AAT N1 and P2 baq, personal choice

Reference to infinity.

cat("\014")     # clean terminal



rm(list = ls()) # clean workspace
try(dev.off(), silent = TRUE) # close all plots
library(tidyverse)
library(readxl)
library(afex)
library(emmeans)
library(GGally)
library(ggpubr)

my_dodge <- .5
exclude_bad_eeg <- TRUE
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) {
      cat(rep("_", 100), '\n', sep = "")
      print(emmeans(afex_aov, factors[[i]], contr = "pairwise"))
    }
  }
}

eeg_check <- read_excel(file.path('..', '..', 'bad_channels_bqd.xlsx'))
eeg_check <- eeg_check %>%
  mutate(badchan_num = ifelse(bad_channels == '0', 0, sapply(strsplit(bad_channels, " "), length)))
bad_eeg   <- eeg_check$file[eeg_check$to_kill == 1]
data_dir    <- file.path('..', 'results')
aat_n1_name <- file.path(data_dir, 'average_voltage_145_to_175_AAT_infinity_reference_and_personal_choice.txt')
aat_p2_name <- file.path(data_dir, 'average_voltage_210_to_250_AAT_infinity_reference_and_personal_choice.txt')
aat_n1_data <- read.table(aat_n1_name, header = TRUE, strip.white = TRUE, sep = "\t")
aat_p2_data <- read.table(aat_p2_name, header = TRUE, strip.white = TRUE, sep = "\t")
aat_data    <- rbind(aat_n1_data, aat_p2_data)
aat_data$worklat <- gsub(".0", "", aat_data$worklat, fixed = TRUE)
names(aat_data)[names(aat_data) == "mlabel"]   <- "component"
names(aat_data)[names(aat_data) == "value"]    <- "amplitude"
names(aat_data)[names(aat_data) == "binlabel"] <- "stimulus"
aat_data <- aat_data %>% separate(stimulus, c("cue","valence"), sep = "_", remove = FALSE)
aat_data$id_code <- gsub ('_Ctrl_infinity_reference_and_personal_choice', '', aat_data$ERPset)
aat_data$id_code <- gsub ('_Mind_infinity_reference_and_personal_choice', '', aat_data$id_code)
aat_data$num_id  <- readr::parse_number(aat_data$id_code)
aat_data <- aat_data %>% separate(id_code, c(NA, NA, "figure", "side"), sep = "_", remove = FALSE)
aat_data$group[grepl("Ctrl", aat_data$ERPset)] <- "Control"
aat_data$group[grepl("Mind", aat_data$ERPset)] <- "Mindfulness"
aat_data$anteroposterior[aat_data$chlabel %in% c('O1'  , 'Oz'  , 'O2'  )] <- 'Occipital'
aat_data$anteroposterior[aat_data$chlabel %in% c('E014', 'E024', 'E027')] <- 'SubOccipital'
aat_data$anteroposterior[aat_data$chlabel %in% c('E013', 'Iz'  , 'E026')] <- 'Inion'
aat_data$lateral[aat_data$chlabel %in% c('O1', 'E014', 'E013')] <- 'Left'
aat_data$lateral[aat_data$chlabel %in% c('Oz', 'E024', 'Iz'  )] <- 'Central'
aat_data$lateral[aat_data$chlabel %in% c('O2', 'E027', 'E026')] <- 'Right'
aat_data[sapply(aat_data, is.character)] <- lapply(aat_data[sapply(aat_data, is.character)], as.factor)
aat_data$lateral         <- factor(aat_data$lateral        , levels = c("Left", "Central", "Right"))
aat_data$anteroposterior <- factor(aat_data$anteroposterior, levels = c("Occipital", "SubOccipital", "Inion"))
aat_data$cue <- fct_rev(aat_data$cue)
if (exclude_bad_eeg) {
  aat_data <- aat_data[!(aat_data$id_code %in% bad_eeg), ]
  }
write.csv(aat_data,  file.path(data_dir, 'aat_data_clean_infinity_reference_and_personal_choice.csv'),  row.names = FALSE)

aat_n1_name_peak <- file.path(data_dir, 'peak_voltage_135_to_215_AAT_infinity_reference_and_personal_choice.txt')
aat_p2_name_peak <- file.path(data_dir, 'peak_voltage_215_to_285_AAT_infinity_reference_and_personal_choice.txt')
aat_n1_data_peak <- read.table(aat_n1_name_peak, header = TRUE, strip.white = TRUE, sep = "\t")
aat_p2_data_peak <- read.table(aat_p2_name_peak, header = TRUE, strip.white = TRUE, sep = "\t")
aat_data_peak    <- rbind(aat_n1_data_peak, aat_p2_data_peak) 
names(aat_data_peak)[names(aat_data_peak) == "worklat"]  <- "latency"
aat_data_peak$latency <- gsub('[', '', aat_data_peak$latency, fixed = TRUE)
aat_data_peak$latency <- gsub(']', '', aat_data_peak$latency, fixed = TRUE)
aat_data_peak$latency <- as.numeric(aat_data_peak$latency)
names(aat_data_peak)[names(aat_data_peak) == "mlabel"]   <- "component"
names(aat_data_peak)[names(aat_data_peak) == "value"]    <- "amplitude"
names(aat_data_peak)[names(aat_data_peak) == "binlabel"] <- "stimulus"
aat_data_peak <- aat_data_peak %>% separate(stimulus, c("cue","valence"), sep = "_", remove = FALSE)
aat_data_peak$id_code <- gsub ('_Ctrl_infinity_reference_and_personal_choice', '', aat_data_peak$ERPset)
aat_data_peak$id_code <- gsub ('_Mind_infinity_reference_and_personal_choice', '', aat_data_peak$id_code)
aat_data_peak$num_id  <- readr::parse_number(aat_data_peak$id_code)
aat_data_peak <- aat_data_peak %>% separate(id_code, c(NA, NA, "figure", "side"), sep = "_", remove = FALSE)
aat_data_peak$group[grepl("Ctrl", aat_data_peak$ERPset)] <- "Control"
aat_data_peak$group[grepl("Mind", aat_data_peak$ERPset)] <- "Mindfulness"
aat_data_peak <- aat_data_peak %>% separate(id_code, c(NA, NA, "figure", "side"), sep = "_", remove = FALSE)
names(aat_data_peak)[names(aat_data_peak) == "binlabel"] <- "stimulus"
aat_data_peak$num_id <- readr::parse_number(aat_data_peak$ERPset)
aat_data_peak$group[grepl("Ctrl", aat_data_peak$ERPset)] <- "Control"
aat_data_peak$group[grepl("Mind", aat_data_peak$ERPset)] <- "Mindfulness"
aat_data_peak <- aat_data_peak %>% separate(stimulus, c("cue","valence"), sep = "_", remove = FALSE)
aat_data_peak[sapply(aat_data_peak, is.character)] <- lapply(aat_data_peak[sapply(aat_data_peak, is.character)], as.factor)
aat_data_peak$cue <- fct_rev(aat_data_peak$cue)
if (exclude_bad_eeg) {
  aat_data <- aat_data[!(aat_data$id_code %in% bad_eeg), ]
  }
write.csv(aat_data_peak,  file.path(data_dir, 'aat_data_peak_clean_infinity_reference_and_personal_choice.csv'),  row.names = FALSE)

