Postural Stability and Emotions
Young & Elder groups
Alvaro Rivera-Rei
13 septiembre, 2023
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$`Pr(>F)`
for (i in 1:length(p_values)) {
if (p_values[i] <= sig_level) {
print(emmeans(afex_aov, factors[[i]], contr = "pairwise"))
cat(rep("_", 100), '\n', sep = "")
}
}
}master_dir <- '~/Insync/OneDrive_shared/Fondecyt_Emociones_Estabilometria'
data_dir <- paste(master_dir, 'data', sep = '/')
emo_data_name <- paste(data_dir, 'emo_data_clean.csv', sep='/')
emo_data_clean <- read.csv(emo_data_name, header = TRUE)
emo_data_clean <- emo_data_clean[(emo_data_clean$Group == 'Elder' | emo_data_clean$Group == 'Young'), ]
emo_data_clean$Group <- factor(emo_data_clean$Group, levels = c("Young", "Elder"))
emo_data_clean$Task <- factor(emo_data_clean$Task, levels = c("Unpleasant", "Neutral", "Pleasant"))
emo_data_clean$Emotion <- factor(emo_data_clean$Emotion)
emo_data_clean$ID <- factor(emo_data_clean$ID)
emo_data_clean$num_ID <- factor(emo_data_clean$num_ID)
emo_data_clean$log10_area <- log10(emo_data_clean$area)
emo_data_clean$log10_axis1 <- log10(emo_data_clean$axis1)
emo_data_clean$log10_axis2 <- log10(emo_data_clean$axis2)
emo_data_clean$log10_mdist <- log10(emo_data_clean$mdist)
emo_data_clean$log10_rmv <- log10(emo_data_clean$rmv)
emo_data_clean$log10_rmsx <- log10(emo_data_clean$rmsx)
emo_data_clean$log10_rmsy <- log10(emo_data_clean$rmsy)
emo_data_clean$log10_MPFx <- log10(emo_data_clean$MPFx)
emo_data_clean$log10_MPFy <- log10(emo_data_clean$MPFy)
emo_data_clean$log10_PEAKx <- log10(emo_data_clean$PEAKx)
emo_data_clean$log10_PEAKy <- log10(emo_data_clean$PEAKy)
emo_data_clean$log10_F50x <- log10(emo_data_clean$F50x)
emo_data_clean$log10_F50y <- log10(emo_data_clean$F50y)
emo_data_clean$log10_F95x <- log10(emo_data_clean$F95x)
emo_data_clean$log10_F95y <- log10(emo_data_clean$F95y)
emo_data_clean$log10_sampen_x <- log10(emo_data_clean$sampen_x)
emo_data_clean$log10_sampen_y <- log10(emo_data_clean$sampen_y)
emo_data_clean$log10_rMSSD <- log10(emo_data_clean$rMSSD)
emo_data_clean$log10_hrv_rmssd_nk2 <- log10(emo_data_clean$hrv_rmssd_nk2)
emo_data_clean$log10_ave_phasic_eda <- log10(emo_data_clean$ave_phasic_eda)Warning: NaNs producedemo_data_clean$rrv_rmssd[emo_data_clean$rrv_rmssd > 4500] <- NA
emo_data_clean$log10_scr_peaks_number <- log10(emo_data_clean$scr_peaks_number)
emo_data_clean$log10_scr_mean_amplitude <- log10(emo_data_clean$scr_mean_amplitude)
emo_data_clean[sapply(emo_data_clean, is.infinite)] <- NA1 General Description
filename Group Paradigm Task Emotion ID
Length:296 Young:153 Length:296 Unpleasant:99 Yes:296 FOJO_04: 3
