Alpha power estimated by averaging between 2 frequencies (the shitty way)

Anaie ena enemiegensgshjs…

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)

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"))
    }
  }
}

master_dir                 <- '~/Insync/Drive/00EEG/Proyectos/Huepe/fdcyt_2017/resting_huepe'
data_dir                   <- paste(master_dir, 'FAA_results',  sep = '/')
alpha_power_data_name      <- paste(data_dir, 'average_alpha_power_9_to_11_resting_3_times.txt', sep='/')
alpha_power_data           <- read.table(alpha_power_data_name, header = TRUE, strip.white = TRUE, sep = "\t")
names(alpha_power_data)[names(alpha_power_data) == 'chlabel'] <- 'Electrode'
names(alpha_power_data)[names(alpha_power_data) == 'ERPset']  <- 'Dataset'
names(alpha_power_data)[names(alpha_power_data) == 'binlabel']  <- 'Period'
alpha_power_data$Subject   <- as.numeric(gsub(".*?([0-9]+).*", "\\1", alpha_power_data$Dataset))
alpha_power_data$log10_uvolts <- log10(alpha_power_data$value)
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$Period    <- factor(alpha_power_data$Period, levels = c("beginning", "middle", "end"))
alpha_power_data$hemisphere[alpha_power_data$Electrode %in% c('E093-Fp1',  'E092-AF3a',  'E094-AF7',  'E089-F1a',  'E100-F3a',  'E101-F5a',  'E103-F7',  'E088-FC1a')] <- 'Left'
alpha_power_data$hemisphere[alpha_power_data$Electrode %in% c('E080-Fp2',  'E079-AF4a',  'E072-AF8',  'E076-F2a',  'E068-F4a',  'E069-F6a',  'E071-F8',  'E075-FC2a')] <- 'Right'
alpha_power_data$hemisphere <- factor(alpha_power_data$hemisphere)
alpha_power_data$electrode_pair[alpha_power_data$Electrode %in% c('E093-Fp1' , 'E080-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('E094-AF7' , 'E072-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('E103-F7'  , 'E071-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'))
group_id   <- read.table("/home/alvaro/Insync/Drive/00EEG/Proyectos/Huepe/fdcyt_2017/Registro-Evaluaciones-FDCYT-DH-2017 - General ANONIMO.csv",
                         sep = ",", header = TRUE, col.names = c("full.id", "Subject", "Sex", "Group", "Stress"))
group_id$Sex           <- factor(group_id$Sex)
group_id$Group         <- factor(group_id$Group)
levels(group_id$Sex)   <- list(female  = "F", male  = "M")
levels(group_id$Group) <- list(invulnerable  = "CN", vulnerable  = "EX")
group_id               <- group_id[c('Subject', 'Group', 'Sex')]
alpha_power_data       <- merge(alpha_power_data, group_id, by = 'Subject')
write.csv(alpha_power_data,  paste(data_dir, '/alpha_power_data_clean_old(bad)_style.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()
Subject           <- c()
Period            <- c()
subjectos <- levels(alpha_power_data$Subject)
periodos  <- levels(alpha_power_data$Period)
for (subj in subjectos) {
  for (part in periodos) {
    subject_data <- subset(alpha_power_data, Subject == subj & Period == part)
    Subject           <- c(Subject, as.character(subject_data$Subject[1]))
    Period            <- c(Period, as.character(subject_data$Period[1]))
    asymmetry_Fp2_Fp1 <- c(asymmetry_Fp2_Fp1, subject_data[which(subject_data$Electrode == 'E080-Fp2') , 10] - subject_data[which(subject_data$Electrode=='E093-Fp1') , 10])
    asymmetry_AF4_AF3 <- c(asymmetry_AF4_AF3, subject_data[which(subject_data$Electrode == 'E079-AF4a'), 10] - subject_data[which(subject_data$Electrode=='E092-AF3a'), 10])
    asymmetry_AF8_AF7 <- c(asymmetry_AF8_AF7, subject_data[which(subject_data$Electrode == 'E072-AF8') , 10] - subject_data[which(subject_data$Electrode=='E094-AF7') , 10])
    asymmetry_F2_F1   <- c(asymmetry_F2_F1  , subject_data[which(subject_data$Electrode == 'E076-F2a') , 10] - subject_data[which(subject_data$Electrode=='E089-F1a') , 10])
    asymmetry_F4_F3   <- c(asymmetry_F4_F3  , subject_data[which(subject_data$Electrode == 'E068-F4a') , 10] - subject_data[which(subject_data$Electrode=='E100-F3a') , 10])
    asymmetry_F6_F5   <- c(asymmetry_F6_F5  , subject_data[which(subject_data$Electrode == 'E069-F6a') , 10] - subject_data[which(subject_data$Electrode=='E101-F5a') , 10])
    asymmetry_F8_F7   <- c(asymmetry_F8_F7  , subject_data[which(subject_data$Electrode == 'E071-F8')  , 10] - subject_data[which(subject_data$Electrode=='E103-F7')  , 10])
    asymmetry_FC2_FC1 <- c(asymmetry_FC2_FC1, subject_data[which(subject_data$Electrode == 'E075-FC2a'), 10] - subject_data[which(subject_data$Electrode=='E088-FC1a'), 10])
  }
}
alpha_asymmetry_data <- data.frame(Subject, Period, asymmetry_Fp2_Fp1, asymmetry_AF4_AF3, asymmetry_AF8_AF7, asymmetry_F2_F1, asymmetry_F4_F3, asymmetry_F6_F5, asymmetry_F8_F7, asymmetry_FC2_FC1)
alpha_asymmetry_data$Subject <- factor(alpha_asymmetry_data$Subject)
alpha_asymmetry_data$Period <- factor(alpha_asymmetry_data$Period, levels = c("beginning", "middle", "end"))
alpha_asymmetry_data <- merge(alpha_asymmetry_data, group_id, by = 'Subject')
write.csv(alpha_asymmetry_data,  paste(data_dir, '/alpha_asymmetry_data_clean_old(bad)_style.csv', sep = ''),  row.names = FALSE)