1 Participants

options(width = 100)
mytable <- xtabs(~ group + figure + side, data = aat_data) / length(unique(aat_data$chindex)) / length(unique(aat_data$bini)) / length(unique(aat_data$component))
ftable(addmargins(mytable))

                   side left right Sum
group       figure                    
Control     circ           7     8  15
            rect           7     8  15
            Sum           14    16  30
Mindfulness circ           9     6  15
            rect           8     7  15
            Sum           17    13  30
Sum         circ          16    14  30
            rect          15    15  30
            Sum           31    29  60

2 ERP plots

Dong, L., Li, F., Liu, Q., Wen, X., Lai, Y., Xu, P., & Yao, D. (2017). MATLAB Toolboxes for Reference Electrode Standardization Technique (REST) of Scalp EEG. Frontiers in Neuroscience, 11(OCT), 601. https://doi.org/10.3389/fnins.2017.00601

ggplot(eeg_check, aes(badchan_num)) + geom_histogram(bins = 13, color = 'darkblue', fill = 'lightblue') + scale_x_continuous(breaks=0:12)

2.1 Approach and Avoid:

2.2 Topographic layout:

3 General description

options(width = 100)
ggplot(
  aat_data, aes(x = amplitude, fill = component, color = component)) +
  geom_histogram(alpha = .3, position = "identity") +
  ggtitle("Average voltages")

`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

summary(aat_data)

       worklat     component   amplitude           chindex         chlabel          bini     
 [145  175]:2160   N1:2160   Min.   :-21.4284   Min.   :13.00   E013   : 480   Min.   :5.00  
 [210  250]:2160   P2:2160   1st Qu.: -0.5646   1st Qu.:15.00   E014   : 480   1st Qu.:5.75  
                             Median :  2.4152   Median :24.00   E024   : 480   Median :6.50  
                             Mean   :  2.2587   Mean   :21.67   E026   : 480   Mean   :6.50  
                             3rd Qu.:  5.3956   3rd Qu.:26.00   E027   : 480   3rd Qu.:7.25  
                             Max.   : 19.2828   Max.   :28.00   Iz     : 480   Max.   :8.00  
                                                                (Other):1440                 
                stimulus          cue             valence    
 Approach_Attractive:1080   Avoid   :2160   Attractive:2160  
 Approach_Neutral   :1080   Approach:2160   Neutral   :2160  
 Avoid_Attractive   :1080                                    
 Avoid_Neutral      :1080                                    
                                                             
                                                             
                                                             
                                                           ERPset                  id_code    
 S01_C1_circ_right_Ctrl_infinity_reference_and_personal_choice:  72   S01_C1_circ_right:  72  
 S02_C2_circ_left_Mind_infinity_reference_and_personal_choice :  72   S02_C2_circ_left :  72  
 S03_C1_rect_right_Ctrl_infinity_reference_and_personal_choice:  72   S03_C1_rect_right:  72  
 S04_C1_rect_left_Ctrl_infinity_reference_and_personal_choice :  72   S04_C1_rect_left :  72  
 S05_C1_circ_left_Ctrl_infinity_reference_and_personal_choice :  72   S05_C1_circ_left :  72  
 S06_C2_circ_left_Mind_infinity_reference_and_personal_choice :  72   S06_C2_circ_left :  72  
 (Other)                                                      :3888   (Other)          :3888  
  figure        side          num_id              group          anteroposterior    lateral    
 circ:2160   left :2232   Min.   : 1.00   Control    :2160   Occipital   :1440   Left   :1440  
 rect:2160   right:2088   1st Qu.:16.75   Mindfulness:2160   SubOccipital:1440   Central:1440  
                          Median :32.50                      Inion       :1440   Right  :1440  
                          Mean   :32.40                                                        
                          3rd Qu.:47.25                                                        
                          Max.   :65.00                                                        
                                                                                               

4 N1: average from 145 to 175 ms

Electrodes O1, Oz, O2, E014, E024, E027, E013, Iz, E026

4.1 N1 by group

options(width = 100)
aat_n1_rep_anova <- aov_ez("num_id", "amplitude", subset(aat_data, component == "N1"), within = c("cue", "valence"), between = c("group"))

Warning: More than one observation per design cell, aggregating data using `fun_aggregate = mean`.
To turn off this warning, pass `fun_aggregate = mean` explicitly.
Contrasts set to contr.sum for the following variables: group

cell_means <- aat_n1_rep_anova$data$long |>
  group_by(group, valence, cue) |>
  summarise(amplitude = mean(amplitude))

