmetry_rep_anova = aov_ez("Subject", "asymmetry_AF8_AF7", alpha_asymmetry_data, between = c("belief", "sex"))
mytable <- xtabs(~ sex + belief, data = alpha_asymmetry_rep_anova$data$long)
ftable(addmargins(mytable))
alpha_asymmetry_rain <- ggplot(alpha_asymmetry_rep_anova$data$long, aes(y = asymmetry_AF8_AF7, x = belief, color = sex, fill = sex)) +
  # ggtitle("alpha_asymmetry") +
  ylab("power") +
  stat_halfeye(
    trim   = FALSE, 
    adjust = 1, 
    .width = 0, 
    justification = -.15, 
    alpha  = .5,
    point_colour = NA) + 
  # theme(legend.position='none')
  # geom_boxplot(width = .15, alpha = .2, outlier.shape = NA) +
  geom_point(size = 2, alpha = .4, position = position_jitter(width = .05, height = 0)) 
suppressWarnings(print(alpha_asymmetry_rain))
alpha_asymmetry_afex_plot <-
  afex_plot(
    alpha_asymmetry_rep_anova,
    x = "belief",
    trace = "sex",
    error = "between",
    error_arg = list(width = .1),
    dodge = -.5,
    mapping = c("color"),
    point_arg = list(size = 4)
  )
suppressWarnings(print(alpha_asymmetry_afex_plot))
nice(alpha_asymmetry_rep_anova)
a_posteriori(alpha_asymmetry_rep_anova)
```
## F2-F1 pair

```{r asymmetry_F2_F1, fig.width = 12}
options(width = 100)
alpha_asymmetry_rep_anova = aov_ez("Subject", "asymmetry_F2_F1", alpha_asymmetry_data, between = c("belief", "sex"))
mytable <- xtabs(~ sex + belief, data = alpha_asymmetry_rep_anova$data$long)
ftable(addmargins(mytable))
alpha_asymmetry_rain <- ggplot(alpha_asymmetry_rep_anova$data$long, aes(y = asymmetry_F2_F1, x = belief, color = sex, fill = sex)) +
  # ggtitle("alpha_asymmetry") +
  ylab("power") +
  stat_halfeye(
    trim   = FALSE, 
    adjust = 1, 
    .width = 0, 
    justification = -.15, 
    alpha  = .5,
    point_colour = NA) + 
  # theme(legend.position='none')
  # geom_boxplot(width = .15, alpha = .2, outlier.shape = NA) +
  geom_point(size = 2, alpha = .4, position = position_jitter(width = .05, height = 0)) 
suppressWarnings(print(alpha_asymmetry_rain))
alpha_asymmetry_afex_plot <-
  afex_plot(
    alpha_asymmetry_rep_anova,
    x = "belief",
    trace = "sex",
    error = "between",
    error_arg = list(width = .1),
    dodge = -.5,
    mapping = c("color"),
    point_arg = list(size = 4)
  )
suppressWarnings(print(alpha_asymmetry_afex_plot))
nice(alpha_asymmetry_rep_anova)
a_posteriori(alpha_asymmetry_rep_anova)
```

## F4-F3 pair

```{r asymmetry_F4_F3, fig.width = 12}
options(width = 100)
alpha_asymmetry_rep_anova = aov_ez("Subject", "asymmetry_F4_F3", alpha_asymmetry_data, between = c("belief", "sex"))
mytable <- xtabs(~ sex + belief, data = alpha_asymmetry_rep_anova$data$long)
ftable(addmargins(mytable))
alpha_asymmetry_rain <- ggplot(alpha_asymmetry_rep_anova$data$long, aes(y = asymmetry_F4_F3, x = belief, color = sex, fill = sex)) +
  # ggtitle("alpha_asymmetry") +
  ylab("power") +
  stat_halfeye(
    trim   = FALSE, 
    adjust = 1, 
    .width = 0, 
    justification = -.15, 
    alpha  = .5,
    point_colour = NA) + 
  # theme(legend.position='none')
  # geom_boxplot(width = .15, alpha = .2, outlier.shape = NA) +
  geom_point(size = 2, alpha = .4, position = position_jitter(width = .05, height = 0)) 
suppressWarnings(print(alpha_asymmetry_rain))
alpha_asymmetry_afex_plot <-
  afex_plot(
    alpha_asymmetry_rep_anova,
    x = "belief",
    trace = "sex",
    error = "between",
    error_arg = list(width = .1),
    dodge = -.5,
    mapping = c("color"),
    point_arg = list(size = 4)
  )
suppressWarnings(print(alpha_asymmetry_afex_plot))
nice(alpha_asymmetry_rep_anova)
a_posteriori(alpha_asymmetry_rep_anova)
```

## F6-F5 pair