1 Spectral decomposition

• Infinity Reference or Reference Electrode Standardization Technique (REST).
  
• 120 consecutive segments, 5 seconds each.
  
• 3 Periods, 40 segments each.
  
• PSD computed with Welch’s method.

1.1 Scalp Map, mean power [9.0 11.0] 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 General Description

options(width = 100)
summary(alpha_asymmetry_data)

    Subject          Period   asymmetry_Fp2_Fp1   asymmetry_AF4_AF3   asymmetry_AF8_AF7  
 1      :  3   beginning:77   Min.   :-0.310444   Min.   :-0.287329   Min.   :-0.557232  
 10     :  3   middle   :77   1st Qu.:-0.035430   1st Qu.:-0.037404   1st Qu.:-0.069376  
 11     :  3   end      :77   Median : 0.008193   Median : 0.002536   Median : 0.003581  
 12     :  3                  Mean   : 0.023631   Mean   : 0.004174   Mean   : 0.004315  
 13     :  3                  3rd Qu.: 0.059022   3rd Qu.: 0.039573   3rd Qu.: 0.086802  
 14     :  3                  Max.   : 0.946861   Max.   : 0.420243   Max.   : 1.284300  
 (Other):213                                                                             
 asymmetry_F2_F1     asymmetry_F4_F3     asymmetry_F6_F5     asymmetry_F8_F7    asymmetry_FC2_FC1 
 Min.   :-0.148967   Min.   :-0.379484   Min.   :-0.354521   Min.   :-0.56596   Min.   :-0.46405  
 1st Qu.:-0.022153   1st Qu.:-0.064552   1st Qu.:-0.060480   1st Qu.:-0.10231   1st Qu.:-0.02340  
 Median : 0.007042   Median :-0.001131   Median : 0.002679   Median : 0.00198   Median : 0.01202  
 Mean   : 0.005953   Mean   :-0.005603   Mean   :-0.009089   Mean   :-0.01688   Mean   : 0.01307  
 3rd Qu.: 0.028496   3rd Qu.: 0.058105   3rd Qu.: 0.062659   3rd Qu.: 0.08143   3rd Qu.: 0.05438  
 Max.   : 0.170082   Max.   : 0.276392   Max.   : 0.291835   Max.   : 0.93128   Max.   : 0.35410  
                                                                                                  