`summarise()` has grouped output by 'group', 'valence'. You can override using the `.groups`
argument.

aat_n1_afex_plot <-
  afex_plot(
    aat_n1_rep_anova,
    x     = "valence",
    trace = "cue",
    panel = "group",
    error = "between",
    error_arg = list(width = .2, lwd = .75, col = 'gray30', alpha = .7),
    dodge = my_dodge,
    data_arg  = list(
      position = 
        position_jitterdodge(
          jitter.width  = .2,
          # jitter.height = .1, 
          dodge.width   = my_dodge  ## needs to be same as dodge
        )),
    mapping = c("color"), data_alpha = .3,
    # point_arg = list(size = 3)
  )

Warning: Panel(s) show within-subjects factors, but not within-subjects error bars.
For within-subjects error bars use: error = "within"

nice(aat_n1_rep_anova)

Anova Table (Type 3 tests)

Response: amplitude
             Effect    df   MSE         F   ges p.value
1             group 1, 58 60.93      0.01 <.001    .916
2               cue 1, 58  0.67   9.44 **  .002    .003
3         group:cue 1, 58  0.67    3.88 + <.001    .054
4           valence 1, 58  0.50 59.43 ***  .008   <.001
5     group:valence 1, 58  0.50      0.00 <.001    .966
6       cue:valence 1, 58  0.57      0.66 <.001    .421
7 group:cue:valence 1, 58  0.57      0.12 <.001    .733
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘+’ 0.1 ‘ ’ 1

suppressWarnings(print(aat_n1_afex_plot +
                         geom_point(data = cell_means,
                                    aes(x = valence, y = amplitude, group = cue, color = cue),
                                    position = position_dodge(my_dodge), size = 2.5) +
                         facet_grid(~group)
))

a_posteriori(aat_n1_rep_anova)

____________________________________________________________________________________________________
$emmeans
 cue       emmean    SE df lower.CL upper.CL
 Avoid    -0.0617 0.502 58    -1.07    0.944
 Approach -0.3873 0.511 58    -1.41    0.635

Results are averaged over the levels of: group, valence 
Confidence level used: 0.95 

$contrasts
 contrast         estimate    SE df t.ratio p.value
 Avoid - Approach    0.326 0.106 58   3.073  0.0032

Results are averaged over the levels of: group, valence 

____________________________________________________________________________________________________
$emmeans
 valence    emmean    SE df lower.CL upper.CL
 Attractive -0.576 0.509 58    -1.59    0.443
 Neutral     0.127 0.503 58    -0.88    1.134

Results are averaged over the levels of: group, cue 
Confidence level used: 0.95 

$contrasts
 contrast             estimate     SE df t.ratio p.value
 Attractive - Neutral   -0.703 0.0912 58  -7.709  <.0001

Results are averaged over the levels of: group, cue 

afex_plot(
  aat_n1_rep_anova,
  x     = "valence",
  trace = "cue",
  panel = "group",
  error = "between",
  data_geom = geom_violin,
  error_arg = list(width = .2, lwd = .75, col = 'gray10', alpha = .8),
  dodge = my_dodge,
  line_arg = list(size = 0),
  mapping = c("color")
  ) + 
  geom_point(data = aat_n1_rep_anova$data$long,
             aes(x = valence, y = amplitude, group = cue, color = cue),
             position = position_jitterdodge(jitter.width = 0.2, dodge.width = my_dodge),
             alpha = .4) +
  geom_point(data = cell_means,
             aes(x = valence, y = amplitude, group = cue, color = cue),
             position = position_dodge(my_dodge), size = 2.5) +
  facet_grid(~group)

Warning: Panel(s) show within-subjects factors, but not within-subjects error bars.
For within-subjects error bars use: error = "within"

4.2 N1 by topography

options(width = 100)
aat_n1_topo_rep_anova <- aov_ez("num_id", "amplitude", subset(aat_data, component == "N1"), within = c("anteroposterior", "lateral"))

Warning: More than one observation per design cell, aggregating data using `fun_aggregate = mean`.
To turn off this warning, pass `fun_aggregate = mean` explicitly.

aat_n1_topo_afex_plot <-
  afex_plot(
    aat_n1_topo_rep_anova,
    x     = "lateral",
    trace = "anteroposterior",
    error = "between",
    error_arg = list(width = .2, lwd = .75, col = 'gray30', alpha = .7),
    dodge = my_dodge,
    data_arg  = list(
      position = 
        position_jitterdodge(
          jitter.width  = .2,
          # jitter.height = .1, 
          dodge.width   = my_dodge  ## needs to be same as dodge
        )),
    mapping = c("color"), data_alpha = .3,
    # point_arg = list(size = 2)
  )

Warning: Panel(s) show within-subjects factors, but not within-subjects error bars.
For within-subjects error bars use: error = "within"

nice(aat_n1_topo_rep_anova)

Anova Table (Type 3 tests)

Response: amplitude
                   Effect           df  MSE         F  ges p.value
1         anteroposterior  1.23, 72.73 3.78    3.16 + .002    .071
2                 lateral 1.80, 105.98 8.27 49.67 *** .074   <.001
3 anteroposterior:lateral 3.32, 195.97 0.87 11.62 *** .004   <.001
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘+’ 0.1 ‘ ’ 1

Sphericity correction method: GG 

suppressWarnings(print(aat_n1_topo_afex_plot))

a_posteriori(aat_n1_topo_rep_anova)

____________________________________________________________________________________________________
$emmeans
 lateral  emmean    SE df lower.CL upper.CL
 Left     1.0408 0.493 59   0.0538    2.028
 Central -1.7790 0.557 59  -2.8927   -0.665
 Right    0.0646 0.528 59  -0.9911    1.120

Results are averaged over the levels of: anteroposterior 
Confidence level used: 0.95 