Class :character Elder:143 Class :character Neutral :98 FOJO_05: 3
Mode :character Mode :character Pleasant :99 FOJO_06: 3
FOJO_07: 3
FOJO_08: 3
FOJO_09: 3
(Other):278
area axis1 axis2 angle mdist
Min. : 17.68 Min. : 3.974 Min. : 0.9813 Min. :-3.092 Min. : 1.593
1st Qu.: 99.64 1st Qu.: 9.125 1st Qu.: 3.1921 1st Qu.: 1.402 1st Qu.: 3.441
Median : 153.04 Median :10.913 Median : 4.5314 Median : 1.555 Median : 4.258
Mean : 197.57 Mean :11.733 Mean : 4.9910 Mean : 1.426 Mean : 4.473
3rd Qu.: 250.36 3rd Qu.:13.522 3rd Qu.: 6.3915 3rd Qu.: 1.666 3rd Qu.: 5.228
Max. :1050.94 Max. :40.677 Max. :12.5001 Max. : 3.102 Max. :12.659
NA's :13 NA's :13 NA's :13 NA's :13 NA's :13
rmv rmsx rmsy MPFx PEAKx
Min. : 4.412 Min. : 0.4172 Min. : 1.597 Min. :0.000290 Min. :0.000122
1st Qu.: 7.325 1st Qu.: 1.4249 1st Qu.: 3.603 1st Qu.:0.001301 1st Qu.:0.000122
Median : 8.882 Median : 1.9841 Median : 4.346 Median :0.001757 Median :0.000122
Mean : 9.392 Mean : 2.2972 Mean : 4.624 Mean :0.002079 Mean :0.000377
3rd Qu.:10.906 3rd Qu.: 2.8276 3rd Qu.: 5.411 3rd Qu.:0.002763 3rd Qu.:0.000244
Max. :21.810 Max. :10.3865 Max. :16.609 Max. :0.005838 Max. :0.006836
NA's :13 NA's :13 NA's :13 NA's :13 NA's :13
F50x F95x MPFy PEAKy F50y
Min. :0.000244 Min. :0.000488 Min. :0.000388 Min. :0.000122 Min. :0.000244
1st Qu.:0.000244 1st Qu.:0.004761 1st Qu.:0.001227 1st Qu.:0.000122 1st Qu.:0.000488
Median :0.000732 Median :0.006226 Median :0.001673 Median :0.000244 Median :0.000977
Mean :0.001200 Mean :0.006544 Mean :0.001717 Mean :0.000427 Mean :0.001046
3rd Qu.:0.001587 3rd Qu.:0.008118 3rd Qu.:0.002061 3rd Qu.:0.000366 3rd Qu.:0.001465
Max. :0.006226 Max. :0.015259 Max. :0.003962 Max. :0.003540 Max. :0.003418
NA's :13 NA's :13 NA's :13 NA's :13 NA's :13
F95y forward_mov sampen_x sampen_y sampen_resul_vect
Min. :0.001343 Min. :0.1191 Min. :0.004882 Min. :0.002094 Min. :0.3943
1st Qu.:0.004028 1st Qu.:0.4423 1st Qu.:0.160767 1st Qu.:0.072823 1st Qu.:1.8177
Median :0.005615 Median :0.4978 Median :0.304356 Median :0.125161 Median :2.1314
Mean :0.005787 Mean :0.4964 Mean :0.400979 Mean :0.142622 Mean :2.1399
3rd Qu.:0.007141 3rd Qu.:0.5499 3rd Qu.:0.509943 3rd Qu.:0.196104 3rd Qu.:2.4388
Max. :0.015015 Max. :0.8000 Max. :1.935251 Max. :0.553328 Max. :3.7222
NA's :13 NA's :13 NA's :13 NA's :13 NA's :36
sampen_phi_rad sampen_delta_phi heart_rate rMSSD ave_phasic_eda
Min. :0.6319 Min. :1.114 Min. : 53.48 Min. : 3.457 Min. :-0.003113
1st Qu.:0.9191 1st Qu.:1.525 1st Qu.: 70.19 1st Qu.: 11.323 1st Qu.: 0.041904
Median :1.0177 Median :1.640 Median : 77.51 Median : 20.189 Median : 0.185042