          Group         Sex     
 invulnerable:117   female:129  
 vulnerable  :114   male  :102  
                                
                                
                                
                                
                                

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 = Period, alpha = .25),
                       progress = FALSE,
                       lower = list(continuous = wrap("points")))
suppressWarnings(print(asymmetry_pairs_pairs))

3 Alpha Asymmetry, [9.0 11.0] Hz

3.1 Fp2-Fp1 pair

options(width = 100)
alpha_asymmetry_rep_anova = aov_ez("Subject", "asymmetry_Fp2_Fp1", alpha_asymmetry_data, between = c("Group", "Sex"), within = "Period")

Contrasts set to contr.sum for the following variables: Group, Sex

mytable <- xtabs(~ Group + Sex, data = alpha_asymmetry_rep_anova$data$long) / length(unique(alpha_asymmetry_data$Period))
ftable(addmargins(mytable))

             Sex female male Sum
Group                           
invulnerable         19   20  39
vulnerable           24   14  38
Sum                  43   34  77

alpha_asymmetry_rain <- ggplot(alpha_asymmetry_rep_anova$data$long, aes(y = asymmetry_Fp2_Fp1, x = Group, 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 = "Period",
    trace = "Sex",
    panel = "Group",
    error = "between",
    error_arg = list(width = .1),
    dodge = -.5,
    mapping = c("color"),
    point_arg = list(size = 4)
  )

Warning: Panel(s) show a mixed within-between-design.
Error bars do not allow comparisons across all means.
Suppress error bars with: error = "none"

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            Group        1, 73 0.04 0.12  .001    .731
2              Sex        1, 73 0.04 0.60  .006    .443
3        Group:Sex        1, 73 0.04 2.06  .021    .156
4           Period 1.98, 144.52 0.01 0.27 <.001    .765
5     Group:Period 1.98, 144.52 0.01 1.52  .005    .222
6       Sex:Period 1.98, 144.52 0.01 1.93  .006    .149
7 Group:Sex:Period 1.98, 144.52 0.01 0.11 <.001    .891
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘+’ 0.1 ‘ ’ 1

Sphericity correction method: GG 

a_posteriori(alpha_asymmetry_rep_anova)

3.2 AF4-AF3 pair

options(width = 100)
alpha_asymmetry_rep_anova = aov_ez("Subject", "asymmetry_AF4_AF3", alpha_asymmetry_data, between = c("Group", "Sex"), within = "Period")

Contrasts set to contr.sum for the following variables: Group, Sex

mytable <- xtabs(~ Group + Sex, data = alpha_asymmetry_rep_anova$data$long) / length(unique(alpha_asymmetry_data$Period))
ftable(addmargins(mytable))

             Sex female male Sum
Group                           
invulnerable         19   20  39
vulnerable           24   14  38
Sum                  43   34  77

alpha_asymmetry_rain <- ggplot(alpha_asymmetry_rep_anova$data$long, aes(y = asymmetry_AF4_AF3, x = Group, 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 = "Period",
    trace = "Sex",
    panel = "Group",
    error = "between",
    error_arg = list(width = .1),
    dodge = -.5,
    mapping = c("color"),
    point_arg = list(size = 4)
  )

Warning: Panel(s) show a mixed within-between-design.
Error bars do not allow comparisons across all means.
Suppress error bars with: error = "none"

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            Group        1, 73 0.03 2.88 +  .035    .094
2              Sex        1, 73 0.03   1.54  .019    .218
3        Group:Sex        1, 73 0.03   1.23  .015    .271
4           Period 1.91, 139.76 0.00   0.54 <.001    .578
5     Group:Period 1.91, 139.76 0.00   1.06  .001    .348
6       Sex:Period 1.91, 139.76 0.00   0.77 <.001    .458
7 Group:Sex:Period 1.91, 139.76 0.00   0.10 <.001    .893
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘+’ 0.1 ‘ ’ 1