$contrasts
 contrast        estimate    SE df t.ratio p.value
 Left - Central     2.820 0.238 59  11.861  <.0001
 Left - Right       0.976 0.323 59   3.027  0.0101
 Central - Right   -1.844 0.295 59  -6.246  <.0001

Results are averaged over the levels of: anteroposterior 
P value adjustment: tukey method for comparing a family of 3 estimates 

____________________________________________________________________________________________________
$emmeans
 anteroposterior lateral emmean    SE df lower.CL upper.CL
 Occipital       Left     1.175 0.552 59   0.0694    2.280
 SubOccipital    Left     1.230 0.534 59   0.1606    2.299
 Inion           Left     0.718 0.436 59  -0.1539    1.589
 Occipital       Central -2.284 0.625 59  -3.5341   -1.034
 SubOccipital    Central -1.730 0.549 59  -2.8296   -0.631
 Inion           Central -1.322 0.533 59  -2.3882   -0.257
 Occipital       Right   -0.262 0.563 59  -1.3877    0.865
 SubOccipital    Right    0.121 0.549 59  -0.9768    1.219
 Inion           Right    0.334 0.496 59  -0.6587    1.327

Confidence level used: 0.95 

$contrasts
 contrast                                  estimate     SE df t.ratio p.value
 Occipital Left - SubOccipital Left          -0.055 0.1482 59  -0.371  1.0000
 Occipital Left - Inion Left                  0.457 0.2894 59   1.579  0.8118
 Occipital Left - Occipital Central           3.459 0.3103 59  11.146  <.0001
 Occipital Left - SubOccipital Central        2.905 0.3293 59   8.823  <.0001
 Occipital Left - Inion Central               2.497 0.3224 59   7.746  <.0001
 Occipital Left - Occipital Right             1.436 0.3725 59   3.857  0.0082
 Occipital Left - SubOccipital Right          1.054 0.3875 59   2.719  0.1637
 Occipital Left - Inion Right                 0.841 0.3980 59   2.112  0.4753
 SubOccipital Left - Inion Left               0.512 0.2140 59   2.394  0.3067
 SubOccipital Left - Occipital Central        3.514 0.3052 59  11.513  <.0001
 SubOccipital Left - SubOccipital Central     2.960 0.2883 59  10.268  <.0001
 SubOccipital Left - Inion Central            2.552 0.2693 59   9.478  <.0001
 SubOccipital Left - Occipital Right          1.491 0.3472 59   4.295  0.0020
 SubOccipital Left - SubOccipital Right       1.108 0.3496 59   3.171  0.0567
 SubOccipital Left - Inion Right              0.896 0.3348 59   2.675  0.1793
 Inion Left - Occipital Central               3.002 0.3583 59   8.378  <.0001
 Inion Left - SubOccipital Central            2.448 0.2572 59   9.519  <.0001
 Inion Left - Inion Central                   2.040 0.2174 59   9.385  <.0001
 Inion Left - Occipital Right                 0.979 0.3779 59   2.591  0.2125
 Inion Left - SubOccipital Right              0.596 0.3780 59   1.578  0.8127
 Inion Left - Inion Right                     0.384 0.2950 59   1.300  0.9274
 Occipital Central - SubOccipital Central    -0.554 0.2367 59  -2.339  0.3368
 Occipital Central - Inion Central           -0.962 0.2604 59  -3.693  0.0134
 Occipital Central - Occipital Right         -2.023 0.3443 59  -5.874  <.0001
 Occipital Central - SubOccipital Right      -2.405 0.3688 59  -6.522  <.0001
 Occipital Central - Inion Right             -2.618 0.3863 59  -6.777  <.0001
 SubOccipital Central - Inion Central        -0.408 0.1230 59  -3.317  0.0387
 SubOccipital Central - Occipital Right      -1.469 0.3399 59  -4.321  0.0019
 SubOccipital Central - SubOccipital Right   -1.852 0.3467 59  -5.341  0.0001
 SubOccipital Central - Inion Right          -2.065 0.3124 59  -6.610  <.0001
 Inion Central - Occipital Right             -1.061 0.3134 59  -3.385  0.0322
 Inion Central - SubOccipital Right          -1.444 0.3171 59  -4.553  0.0009
 Inion Central - Inion Right                 -1.657 0.2728 59  -6.072  <.0001
 Occipital Right - SubOccipital Right        -0.383 0.0992 59  -3.861  0.0081
 Occipital Right - Inion Right               -0.596 0.2146 59  -2.776  0.1447
 SubOccipital Right - Inion Right            -0.213 0.1770 59  -1.202  0.9528

P value adjustment: tukey method for comparing a family of 9 estimates 

5 P2: average from 210 to 250 ms

Electrodes O1, Oz, O2, E014, E024, E027, E013, Iz, E026

5.1 P2 by group

options(width = 100)
aat_p2_rep_anova <- aov_ez("num_id", "amplitude", subset(aat_data, component == "P2"), within = c("cue", "valence"), between = c("group"))

Warning: More than one observation per design cell, aggregating data using `fun_aggregate = mean`.
To turn off this warning, pass `fun_aggregate = mean` explicitly.
Contrasts set to contr.sum for the following variables: group

cell_means <- aat_p2_rep_anova$data$long |>
  group_by(group, valence, cue) |>
  summarise(amplitude = mean(amplitude))

`summarise()` has grouped output by 'group', 'valence'. You can override using the `.groups`
argument.

aat_p2_afex_plot <-
  afex_plot(
    aat_p2_rep_anova,
    x     = "valence",
    trace = "cue",
    panel = "group",
    error = "between",
    error_arg = list(width = .2, lwd = .75, col = 'gray30', alpha = .7),
    dodge = my_dodge,
    data_arg  = list(
      position = 
        position_jitterdodge(
          jitter.width  = .2,
          # jitter.height = .1, 
          dodge.width   = my_dodge  ## needs to be same as dodge
        )),
    mapping = c("color"), data_alpha = .3,
    point_arg = list(size = 2)
  )