Mean :1.0229 Mean :1.633 Mean : 78.27 Mean : 24.090 Mean : 0.559187
3rd Qu.:1.1168 3rd Qu.:1.744 3rd Qu.: 85.85 3rd Qu.: 30.040 3rd Qu.: 0.711464
Max. :1.3967 Max. :2.154 Max. :117.80 Max. :117.520 Max. : 6.133009
NA's :13 NA's :13 NA's :11 NA's :11 NA's :12
ave_tonic_eda recurrence_rate_x determinism_x ave_diag_len_x longest_diag_x
Min. :-0.335 Min. :0.009456 Min. :0.9845 Min. : 6.371 Min. : 86.0
1st Qu.: 6.020 1st Qu.:0.057900 1st Qu.:0.9991 1st Qu.: 20.019 1st Qu.: 265.5
Median :10.006 Median :0.079944 Median :0.9996 Median : 30.059 Median : 366.0
Mean :14.385 Mean :0.083801 Mean :0.9990 Mean : 34.571 Mean : 442.5
3rd Qu.:20.727 3rd Qu.:0.102887 3rd Qu.:0.9998 3rd Qu.: 42.827 3rd Qu.: 504.0
Max. :59.139 Max. :0.459716 Max. :1.0000 Max. :326.286 Max. :4922.0
NA's :12 NA's :13 NA's :13 NA's :13 NA's :13
diag_entropy_x laminarity_x trapping_time_x longest_vertical_x rec_time1_x
Min. :2.544 Min. :0.9930 Min. : 8.727 Min. : 63.0 Min. : 2.215
1st Qu.:3.853 1st Qu.:0.9995 1st Qu.: 24.371 1st Qu.: 262.0 1st Qu.: 9.852
Median :4.282 Median :0.9997 Median : 37.031 Median : 383.0 Median : 12.664
Mean :4.216 Mean :0.9995 Mean : 42.361 Mean : 459.6 Mean : 18.505
3rd Qu.:4.645 3rd Qu.:0.9998 3rd Qu.: 52.351 3rd Qu.: 560.0 3rd Qu.: 17.493
Max. :6.416 Max. :1.0000 Max. :415.057 Max. :3738.0 Max. :107.733
NA's :13 NA's :13 NA's :13 NA's :13 NA's :13
rec_time2_x rec_per_dens_entr_x clustering_x transitivity_x delay_x
Min. : 241.7 Min. :0.5791 Min. :0.3124 Min. :0.3686 Min. : 9.00
1st Qu.: 424.7 1st Qu.:0.6911 1st Qu.:0.5129 1st Qu.:0.5432 1st Qu.:23.00
Median : 485.0 Median :0.7188 Median :0.5445 Median :0.5873 Median :24.00
Mean : 515.7 Mean :0.7160 Mean :0.5403 Mean :0.5831 Mean :24.26
3rd Qu.: 569.0 3rd Qu.:0.7459 3rd Qu.:0.5820 3rd Qu.:0.6261 3rd Qu.:26.00
Max. :1239.9 Max. :0.8138 Max. :0.8407 Max. :0.8802 Max. :36.00
NA's :13 NA's :13 NA's :13 NA's :13 NA's :13
dim_x recurrence_rate_y determinism_y ave_diag_len_y longest_diag_y
Min. :3.000 Min. :0.03681 Min. :0.9987 Min. : 15.42 Min. : 163.0
1st Qu.:3.000 1st Qu.:0.06436 1st Qu.:0.9997 1st Qu.: 28.82 1st Qu.: 282.5
Median :3.000 Median :0.07875 Median :0.9998 Median : 36.94 Median : 355.0
Mean :3.092 Mean :0.08243 Mean :0.9998 Mean : 39.71 Mean : 390.1
3rd Qu.:3.000 3rd Qu.:0.09426 3rd Qu.:0.9999 3rd Qu.: 47.59 3rd Qu.: 456.0
Max. :4.000 Max. :0.34314 Max. :1.0000 Max. :160.09 Max. :1418.0
NA's :13 NA's :13 NA's :13 NA's :13 NA's :13
diag_entropy_y laminarity_y trapping_time_y longest_vertical_y rec_time1_y
Min. :3.558 Min. :0.9993 Min. : 18.63 Min. : 161.0 Min. : 2.963