Sphericity correction method: GG 

a_posteriori(alpha_asymmetry_rep_anova)

3.3 AF8-AF7 pair

options(width = 100)
alpha_asymmetry_rep_anova = aov_ez("Subject", "asymmetry_AF8_AF7", alpha_asymmetry_data, between = c("Group", "Sex"), within = "Period")

Contrasts set to contr.sum for the following variables: Group, Sex

mytable <- xtabs(~ Group + Sex, data = alpha_asymmetry_rep_anova$data$long) / length(unique(alpha_asymmetry_data$Period))
ftable(addmargins(mytable))

             Sex female male Sum
Group                           
invulnerable         19   20  39
vulnerable           24   14  38
Sum                  43   34  77

alpha_asymmetry_rain <- ggplot(alpha_asymmetry_rep_anova$data$long, aes(y = asymmetry_AF8_AF7, x = Group, 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 = "Period",
    trace = "Sex",
    panel = "Group",
    error = "between",
    error_arg = list(width = .1),
    dodge = -.5,
    mapping = c("color"),
    point_arg = list(size = 4)
  )

Warning: Panel(s) show a mixed within-between-design.
Error bars do not allow comparisons across all means.
Suppress error bars with: error = "none"

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            Group        1, 73 0.12   2.08  .026    .154
2              Sex        1, 73 0.12 2.80 +  .034    .099
3        Group:Sex        1, 73 0.12   0.02 <.001    .878
4           Period 1.52, 111.05 0.01   1.35  .001    .260
5     Group:Period 1.52, 111.05 0.01   1.57  .002    .216
6       Sex:Period 1.52, 111.05 0.01   1.38  .001    .254
7 Group:Sex:Period 1.52, 111.05 0.01   0.17 <.001    .786
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘+’ 0.1 ‘ ’ 1

Sphericity correction method: GG 

a_posteriori(alpha_asymmetry_rep_anova)

3.4 F2-F1 pair

options(width = 100)
alpha_asymmetry_rep_anova = aov_ez("Subject", "asymmetry_F2_F1", alpha_asymmetry_data, between = c("Group", "Sex"), within = "Period")

Contrasts set to contr.sum for the following variables: Group, Sex

mytable <- xtabs(~ Group + Sex, data = alpha_asymmetry_rep_anova$data$long) / length(unique(alpha_asymmetry_data$Period))
ftable(addmargins(mytable))

             Sex female male Sum
Group                           
invulnerable         19   20  39
vulnerable           24   14  38
Sum                  43   34  77

alpha_asymmetry_rain <- ggplot(alpha_asymmetry_rep_anova$data$long, aes(y = asymmetry_F2_F1, x = Group, 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 = "Period",
    trace = "Sex",
    panel = "Group",
    error = "between",
    error_arg = list(width = .1),
    dodge = -.5,
    mapping = c("color"),
    point_arg = list(size = 4)
  )

Warning: Panel(s) show a mixed within-between-design.
Error bars do not allow comparisons across all means.
Suppress error bars with: error = "none"

suppressWarnings(print(alpha_asymmetry_afex_plot))

nice(alpha_asymmetry_rep_anova)

Anova Table (Type 3 tests)

Response: asymmetry_F2_F1
            Effect           df  MSE    F   ges p.value
1            Group        1, 73 0.01 2.44  .030    .122
2              Sex        1, 73 0.01 0.85  .011    .360
3        Group:Sex        1, 73 0.01 0.73  .009    .396
4           Period 1.70, 123.80 0.00 1.83  .002    .171
5     Group:Period 1.70, 123.80 0.00 0.29 <.001    .714
6       Sex:Period 1.70, 123.80 0.00 0.26 <.001    .738
7 Group:Sex:Period 1.70, 123.80 0.00 1.98  .002    .149
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘+’ 0.1 ‘ ’ 1

Sphericity correction method: GG 

a_posteriori(alpha_asymmetry_rep_anova)