Warning: Panel(s) show within-subjects factors, but not within-subjects error bars.
For within-subjects error bars use: error = "within"

nice(aat_p2_rep_anova)

Anova Table (Type 3 tests)

Response: amplitude
             Effect    df   MSE         F   ges p.value
1             group 1, 58 43.92      0.02 <.001    .885
2               cue 1, 58  0.86 12.07 ***  .004   <.001
3         group:cue 1, 58  0.86      0.00 <.001    .958
4           valence 1, 58  0.72  10.51 **  .003    .002
5     group:valence 1, 58  0.72      0.02 <.001    .893
6       cue:valence 1, 58  0.69      0.17 <.001    .684
7 group:cue:valence 1, 58  0.69      0.17 <.001    .683
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘+’ 0.1 ‘ ’ 1

suppressWarnings(print(aat_p2_afex_plot +
                         geom_point(data = cell_means,
                                    aes(x = valence, y = amplitude, group = cue, color = cue),
                                    position = position_dodge(my_dodge), size = 2.5) +
                         facet_grid(~group)
))

a_posteriori(aat_p2_rep_anova)

____________________________________________________________________________________________________
$emmeans
 cue      emmean    SE df lower.CL upper.CL
 Avoid      4.95 0.441 58     4.07     5.83
 Approach   4.53 0.423 58     3.69     5.38

Results are averaged over the levels of: group, valence 
Confidence level used: 0.95 

$contrasts
 contrast         estimate   SE df t.ratio p.value
 Avoid - Approach    0.416 0.12 58   3.474  0.0010

Results are averaged over the levels of: group, valence 

____________________________________________________________________________________________________
$emmeans
 valence    emmean    SE df lower.CL upper.CL
 Attractive   4.92 0.415 58     4.09     5.75
 Neutral      4.56 0.447 58     3.67     5.46

Results are averaged over the levels of: group, cue 
Confidence level used: 0.95 

$contrasts
 contrast             estimate   SE df t.ratio p.value
 Attractive - Neutral    0.356 0.11 58   3.241  0.0020

Results are averaged over the levels of: group, cue 

afex_plot(
  aat_p2_rep_anova,
  x     = "valence",
  trace = "cue",
  panel = "group",
  error = "between",
  data_geom = geom_violin,
  error_arg = list(width = .2, lwd = .75, col = 'gray10', alpha = .8),
  dodge = my_dodge,
  line_arg = list(size = 0),
  mapping = c("color")
  ) + 
  geom_point(data = aat_p2_rep_anova$data$long,
             aes(x = valence, y = amplitude, group = cue, color = cue),
             position = position_jitterdodge(jitter.width = 0.2, dodge.width = my_dodge),
             alpha = .4) +
  geom_point(data = cell_means,
             aes(x = valence, y = amplitude, group = cue, color = cue),
             position = position_dodge(my_dodge), size = 2.5) +
  facet_grid(~group)

Warning: Panel(s) show within-subjects factors, but not within-subjects error bars.
For within-subjects error bars use: error = "within"

5.2 P2 by topography

options(width = 100)
aat_p2_topo_rep_anova <- aov_ez("num_id", "amplitude", subset(aat_data, component == "P2"), within = c("anteroposterior", "lateral"))

Warning: More than one observation per design cell, aggregating data using `fun_aggregate = mean`.
To turn off this warning, pass `fun_aggregate = mean` explicitly.

aat_p2_topo_afex_plot <-
  afex_plot(
    aat_p2_topo_rep_anova,
    x     = "lateral",
    trace = "anteroposterior",
    error = "between",
    error_arg = list(width = .2, lwd = .75, col = 'gray30', alpha = .7),
    dodge = my_dodge,
    data_arg  = list(
      position = 
        position_jitterdodge(
          jitter.width  = .2,
          # jitter.height = .1, 
          dodge.width   = my_dodge  ## needs to be same as dodge
        )),
    mapping = c("color"), data_alpha = .3,
    point_arg = list(size = 2)
  )

Warning: Panel(s) show within-subjects factors, but not within-subjects error bars.
For within-subjects error bars use: error = "within"

nice(aat_p2_topo_rep_anova)

Anova Table (Type 3 tests)

Response: amplitude
                   Effect           df  MSE         F  ges p.value
1         anteroposterior  1.29, 75.96 2.99   8.12 ** .005    .003
2                 lateral  1.65, 97.56 7.51 12.55 *** .022   <.001
3 anteroposterior:lateral 3.34, 197.01 0.77  6.72 *** .003   <.001
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘+’ 0.1 ‘ ’ 1

Sphericity correction method: GG 

suppressWarnings(print(aat_p2_topo_afex_plot))

a_posteriori(aat_p2_topo_rep_anova)

____________________________________________________________________________________________________
$emmeans
 anteroposterior emmean    SE df lower.CL upper.CL
 Occipital         4.89 0.460 59     3.97     5.81
 SubOccipital      4.93 0.440 59     4.05     5.81
 Inion             4.40 0.396 59     3.61     5.20

Results are averaged over the levels of: lateral 
Confidence level used: 0.95 

$contrasts
 contrast                 estimate    SE df t.ratio p.value
 Occipital - SubOccipital  -0.0394 0.115 59  -0.344  0.9371
 Occipital - Inion          0.4894 0.193 59   2.534  0.0366
 SubOccipital - Inion       0.5287 0.117 59   4.512  0.0001

Results are averaged over the levels of: lateral 
P value adjustment: tukey method for comparing a family of 3 estimates 

____________________________________________________________________________________________________
$emmeans
 lateral emmean    SE df lower.CL upper.CL
 Left      4.84 0.431 59     3.97     5.70
 Central   4.04 0.443 59     3.16     4.93
 Right     5.35 0.477 59     4.39     6.30

Results are averaged over the levels of: anteroposterior 
Confidence level used: 0.95 