1st Qu.:4.268 1st Qu.:0.9998 1st Qu.: 32.12 1st Qu.: 280.0 1st Qu.:10.738
Median :4.523 Median :0.9999 Median : 41.11 Median : 358.0 Median :12.848
Mean :4.531 Mean :0.9998 Mean : 45.36 Mean : 396.7 Mean :13.468
3rd Qu.:4.794 3rd Qu.:0.9999 3rd Qu.: 54.37 3rd Qu.: 467.5 3rd Qu.:15.716
Max. :6.013 Max. :1.0000 Max. :186.71 Max. :1802.0 Max. :27.485
NA's :13 NA's :13 NA's :13 NA's :13 NA's :13
rec_time2_y rec_per_dens_entr_y clustering_y transitivity_y delay_y dim_y
Min. :278.4 Min. :0.6747 Min. :0.4687 Min. :0.5114 Min. :13.00 Min. :3
1st Qu.:490.8 1st Qu.:0.7434 1st Qu.:0.5263 1st Qu.:0.5621 1st Qu.:21.00 1st Qu.:3
Median :542.4 Median :0.7627 Median :0.5457 Median :0.5862 Median :22.00 Median :3
Mean :546.1 Mean :0.7610 Mean :0.5512 Mean :0.5907 Mean :22.61 Mean :3
3rd Qu.:601.5 3rd Qu.:0.7808 3rd Qu.:0.5725 3rd Qu.:0.6174 3rd Qu.:24.00 3rd Qu.:3
Max. :893.8 Max. :0.8231 Max. :0.7109 Max. :0.7376 Max. :32.00 Max. :3
NA's :13 NA's :13 NA's :13 NA's :13 NA's :13 NA's :13
delay_x2 dim_x2 tol_x2 delay_y2 dim_y2 tol_y2
Min. :6 Min. :6 Min. :375.6 Min. :10 Min. :6 Min. :171.1
1st Qu.:6 1st Qu.:6 1st Qu.:375.6 1st Qu.:10 1st Qu.:6 1st Qu.:171.1
Median :6 Median :6 Median :375.6 Median :10 Median :6 Median :171.1
Mean :6 Mean :6 Mean :375.6 Mean :10 Mean :6 Mean :171.1
3rd Qu.:6 3rd Qu.:6 3rd Qu.:375.6 3rd Qu.:10 3rd Qu.:6 3rd Qu.:171.1
Max. :6 Max. :6 Max. :375.6 Max. :10 Max. :6 Max. :171.1
NA's :295 NA's :295 NA's :295 NA's :295 NA's :295 NA's :295
rsp_rate rrv_rmssd rsa_porges scr_peaks_number scr_mean_amplitude
Min. : 6.343 Min. : 253.5 Min. :-10.724 Min. : 1.00 Min. :0.000e+00
1st Qu.:11.176 1st Qu.:1286.3 1st Qu.: -7.581 1st Qu.: 5.00 1st Qu.:9.310e-06
Median :13.593 Median :1880.6 Median : -6.474 Median : 7.50 Median :5.094e-05
Mean :13.412 Mean :1976.7 Mean : -6.671 Mean : 9.27 Mean :1.750e-04
3rd Qu.:15.584 3rd Qu.:2659.2 3rd Qu.: -5.693 3rd Qu.:10.00 3rd Qu.:1.803e-04
Max. :22.788 Max. :4469.5 Max. : -2.758 Max. :90.00 Max. :4.675e-03
NA's :6
heart_rate_nk2 hrv_rmssd_nk2 num_ID log10_area log10_axis1
Min. : 53.33 Min. : 3.658 4 : 3 Min. :1.247 Min. :0.5992
1st Qu.: 69.75 1st Qu.: 12.903 5 : 3 1st Qu.:1.998 1st Qu.:0.9602
Median : 77.36 Median : 20.803 6 : 3 Median :2.185 Median :1.0380
Mean : 78.14 Mean : 24.948 7 : 3 Mean :2.196 Mean :1.0460
3rd Qu.: 85.73 3rd Qu.: 30.341 8 : 3 3rd Qu.:2.399 3rd Qu.:1.1311
Max. :117.54 Max. :209.589 9 : 3 Max. :3.022 Max. :1.6093
(Other):278 NA's :13 NA's :13
log10_axis2 log10_mdist log10_rmv log10_rmsx log10_rmsy
Min. :-0.008213 Min. :0.2023 Min. :0.6446 Min. :-0.3797 Min. :0.2033
1st Qu.: 0.504078 1st Qu.:0.5367 1st Qu.:0.8648 1st Qu.: 0.1538 1st Qu.:0.5567