```{r asymmetry_F6_F5, fig.width = 12}
options(width = 100)
alpha_asymmetry_rep_anova = aov_ez("Subject", "asymmetry_F6_F5", alpha_asymmetry_data, between = c("belief", "sex"))
mytable <- xtabs(~ sex + belief, data = alpha_asymmetry_rep_anova$data$long)
ftable(addmargins(mytable))
alpha_asymmetry_rain <- ggplot(alpha_asymmetry_rep_anova$data$long, aes(y = asymmetry_F6_F5, x = belief, color = sex, fill = sex)) +
  # ggtitle("alpha_asymmetry") +
  ylab("power") +
  stat_halfeye(
    trim   = FALSE, 
    adjust = 1, 
    .width = 0, 
    justification = -.15, 
    alpha  = .5,
    point_colour = NA) + 
  # theme(legend.position='none')
  # geom_boxplot(width = .15, alpha = .2, outlier.shape = NA) +
  geom_point(size = 2, alpha = .4, position = position_jitter(width = .05, height = 0)) 
suppressWarnings(print(alpha_asymmetry_rain))
alpha_asymmetry_afex_plot <-
  afex_plot(
    alpha_asymmetry_rep_anova,
    x = "belief",
    trace = "sex",
    error = "between",
    error_arg = list(width = .1),
    dodge = -.5,
    mapping = c("color"),
    point_arg = list(size = 4)
  )
suppressWarnings(print(alpha_asymmetry_afex_plot))
nice(alpha_asymmetry_rep_anova)
a_posteriori(alpha_asymmetry_rep_anova)
```

## F8-F7 pair

```{r aasymmetry_F8_F7, fig.width = 12}
options(width = 100)
alpha_asymmetry_rep_anova = aov_ez("Subject", "asymmetry_F8_F7", alpha_asymmetry_data, between = c("belief", "sex"))
mytable <- xtabs(~ sex + belief, data = alpha_asymmetry_rep_anova$data$long)
ftable(addmargins(mytable))
alpha_asymmetry_rain <- ggplot(alpha_asymmetry_rep_anova$data$long, aes(y = asymmetry_F8_F7, x = belief, color = sex, fill = sex)) +
  # ggtitle("alpha_asymmetry") +
  ylab("power") +
  stat_halfeye(
    trim   = FALSE, 
    adjust = 1, 
    .width = 0, 
    justification = -.15, 
    alpha  = .5,
    point_colour = NA) + 
  # theme(legend.position='none')
  # geom_boxplot(width = .15, alpha = .2, outlier.shape = NA) +
  geom_point(size = 2, alpha = .4, position = position_jitter(width = .05, height = 0)) 
suppressWarnings(print(alpha_asymmetry_rain))
alpha_asymmetry_afex_plot <-
  afex_plot(
    alpha_asymmetry_rep_anova,
    x = "belief",
    trace = "sex",
    error = "between",
    error_arg = list(width = .1),
    dodge = -.5,
    mapping = c("color"),
    point_arg = list(size = 4)
  )
suppressWarnings(print(alpha_asymmetry_afex_plot))
nice(alpha_asymmetry_rep_anova)
a_posteriori(alpha_asymmetry_rep_anova)
```

## FC2-FC1 pair

```{r asymmetry_FC2_FC1, fig.width = 12}
options(width = 100)
alpha_asymmetry_rep_anova = aov_ez("Subject", "asymmetry_FC2_FC1", alpha_asymmetry_data, between = c("belief", "sex"))
mytable <- xtabs(~ sex + belief, data = alpha_asymmetry_rep_anova$data$long)
ftable(addmargins(mytable))
alpha_asymmetry_rain <- ggplot(alpha_asymmetry_rep_anova$data$long, aes(y = asymmetry_FC2_FC1, x = belief, color = sex, fill = sex)) +
  # ggtitle("alpha_asymmetry") +
  ylab("power") +
  stat_halfeye(
    trim   = FALSE, 
    adjust = 1, 
    .width = 0, 
    justification = -.15, 
    alpha  = .5,
    point_colour = NA) + 
  # theme(legend.position='none')
  # geom_boxplot(width = .15, alpha = .2, outlier.shape = NA) +
  geom_point(size = 2, alpha = .4, position = position_jitter(width = .05, height = 0)) 
suppressWarnings(print(alpha_asymmetry_rain))
alpha_asymmetry_afex_plot <-
  afex_plot(
    alpha_asymmetry_rep_anova,
    x = "belief",
    trace = "sex",
    error = "between",
    error_arg = list(width = .1),
    dodge = -.5,
    mapping = c("color"),
    point_arg = list(size = 4)
  )
suppressWarnings(print(alpha_asymmetry_afex_plot))
nice(alpha_asymmetry_rep_anova)
a_posteriori(alpha_asymmetry_rep_anova)
```

# Alpha peak parameters and aperiodic activity