$contrasts
 contrast        estimate    SE df t.ratio p.value
 Left - Central     0.795 0.198 59   4.018  0.0005
 Left - Right      -0.511 0.305 59  -1.676  0.2229
 Central - Right   -1.305 0.274 59  -4.766  <.0001

Results are averaged over the levels of: anteroposterior 
P value adjustment: tukey method for comparing a family of 3 estimates 

____________________________________________________________________________________________________
$emmeans
 anteroposterior lateral emmean    SE df lower.CL upper.CL
 Occipital       Left      5.20 0.472 59     4.25     6.14
 SubOccipital    Left      5.11 0.473 59     4.16     6.05
 Inion           Left      4.21 0.393 59     3.42     4.99
 Occipital       Central   3.99 0.488 59     3.01     4.97
 SubOccipital    Central   4.12 0.444 59     3.23     5.01
 Inion           Central   4.01 0.426 59     3.16     4.87
 Occipital       Right     5.49 0.522 59     4.44     6.53
 SubOccipital    Right     5.57 0.503 59     4.56     6.57
 Inion           Right     4.99 0.435 59     4.12     5.86

Confidence level used: 0.95 

$contrasts
 contrast                                  estimate     SE df t.ratio p.value
 Occipital Left - SubOccipital Left          0.0933 0.1616 59   0.577  0.9997
 Occipital Left - Inion Left                 0.9923 0.2684 59   3.698  0.0132
 Occipital Left - Occipital Central          1.2090 0.2656 59   4.552  0.0009
 Occipital Left - SubOccipital Central       1.0755 0.2631 59   4.088  0.0040
 Occipital Left - Inion Central              1.1850 0.2969 59   3.992  0.0054
 Occipital Left - Occipital Right           -0.2893 0.3643 59  -0.794  0.9966
 Occipital Left - SubOccipital Right        -0.3672 0.4009 59  -0.916  0.9912
 Occipital Left - Inion Right                0.2105 0.3911 59   0.538  0.9998
 SubOccipital Left - Inion Left              0.8990 0.1916 59   4.692  0.0005
 SubOccipital Left - Occipital Central       1.1157 0.2645 59   4.218  0.0026
 SubOccipital Left - SubOccipital Central    0.9822 0.2406 59   4.082  0.0040
 SubOccipital Left - Inion Central           1.0917 0.2472 59   4.417  0.0014
 SubOccipital Left - Occipital Right        -0.3826 0.3299 59  -1.160  0.9617
 SubOccipital Left - SubOccipital Right     -0.4605 0.3462 59  -1.330  0.9181
 SubOccipital Left - Inion Right             0.1172 0.3279 59   0.358  1.0000
 Inion Left - Occipital Central              0.2167 0.2866 59   0.756  0.9976
 Inion Left - SubOccipital Central           0.0832 0.2011 59   0.414  1.0000
 Inion Left - Inion Central                  0.1927 0.1775 59   1.086  0.9741
 Inion Left - Occipital Right               -1.2816 0.3211 59  -3.991  0.0054
 Inion Left - SubOccipital Right            -1.3595 0.3384 59  -4.017  0.0050
 Inion Left - Inion Right                   -0.7818 0.2667 59  -2.931  0.1020
 Occipital Central - SubOccipital Central   -0.1335 0.1766 59  -0.756  0.9976
 Occipital Central - Inion Central          -0.0240 0.2074 59  -0.116  1.0000
 Occipital Central - Occipital Right        -1.4983 0.3137 59  -4.776  0.0004
 Occipital Central - SubOccipital Right     -1.5762 0.3516 59  -4.483  0.0011
 Occipital Central - Inion Right            -0.9985 0.3605 59  -2.770  0.1467
 SubOccipital Central - Inion Central        0.1095 0.0991 59   1.105  0.9712
 SubOccipital Central - Occipital Right     -1.3648 0.2974 59  -4.589  0.0008
 SubOccipital Central - SubOccipital Right  -1.4427 0.3229 59  -4.468  0.0011
 SubOccipital Central - Inion Right         -0.8650 0.2906 59  -2.977  0.0916
 Inion Central - Occipital Right            -1.4743 0.2849 59  -5.175  0.0001
 Inion Central - SubOccipital Right         -1.5522 0.2965 59  -5.235  0.0001
 Inion Central - Inion Right                -0.9745 0.2497 59  -3.903  0.0071
 Occipital Right - SubOccipital Right       -0.0779 0.1540 59  -0.506  0.9999
 Occipital Right - Inion Right               0.4998 0.2230 59   2.241  0.3939
 SubOccipital Right - Inion Right            0.5777 0.1676 59   3.446  0.0272

P value adjustment: tukey method for comparing a family of 9 estimates 

6 Peak analysis

options(width = 100)
ggplot(
  na.omit(aat_data_peak), aes(x = amplitude, fill = component, color = component)) +
  geom_histogram(alpha = .3, position = "identity") +
  ggtitle("Oz peak voltages")

`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

p <- ggscatterhist(
  na.omit(aat_data_peak), x = "latency", y = "amplitude",
  title = "Oz peak positions", legend = "bottom",# add = "reg.line",
  color = "component", size = 2, alpha = .5, print = FALSE,
  margin.params = list(fill = "component", color = "component", size = .5)
)
p$sp <- p$sp  +
 geom_hline(yintercept = 0, color = "darkgray")
p

summary(aat_data_peak)

    latency      component   amplitude           chindex   chlabel       bini     
 Min.   :138.7   N1:240    Min.   :-24.9053   Min.   :23   Oz:480   Min.   :5.00  
 1st Qu.:168.0   P2:240    1st Qu.: -3.9044   1st Qu.:23            1st Qu.:5.75  
 Median :212.9             Median :  1.1885   Median :23            Median :6.50  
 Mean   :211.6             Mean   :  0.9324   Mean   :23            Mean   :6.50  
 3rd Qu.:252.0             3rd Qu.:  5.7478   3rd Qu.:23            3rd Qu.:7.25  
 Max.   :283.2             Max.   : 21.3475   Max.   :23            Max.   :8.00  
 NA's   :5                 NA's   :5                                              
                stimulus         cue            valence   
 Approach_Attractive:120   Avoid   :240   Attractive:240  
 Approach_Neutral   :120   Approach:240   Neutral   :240  
 Avoid_Attractive   :120                                  
 Avoid_Neutral      :120                                  
                                                          