Median : 0.656230 Median :0.6292 Median :0.9485 Median : 0.2976 Median :0.6381
Mean : 0.653095 Mean :0.6283 Mean :0.9548 Mean : 0.3057 Mean :0.6426
3rd Qu.: 0.805595 3rd Qu.:0.7183 3rd Qu.:1.0377 3rd Qu.: 0.4514 3rd Qu.:0.7333
Max. : 1.096912 Max. :1.1024 Max. :1.3386 Max. : 1.0165 Max. :1.2203
NA's :13 NA's :13 NA's :13 NA's :13 NA's :13
log10_MPFx log10_MPFy log10_PEAKx log10_PEAKy log10_F50x
Min. :-3.537 Min. :-3.411 Min. :-3.913 Min. :-3.913 Min. :-3.612
1st Qu.:-2.886 1st Qu.:-2.911 1st Qu.:-3.913 1st Qu.:-3.913 1st Qu.:-3.612
Median :-2.755 Median :-2.777 Median :-3.913 Median :-3.612 Median :-3.135
Mean :-2.742 Mean :-2.801 Mean :-3.723 Mean :-3.587 Mean :-3.120
3rd Qu.:-2.559 3rd Qu.:-2.686 3rd Qu.:-3.612 3rd Qu.:-3.436 3rd Qu.:-2.799
Max. :-2.234 Max. :-2.402 Max. :-2.165 Max. :-2.451 Max. :-2.206
NA's :13 NA's :13 NA's :13 NA's :13 NA's :13
log10_F50y log10_F95x log10_F95y log10_sampen_x log10_sampen_y
Min. :-3.612 Min. :-3.311 Min. :-2.872 Min. :-2.3114 Min. :-2.6791
1st Qu.:-3.311 1st Qu.:-2.322 1st Qu.:-2.395 1st Qu.:-0.7938 1st Qu.:-1.1377
Median :-3.010 Median :-2.206 Median :-2.251 Median :-0.5166 Median :-0.9025
Mean :-3.064 Mean :-2.219 Mean :-2.272 Mean :-0.5566 Mean :-0.9432
3rd Qu.:-2.834 3rd Qu.:-2.091 3rd Qu.:-2.146 3rd Qu.:-0.2925 3rd Qu.:-0.7075
Max. :-2.466 Max. :-1.816 Max. :-1.823 Max. : 0.2867 Max. :-0.2570
NA's :13 NA's :13 NA's :13 NA's :13 NA's :13
log10_rMSSD log10_hrv_rmssd_nk2 log10_ave_phasic_eda log10_scr_peaks_number
Min. :0.5387 Min. :0.5632 Min. :-3.3986 Min. :0.0000
1st Qu.:1.0540 1st Qu.:1.1107 1st Qu.:-1.3700 1st Qu.:0.6990
Median :1.3051 Median :1.3181 Median :-0.7310 Median :0.8741
Mean :1.2810 Mean :1.2968 Mean :-0.8161 Mean :0.8683
3rd Qu.:1.4777 3rd Qu.:1.4820 3rd Qu.:-0.1475 3rd Qu.:1.0000
Max. :2.0701 Max. :2.3214 Max. : 0.7877 Max. :1.9542
NA's :11 NA's :13
log10_scr_mean_amplitude
Min. :-7.493
1st Qu.:-5.013
Median :-4.291
Mean :-4.447
3rd Qu.:-3.739
Max. :-2.330
NA's :1 1.1 Time-distance parameters
options(width = 100)
time_distance <- c('log10_area', 'log10_axis1', 'log10_axis2', 'log10_mdist', 'log10_rmv', 'log10_rmsx', 'log10_rmsy')
time_distance_pairs <- ggpairs(emo_data_clean,
columns = time_distance,
aes(colour = Group, alpha = .25),
progress = FALSE,
lower = list(continuous = wrap("points")))
suppressWarnings(print(time_distance_pairs))
log10_area log10_axis1 log10_axis2 log10_mdist log10_rmv
Min. :1.247 Min. :0.5992 Min. :-0.008213 Min. :0.2023 Min. :0.6446
1st Qu.:1.998 1st Qu.:0.9602 1st Qu.: 0.504078 1st Qu.:0.5367 1st Qu.:0.8648
Median :2.185 Median :1.0380 Median : 0.656230 Median :0.6292 Median :0.9485