```{r peak_params, fig.width = 12}
options(width = 100)
summary(alpha_power_data)
spec_params <- c('Amplitude', 'Frequency', 'Width', 'Offset', 'Slope')
spec_params_pairs <- ggpairs(alpha_power_data,
                       columns = spec_params,
                       aes(colour = vulnerability, alpha = .25),
                       progress = FALSE,
                       lower = list(continuous = wrap("points")))
suppressWarnings(print(spec_params_pairs))
```

## Alpha Power

```{r alpha_power, fig.width = 12}
options(width = 100)
alpha_param_rep_anova = aov_ez("Subject", "Amplitude", alpha_power_data, between = c("belief", "sex"), within = c("hemisphere", "electrode_pair"))
mytable <- xtabs(~ sex + belief, data = alpha_param_rep_anova$data$long) / length(levels(alpha_param_rep_anova$data$long$electrode_pair)) / length(levels(alpha_param_rep_anova$data$long$hemisphere))
ftable(addmargins(mytable))
alpha_param_rain <- ggplot(alpha_param_rep_anova$data$long, aes(y = Amplitude, x = belief, color = sex, fill = sex)) +
  stat_halfeye(
    trim   = FALSE, 
    adjust = 1, 
    .width = 0, 
    justification = -.15, 
    alpha  = .5,
    point_colour = NA) + 
  geom_point(size = 2, alpha = .4, position = position_jitter(width = .05, height = 0)) 
suppressWarnings(print(alpha_param_rain))
alpha_param_afex_plot <-
  afex_plot(
    alpha_param_rep_anova,
    x = "belief",
    trace = "sex",
    panel = "hemisphere",
    error = "between",
    error_arg = list(width = .1),
    dodge = -.5,
    mapping = c("color"),
    point_arg = list(size = 4)
  )
suppressWarnings(print(alpha_param_afex_plot))
nice(alpha_param_rep_anova)
a_posteriori(alpha_param_rep_anova)
```

## Alpha Frequency

```{r alpha_frequency, fig.width = 12}
options(width = 100)
alpha_param_rep_anova = aov_ez("Subject", "Frequency", alpha_power_data, between = c("belief", "sex"), within = c("hemisphere", "electrode_pair"))
mytable <- xtabs(~ sex + belief, data = alpha_param_rep_anova$data$long) / length(levels(alpha_param_rep_anova$data$long$electrode_pair)) / length(levels(alpha_param_rep_anova$data$long$hemisphere))
ftable(addmargins(mytable))
alpha_param_rain <- ggplot(alpha_param_rep_anova$data$long, aes(y = Frequency, x = belief, color = sex, fill = sex)) +
  stat_halfeye(
    trim   = FALSE, 
    adjust = 1, 
    .width = 0, 
    justification = -.15, 
    alpha  = .5,
    point_colour = NA) + 
  geom_point(size = 2, alpha = .4, position = position_jitter(width = .05, height = 0)) 
suppressWarnings(print(alpha_param_rain))
alpha_param_afex_plot <-
  afex_plot(
    alpha_param_rep_anova,
    x = "belief",
    trace = "sex",
    panel = "hemisphere",
    error = "between",
    error_arg = list(width = .1),
    dodge = -.5,
    mapping = c("color"),
    point_arg = list(size = 4)
  )
suppressWarnings(print(alpha_param_afex_plot))
nice(alpha_param_rep_anova)
a_posteriori(alpha_param_rep_anova)
```
## Alpha Bandwidth