                                                          
                                                          
                                                           ERPset                 id_code   
 S01_C1_circ_right_Ctrl_infinity_reference_and_personal_choice:  8   S01_C1_circ_right:  8  
 S02_C2_circ_left_Mind_infinity_reference_and_personal_choice :  8   S02_C2_circ_left :  8  
 S03_C1_rect_right_Ctrl_infinity_reference_and_personal_choice:  8   S03_C1_rect_right:  8  
 S04_C1_rect_left_Ctrl_infinity_reference_and_personal_choice :  8   S04_C1_rect_left :  8  
 S05_C1_circ_left_Ctrl_infinity_reference_and_personal_choice :  8   S05_C1_circ_left :  8  
 S06_C2_circ_left_Mind_infinity_reference_and_personal_choice :  8   S06_C2_circ_left :  8  
 (Other)                                                      :432   (Other)          :432  
  figure       side         num_id              group    
 circ:240   left :248   Min.   : 1.00   Control    :240  
 rect:240   right:232   1st Qu.:16.75   Mindfulness:240  
                        Median :32.50                    
                        Mean   :32.40                    
                        3rd Qu.:47.25                    
                        Max.   :65.00                    
                                                         

6.1 N1: negative peak from 135 to 215 ms, Oz

options(width = 100)
aat_n1_peak_rep_anova <-
  aov_ez("num_id", "amplitude", subset(aat_data_peak, component == "N1"), within = c("cue", "valence"), between = c("group"))

Contrasts set to contr.sum for the following variables: group

cell_means <- aat_n1_peak_rep_anova$data$long |>
  group_by(group, valence, cue) |>
  summarise(amplitude = mean(amplitude))

`summarise()` has grouped output by 'group', 'valence'. You can override using the `.groups`
argument.

aat_n1_peak_afex_plot <-
  afex_plot(
    aat_n1_peak_rep_anova,
    x     = "valence",
    trace = "cue",
    panel = "group",
    error = "between",
    error_arg = list(width = .2, lwd = .75, col = 'gray30', alpha = .7),
    dodge = my_dodge,
    data_arg  = list(
      position = 
        position_jitterdodge(
          jitter.width  = .2,
          # jitter.height = .1, 
          dodge.width   = my_dodge  ## needs to be same as dodge
        )),
    mapping = c("color"), data_alpha = .3,
    point_arg = list(size = 2)
  )

Warning: Panel(s) show within-subjects factors, but not within-subjects error bars.
For within-subjects error bars use: error = "within"

table_data <- aat_n1_peak_rep_anova$data$long
xtabs(~ group , table_data) / length(unique(table_data$cue)) / length(unique(table_data$valence))

group
    Control Mindfulness 
         30          30 

nice(aat_n1_peak_rep_anova)

Anova Table (Type 3 tests)

Response: amplitude
             Effect    df   MSE         F   ges p.value
1             group 1, 58 89.92      0.38  .006    .539
2               cue 1, 58  1.12  10.60 **  .002    .002
3         group:cue 1, 58  1.12      1.86 <.001    .178
4           valence 1, 58  1.03 31.94 ***  .006   <.001
5     group:valence 1, 58  1.03      0.29 <.001    .590
6       cue:valence 1, 58  1.01      1.18 <.001    .282
7 group:cue:valence 1, 58  1.01      0.97 <.001    .330
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘+’ 0.1 ‘ ’ 1

suppressWarnings(print(aat_n1_peak_afex_plot +
                         geom_point(data = cell_means,
                                    aes(x = valence, y = amplitude, group = cue, color = cue),
                                    position = position_dodge(my_dodge), size = 2.5) +
                         facet_grid(~group)
))

a_posteriori(aat_n1_peak_rep_anova)

____________________________________________________________________________________________________
$emmeans
 cue      emmean    SE df lower.CL upper.CL
 Avoid     -4.06 0.618 58    -5.30    -2.83
 Approach  -4.51 0.614 58    -5.74    -3.28

Results are averaged over the levels of: group, valence 
Confidence level used: 0.95 

$contrasts
 contrast         estimate    SE df t.ratio p.value
 Avoid - Approach    0.445 0.137 58   3.255  0.0019

Results are averaged over the levels of: group, valence 

____________________________________________________________________________________________________
$emmeans
 valence    emmean    SE df lower.CL upper.CL
 Attractive  -4.65 0.632 58    -5.92    -3.39
 Neutral     -3.91 0.598 58    -5.11    -2.72

Results are averaged over the levels of: group, cue 
Confidence level used: 0.95 

$contrasts
 contrast             estimate    SE df t.ratio p.value
 Attractive - Neutral    -0.74 0.131 58  -5.652  <.0001

Results are averaged over the levels of: group, cue 

afex_plot(
  aat_n1_peak_rep_anova,
  x     = "valence",
  trace = "cue",
  panel = "group",
  error = "between",
  data_geom = geom_violin,
  error_arg = list(width = .2, lwd = .75, col = 'gray10', alpha = .8),
  dodge = my_dodge,
  line_arg = list(size = 0),
  mapping = c("color")
  ) + 
  geom_point(data = aat_n1_peak_rep_anova$data$long,
             aes(x = valence, y = amplitude, group = cue, color = cue),
             position = position_jitterdodge(jitter.width = 0.2, dodge.width = my_dodge),
             alpha = .4) +
  geom_point(data = cell_means,
             aes(x = valence, y = amplitude, group = cue, color = cue),
             position = position_dodge(my_dodge), size = 2.5) +
  facet_grid(~group)

Warning: Panel(s) show within-subjects factors, but not within-subjects error bars.
For within-subjects error bars use: error = "within"

6.2 P2: positive peak from 215 to 285 ms, Oz

options(width = 100)
aat_p2_peak_rep_anova <-
  aov_ez("num_id", "amplitude", subset(aat_data_peak, component == "P2"), within = c("cue", "valence"), between = c("group"))

Warning: Missing values for 2 ID(s), which were removed before analysis:
53, 61
Below the first few rows (in wide format) of the removed cases with missing data.
  