Mean :2.196 Mean :1.0460 Mean : 0.653095 Mean :0.6283 Mean :0.9548
3rd Qu.:2.399 3rd Qu.:1.1311 3rd Qu.: 0.805595 3rd Qu.:0.7183 3rd Qu.:1.0377
Max. :3.022 Max. :1.6093 Max. : 1.096912 Max. :1.1024 Max. :1.3386
NA's :13 NA's :13 NA's :13 NA's :13 NA's :13
log10_rmsx log10_rmsy
Min. :-0.3797 Min. :0.2033
1st Qu.: 0.1538 1st Qu.:0.5567
Median : 0.2976 Median :0.6381
Mean : 0.3057 Mean :0.6426
3rd Qu.: 0.4514 3rd Qu.:0.7333
Max. : 1.0165 Max. :1.2203
NA's :13 NA's :13 1.2 Frequency parameters
options(width = 100)
frequency <- c('log10_MPFx', 'log10_MPFy', 'log10_PEAKx', 'log10_PEAKy', 'log10_F50x', 'log10_F50y', 'log10_F95x', 'log10_F95y')
frequency_pairs <- ggpairs(emo_data_clean,
columns = frequency,
aes(colour = Group, alpha = .25),
progress = FALSE,
lower = list(continuous = wrap("points")))
suppressWarnings(print(frequency_pairs))
log10_MPFx log10_MPFy log10_PEAKx log10_PEAKy log10_F50x
Min. :-3.537 Min. :-3.411 Min. :-3.913 Min. :-3.913 Min. :-3.612
1st Qu.:-2.886 1st Qu.:-2.911 1st Qu.:-3.913 1st Qu.:-3.913 1st Qu.:-3.612
Median :-2.755 Median :-2.777 Median :-3.913 Median :-3.612 Median :-3.135
Mean :-2.742 Mean :-2.801 Mean :-3.723 Mean :-3.587 Mean :-3.120
3rd Qu.:-2.559 3rd Qu.:-2.686 3rd Qu.:-3.612 3rd Qu.:-3.436 3rd Qu.:-2.799
Max. :-2.234 Max. :-2.402 Max. :-2.165 Max. :-2.451 Max. :-2.206
NA's :13 NA's :13 NA's :13 NA's :13 NA's :13
log10_F50y log10_F95x log10_F95y
Min. :-3.612 Min. :-3.311 Min. :-2.872
1st Qu.:-3.311 1st Qu.:-2.322 1st Qu.:-2.395
Median :-3.010 Median :-2.206 Median :-2.251
Mean :-3.064 Mean :-2.219 Mean :-2.272
3rd Qu.:-2.834 3rd Qu.:-2.091 3rd Qu.:-2.146
Max. :-2.466 Max. :-1.816 Max. :-1.823
NA's :13 NA's :13 NA's :13 1.3 Entropy
options(width = 100)
entropy <- c('forward_mov', 'log10_sampen_x', 'log10_sampen_y', 'sampen_resul_vect', 'sampen_phi_rad', 'sampen_delta_phi')
entropy_pairs <- ggpairs(emo_data_clean,
columns = entropy,
aes(colour = Group, alpha = .25),
progress = FALSE,
lower = list(continuous = wrap("points")))
suppressWarnings(print(entropy_pairs))
forward_mov log10_sampen_x log10_sampen_y sampen_resul_vect sampen_phi_rad
Min. :0.1191 Min. :-2.3114 Min. :-2.6791 Min. :0.3943 Min. :0.6319
1st Qu.:0.4423 1st Qu.:-0.7938 1st Qu.:-1.1377 1st Qu.:1.8177 1st Qu.:0.9191
Median :0.4978 Median :-0.5166 Median :-0.9025 Median :2.1314 Median :1.0177
Mean :0.4964 Mean :-0.5566 Mean :-0.9432 Mean :2.1399 Mean :1.0229
3rd Qu.:0.5499 3rd Qu.:-0.2925 3rd Qu.:-0.7075 3rd Qu.:2.4388 3rd Qu.:1.1168
Max. :0.8000 Max. : 0.2867 Max. :-0.2570 Max. :3.7222 Max. :1.3967
NA's :13 NA's :13 NA's :13 NA's :36 NA's :13