3.5 F4-F3 pair

options(width = 100)
alpha_asymmetry_rep_anova = aov_ez("Subject", "asymmetry_F4_F3", alpha_asymmetry_data, between = c("Group", "Sex"), within = "Period")

Contrasts set to contr.sum for the following variables: Group, Sex

mytable <- xtabs(~ Group + Sex, data = alpha_asymmetry_rep_anova$data$long) / length(unique(alpha_asymmetry_data$Period))
ftable(addmargins(mytable))

             Sex female male Sum
Group                           
invulnerable         19   20  39
vulnerable           24   14  38
Sum                  43   34  77

alpha_asymmetry_rain <- ggplot(alpha_asymmetry_rep_anova$data$long, aes(y = asymmetry_F4_F3, x = Group, 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 = "Period",
    trace = "Sex",
    panel = "Group",
    error = "between",
    error_arg = list(width = .1),
    dodge = -.5,
    mapping = c("color"),
    point_arg = list(size = 4)
  )

Warning: Panel(s) show a mixed within-between-design.
Error bars do not allow comparisons across all means.
Suppress error bars with: error = "none"

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            Group        1, 73 0.03 2.48  .031    .119
2              Sex        1, 73 0.03 1.07  .014    .303
3        Group:Sex        1, 73 0.03 0.22  .003    .642
4           Period 1.96, 142.80 0.00 0.97 <.001    .381
5     Group:Period 1.96, 142.80 0.00 0.62 <.001    .535
6       Sex:Period 1.96, 142.80 0.00 0.42 <.001    .656
7 Group:Sex:Period 1.96, 142.80 0.00 2.21  .002    .114
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘+’ 0.1 ‘ ’ 1

Sphericity correction method: GG 

a_posteriori(alpha_asymmetry_rep_anova)

3.6 F6-F5 pair

options(width = 100)
alpha_asymmetry_rep_anova = aov_ez("Subject", "asymmetry_F6_F5", alpha_asymmetry_data, between = c("Group", "Sex"), within = "Period")

Contrasts set to contr.sum for the following variables: Group, Sex

mytable <- xtabs(~ Group + Sex, data = alpha_asymmetry_rep_anova$data$long) / length(unique(alpha_asymmetry_data$Period))
ftable(addmargins(mytable))

             Sex female male Sum
Group                           
invulnerable         19   20  39
vulnerable           24   14  38
Sum                  43   34  77

alpha_asymmetry_rain <- ggplot(alpha_asymmetry_rep_anova$data$long, aes(y = asymmetry_F6_F5, x = Group, 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 = "Period",
    trace = "Sex",
    panel = "Group",
    error = "between",
    error_arg = list(width = .1),
    dodge = -.5,
    mapping = c("color"),
    point_arg = list(size = 4)
  )

Warning: Panel(s) show a mixed within-between-design.
Error bars do not allow comparisons across all means.
Suppress error bars with: error = "none"

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            Group        1, 73 0.04   1.54  .019    .219
2              Sex        1, 73 0.04 6.03 *  .071    .016
3        Group:Sex        1, 73 0.04   0.10  .001    .756
4           Period 1.88, 137.60 0.00   2.19  .002    .119
5     Group:Period 1.88, 137.60 0.00   0.00 <.001    .995
6       Sex:Period 1.88, 137.60 0.00   0.06 <.001    .933
7 Group:Sex:Period 1.88, 137.60 0.00   1.98  .002    .144
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘+’ 0.1 ‘ ’ 1

Sphericity correction method: GG 

a_posteriori(alpha_asymmetry_rep_anova)

NOTE: Results may be misleading due to involvement in interactions

$emmeans
 Sex     emmean     SE df lower.CL upper.CL
 female -0.0392 0.0181 73  -0.0753 -0.00309
 male    0.0281 0.0205 73  -0.0129  0.06902

Results are averaged over the levels of: Group, Period 
Confidence level used: 0.95 

$contrasts
 contrast      estimate     SE df t.ratio p.value
 female - male  -0.0672 0.0274 73  -2.456  0.0164