```{r alpha_bandwidth, fig.width = 12}
options(width = 100)
alpha_param_rep_anova = aov_ez("Subject", "Width", alpha_power_data, between = c("belief", "sex"), within = c("hemisphere", "electrode_pair"))
mytable <- xtabs(~ sex + belief, data = alpha_param_rep_anova$data$long) / length(levels(alpha_param_rep_anova$data$long$electrode_pair)) / length(levels(alpha_param_rep_anova$data$long$hemisphere))
ftable(addmargins(mytable))
alpha_param_rain <- ggplot(alpha_param_rep_anova$data$long, aes(y = Width, x = belief, color = sex, fill = sex)) +
  stat_halfeye(
    trim   = FALSE, 
    adjust = 1, 
    .width = 0, 
    justification = -.15, 
    alpha  = .5,
    point_colour = NA) + 
  geom_point(size = 2, alpha = .4, position = position_jitter(width = .05, height = 0)) 
suppressWarnings(print(alpha_param_rain))
alpha_param_afex_plot <-
  afex_plot(
    alpha_param_rep_anova,
    x = "belief",
    trace = "sex",
    panel = "hemisphere",
    error = "between",
    error_arg = list(width = .1),
    dodge = -.5,
    mapping = c("color"),
    point_arg = list(size = 4)
  )
suppressWarnings(print(alpha_param_afex_plot))
nice(alpha_param_rep_anova)
a_posteriori(alpha_param_rep_anova)
```

## Aperiodic Offset

```{r aperiodic_offset, fig.width = 12}
options(width = 100)
aperiodic_offset_rep_anova = aov_ez("Subject", "Offset", alpha_power_data, between = c("belief", "sex"), within = c("hemisphere", "electrode_pair"))
mytable <- xtabs(~ sex + belief, data = aperiodic_offset_rep_anova$data$long) / length(levels(aperiodic_offset_rep_anova$data$long$electrode_pair)) / length(levels(aperiodic_offset_rep_anova$data$long$hemisphere))
ftable(addmargins(mytable))
aperiodic_param_rain <- ggplot(aperiodic_offset_rep_anova$data$long, aes(y = Offset, x = belief, color = sex, fill = sex)) +
  stat_halfeye(
    trim   = FALSE, 
    adjust = 1, 
    .width = 0, 
    justification = -.15, 
    alpha  = .5,
    point_colour = NA) + 
  geom_point(size = 2, alpha = .4, position = position_jitter(width = .05, height = 0)) 
suppressWarnings(print(aperiodic_param_rain))
aperiodic_param_afex_plot <-
  afex_plot(
    aperiodic_offset_rep_anova,
    x = "belief",
    trace = "sex",
    panel = "hemisphere",
    error = "between",
    error_arg = list(width = .1),
    dodge = -.5,
    mapping = c("color"),
    point_arg = list(size = 4)
  )
suppressWarnings(print(aperiodic_param_afex_plot))
nice(aperiodic_offset_rep_anova)
a_posteriori(aperiodic_offset_rep_anova)
```

## Aperiodic Slope

```{r aperiodic_slope, fig.width = 12}
options(width = 100)
aperiodic_slope_rep_anova = aov_ez("Subject", "Slope", alpha_power_data, between = c("belief", "sex"), within = c("hemisphere", "electrode_pair"))
mytable <- xtabs(~ sex + belief, data = aperiodic_slope_rep_anova$data$long) / length(levels(aperiodic_slope_rep_anova$data$long$electrode_pair)) / length(levels(aperiodic_slope_rep_anova$data$long$hemisphere))
ftable(addmargins(mytable))
aperiodic_param_rain <- ggplot(aperiodic_slope_rep_anova$data$long, aes(y = Slope, x = belief, color = sex, fill = sex)) +
  stat_halfeye(
    trim   = FALSE, 
    adjust = 1, 
    .width = 0, 
    justification = -.15, 
    alpha  = .5,
    point_colour = NA) + 
  geom_point(size = 2, alpha = .4, position = position_jitter(width = .05, height = 0)) 
suppressWarnings(print(aperiodic_param_rain))
aperiodic_param_afex_plot <-
  afex_plot(
    aperiodic_slope_rep_anova,
    x = "belief",
    trace = "sex",
    panel = "hemisphere",
    error = "between",
    error_arg = list(width = .1),
    dodge = -.5,
    mapping = c("color"),
    point_arg = list(size = 4)
  )
suppressWarnings(print(aperiodic_param_afex_plot))
nice(aperiodic_slope_rep_anova)
a_posteriori(aperiodic_slope_rep_anova)
```