sampen_delta_phi
Min. :1.114
1st Qu.:1.525
Median :1.640
Mean :1.633
3rd Qu.:1.744
Max. :2.154
NA's :13 2 Classic Center of Pressure
60 seconds
2.1 Area (log10)
options(width = 100)
area_rep_anova <- aov_ez("ID", "log10_area", emo_data_clean, within = c("Task"), between = c("Group"))Warning: Missing values for 8 ID(s), which were removed before analysis:
FOJO_20, FOJO_21, FOJO_23, FOJO_31, FOJO_33, FOJO_47, FOJO_54, FOSA_210
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 Group
Task Young Elder
Unpleasant 44 47
Neutral 44 47
Pleasant 44 47area_rain <- ggplot(rep_anova_data, aes(y = Task, x = log10_area, color = Group, fill = Group)) +
stat_halfeye(
trim = FALSE,
.width = 0,
justification = -.15,
alpha = .4,
point_colour = NA) +
geom_point(size = 2, alpha = .4, position = position_jitter(width = 0, height = .05))
suppressWarnings(print(area_rain))
# area_group_rain <- ggplot(rep_anova_data, aes(y = fct_rev(Group), x = log10_area, color = Group, fill = Group)) +
# ggtitle("Area") +
# ylab("Group") +
# stat_halfeye(
# trim = FALSE,
# adjust = .75,
# .width = 0,
# justification = -.15,
# alpha = .4,
# point_colour = NA) +
# geom_boxplot(width = .15, alpha = .2, outlier.shape = NA) +
# geom_point(size = 2, alpha = .4, position = position_jitter(width = 0, height = .05)) +
# theme(legend.position='none')
# suppressWarnings(print(area_group_rain))
# area_task_rain <- ggplot(rep_anova_data, aes(y = Task, x = log10_area, color = Task, fill = Task)) +
# ggtitle("Area") +
# ylab("Task") +
# stat_halfeye(
# trim = FALSE,
# adjust = .75,
# .width = 0,
# justification = -.15,
# alpha = .4,
# point_colour = NA) +
# geom_boxplot(width = .15, alpha = .2, outlier.shape = NA) +
# geom_point(size = 2, alpha = .4, position = position_jitter(width = 0, height = .05)) +
# theme(legend.position='none')
# suppressWarnings(print(area_task_rain))
area_afex_plot <-
afex_plot(
area_rep_anova,
x = "Task",
trace = "Group",
error = "between",
error_arg = list(width = .15),
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"
Anova Table (Type 3 tests)
Response: log10_area
Effect df MSE F ges p.value
1 Group 1, 89 0.18 5.06 * .040 .027
2 Task 1.98, 175.83 0.03 4.98 ** .015 .008
3 Group:Task 1.98, 175.83 0.03 2.16 .006 .119
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘+’ 0.1 ‘ ’ 1
Sphericity correction method: GG $emmeans
Group emmean SE df lower.CL upper.CL
Young 2.14 0.0371 89 2.06 2.21
Elder 2.25 0.0359 89 2.18 2.33
Results are averaged over the levels of: Task
Confidence level used: 0.95
$contrasts
contrast estimate SE df t.ratio p.value