Results are averaged over the levels of: Group, Period 

3.7 F8-F7 pair

options(width = 100)
alpha_asymmetry_rep_anova = aov_ez("Subject", "asymmetry_F8_F7", alpha_asymmetry_data, between = c("Group", "Sex"), within = "Period")

Contrasts set to contr.sum for the following variables: Group, Sex

mytable <- xtabs(~ Group + Sex, data = alpha_asymmetry_rep_anova$data$long) / length(unique(alpha_asymmetry_data$Period))
ftable(addmargins(mytable))

             Sex female male Sum
Group                           
invulnerable         19   20  39
vulnerable           24   14  38
Sum                  43   34  77

alpha_asymmetry_rain <- ggplot(alpha_asymmetry_rep_anova$data$long, aes(y = asymmetry_F8_F7, x = Group, 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 = "Period",
    trace = "Sex",
    panel = "Group",
    error = "between",
    error_arg = list(width = .1),
    dodge = -.5,
    mapping = c("color"),
    point_arg = list(size = 4)
  )

Warning: Panel(s) show a mixed within-between-design.
Error bars do not allow comparisons across all means.
Suppress error bars with: error = "none"

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            Group        1, 73 0.09    0.17  .002    .684
2              Sex        1, 73 0.09 7.89 **  .091    .006
3        Group:Sex        1, 73 0.09    1.55  .019    .217
4           Period 1.84, 134.49 0.00    1.51  .002    .225
5     Group:Period 1.84, 134.49 0.00    1.04  .001    .352
6       Sex:Period 1.84, 134.49 0.00    0.92 <.001    .395
7 Group:Sex:Period 1.84, 134.49 0.00    0.89 <.001    .406
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘+’ 0.1 ‘ ’ 1

Sphericity correction method: GG 

a_posteriori(alpha_asymmetry_rep_anova)

NOTE: Results may be misleading due to involvement in interactions

$emmeans
 Sex     emmean     SE df lower.CL upper.CL
 female -0.0620 0.0260 73  -0.1138  -0.0102
 male    0.0484 0.0295 73  -0.0104   0.1072

Results are averaged over the levels of: Group, Period 
Confidence level used: 0.95 

$contrasts
 contrast      estimate     SE df t.ratio p.value
 female - male    -0.11 0.0393 73  -2.808  0.0064

Results are averaged over the levels of: Group, Period 

3.8 FC2-FC1 pair

options(width = 100)
alpha_asymmetry_rep_anova = aov_ez("Subject", "asymmetry_FC2_FC1", alpha_asymmetry_data, between = c("Group", "Sex"), within = "Period")

Contrasts set to contr.sum for the following variables: Group, Sex

mytable <- xtabs(~ Group + Sex, data = alpha_asymmetry_rep_anova$data$long) / length(unique(alpha_asymmetry_data$Period))
ftable(addmargins(mytable))

             Sex female male Sum
Group                           
invulnerable         19   20  39
vulnerable           24   14  38
Sum                  43   34  77

alpha_asymmetry_rain <- ggplot(alpha_asymmetry_rep_anova$data$long, aes(y = asymmetry_FC2_FC1, x = Group, 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 = "Period",
    trace = "Sex",
    panel = "Group",
    error = "between",
    error_arg = list(width = .1),
    dodge = -.5,
    mapping = c("color"),
    point_arg = list(size = 4)
  )

Warning: Panel(s) show a mixed within-between-design.
Error bars do not allow comparisons across all means.
Suppress error bars with: error = "none"

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            Group        1, 73 0.02 0.00 <.001    .947
2              Sex        1, 73 0.02 0.63  .008    .430
3        Group:Sex        1, 73 0.02 0.13  .002    .721
4           Period 1.87, 136.61 0.00 0.07 <.001    .926
5     Group:Period 1.87, 136.61 0.00 1.31 <.001    .272
6       Sex:Period 1.87, 136.61 0.00 0.91 <.001    .398
7 Group:Sex:Period 1.87, 136.61 0.00 0.16 <.001    .837
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘+’ 0.1 ‘ ’ 1

Sphericity correction method: GG 

a_posteriori(alpha_asymmetry_rep_anova)