Contrasts set to contr.sum for the following variables: group

cell_means <- aat_p2_peak_rep_anova$data$long |>
  group_by(group, valence, cue) |>
  summarise(amplitude = mean(amplitude))

`summarise()` has grouped output by 'group', 'valence'. You can override using the `.groups`
argument.

aat_p2_peak_afex_plot <-
  afex_plot(
    aat_p2_peak_rep_anova,
    x     = "valence",
    trace = "cue",
    panel = "group",
    error = "between",
    error_arg = list(width = .2, lwd = .75, col = 'gray30', alpha = .7),
    dodge = my_dodge,
    data_arg  = list(
      position = 
        position_jitterdodge(
          jitter.width  = .2,
          # jitter.height = .1, 
          dodge.width   = my_dodge  ## needs to be same as dodge
        )),
    mapping = c("color"), data_alpha = .3,
    # line_arg = list(size = 0),
    point_arg = list(size = 2)
  )

Warning: Panel(s) show within-subjects factors, but not within-subjects error bars.
For within-subjects error bars use: error = "within"

table_data <- aat_p2_peak_rep_anova$data$long
xtabs(~ group , table_data) / length(unique(table_data$cue)) / length(unique(table_data$valence))

group
    Control Mindfulness 
         29          29 

nice(aat_p2_peak_rep_anova)

Anova Table (Type 3 tests)

Response: amplitude
             Effect    df   MSE       F   ges p.value
1             group 1, 56 65.09    0.02 <.001    .878
2               cue 1, 56  1.46 9.40 **  .004    .003
3         group:cue 1, 56  1.46    0.00 <.001    .998
4           valence 1, 56  0.94  3.55 + <.001    .065
5     group:valence 1, 56  0.94    0.15 <.001    .702
6       cue:valence 1, 56  1.02  4.99 *  .001    .029
7 group:cue:valence 1, 56  1.02    0.92 <.001    .343
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘+’ 0.1 ‘ ’ 1

suppressWarnings(print(aat_p2_peak_afex_plot + 
                         geom_point(data = cell_means,
                                    aes(x = valence, y = amplitude, group = cue, color = cue),
                                    position = position_dodge(my_dodge), size = 2.5) +
                         facet_grid(~group)
))

a_posteriori(aat_p2_peak_rep_anova)

____________________________________________________________________________________________________
$emmeans
 cue      emmean    SE df lower.CL upper.CL
 Avoid      6.53 0.552 56     5.43     7.64
 Approach   6.04 0.519 56     5.00     7.08

Results are averaged over the levels of: group, valence 
Confidence level used: 0.95 

$contrasts
 contrast         estimate    SE df t.ratio p.value
 Avoid - Approach    0.487 0.159 56   3.066  0.0033

Results are averaged over the levels of: group, valence 

____________________________________________________________________________________________________
$emmeans
 cue      valence    emmean    SE df lower.CL upper.CL
 Avoid    Attractive   6.50 0.519 56     5.46     7.54
 Approach Attractive   6.31 0.533 56     5.24     7.38
 Avoid    Neutral      6.56 0.600 56     5.36     7.76
 Approach Neutral      5.78 0.517 56     4.74     6.81

Results are averaged over the levels of: group 
Confidence level used: 0.95 

$contrasts
 contrast                               estimate    SE df t.ratio p.value
 Avoid Attractive - Approach Attractive   0.1902 0.187 56   1.016  0.7411
 Avoid Attractive - Avoid Neutral        -0.0573 0.204 56  -0.281  0.9922
 Avoid Attractive - Approach Neutral      0.7266 0.203 56   3.575  0.0040
 Approach Attractive - Avoid Neutral     -0.2475 0.204 56  -1.215  0.6200
 Approach Attractive - Approach Neutral   0.5364 0.161 56   3.328  0.0082
 Avoid Neutral - Approach Neutral         0.7839 0.225 56   3.481  0.0052

Results are averaged over the levels of: group 
P value adjustment: tukey method for comparing a family of 4 estimates 

afex_plot(
  aat_p2_peak_rep_anova,
  x     = "valence",
  trace = "cue",
  panel = "group",
  error = "between",
  data_geom = geom_violin,
  error_arg = list(width = .2, lwd = .75, col = 'gray10', alpha = .8),
  dodge = my_dodge,
  line_arg = list(size = 0),
  mapping = c("color")
  ) + 
  geom_point(data = aat_p2_peak_rep_anova$data$long,
             aes(x = valence, y = amplitude, group = cue, color = cue),
             position = position_jitterdodge(jitter.width = 0.2, dodge.width = my_dodge),
             alpha = .4) +
  geom_point(data = cell_means,
             aes(x = valence, y = amplitude, group = cue, color = cue),
             position = position_dodge(my_dodge), size = 2.5) +
  facet_grid(~group)

Warning: Panel(s) show within-subjects factors, but not within-subjects error bars.
For within-subjects error bars use: error = "within"

7 Mass univariate analysis (cluster mass)

Groppe, D. M., Urbach, T. P., & Kutas, M. (2011). Mass univariate analysis of event-related brain potentials/fields I: A critical tutorial review. Psychophysiology, 48(12), 1711–1725. https://doi.org/10.1111/J.1469-8986.2011.01273.X

7.1 Early time window

No significant group effect or interactions found with FMUT Fclust, Fmax, nor Ffdr

7.2 Late time window

No significant group effect or interactions found with FMUT Fclust, Fmax, nor Ffdr