Young - Elder -0.116 0.0517 89 -2.250 0.0269
Results are averaged over the levels of: Task
____________________________________________________________________________________________________
$emmeans
Task emmean SE df lower.CL upper.CL
Unpleasant 2.20 0.0299 89 2.14 2.26
Neutral 2.15 0.0311 89 2.09 2.21
Pleasant 2.23 0.0294 89 2.18 2.29
Results are averaged over the levels of: Group
Confidence level used: 0.95
$contrasts
contrast estimate SE df t.ratio p.value
Unpleasant - Neutral 0.0536 0.0282 89 1.901 0.1442
Unpleasant - Pleasant -0.0301 0.0267 89 -1.127 0.5000
Neutral - Pleasant -0.0837 0.0256 89 -3.267 0.0044
Results are averaged over the levels of: Group
P value adjustment: tukey method for comparing a family of 3 estimates
____________________________________________________________________________________________________2.2 Ellipse Major Axis (log10)
options(width = 100)
log10_axis1_rep_anova <- aov_ez("ID", "log10_axis1", emo_data_clean, within = c("Task"), between = c("Group"))Warning: Missing values for 8 ID(s), which were removed before analysis:
FOJO_20, FOJO_21, FOJO_23, FOJO_31, FOJO_33, FOJO_47, FOJO_54, FOSA_210
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 Group
Task Young Elder
Unpleasant 44 47
Neutral 44 47
Pleasant 44 47log10_axis1_rain <- ggplot(rep_anova_data, aes(y = Task, x = log10_axis1, color = Group, fill = Group)) +
stat_halfeye(
trim = FALSE,
.width = 0,
justification = -.15,
alpha = .4,
point_colour = NA) +
geom_point(size = 2, alpha = .4, position = position_jitter(width = 0, height = .05))
suppressWarnings(print(log10_axis1_rain))
log10_axis1_afex_plot <-
afex_plot(
log10_axis1_rep_anova,
x = "Task",
trace = "Group",
error = "between",
error_arg = list(width = .15),
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"
Anova Table (Type 3 tests)
Response: log10_axis1
Effect df MSE F ges p.value
1 Group 1, 89 0.04 2.01 .016 .159
2 Task 1.95, 173.90 0.01 11.67 *** .034 <.001
3 Group:Task 1.95, 173.90 0.01 1.94 .006 .148
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘+’ 0.1 ‘ ’ 1
Sphericity correction method: GG $emmeans
Task emmean SE df lower.CL upper.CL
Unpleasant 1.05 0.0138 89 1.019 1.07
Neutral 1.01 0.0148 89 0.985 1.04
Pleasant 1.08 0.0150 89 1.048 1.11
Results are averaged over the levels of: Group
Confidence level used: 0.95
$contrasts
contrast estimate SE df t.ratio p.value
Unpleasant - Neutral 0.0322 0.0121 89 2.652 0.0254
Unpleasant - Pleasant -0.0312 0.0133 89 -2.349 0.0544
Neutral - Pleasant -0.0634 0.0139 89 -4.568 <.0001
Results are averaged over the levels of: Group
P value adjustment: tukey method for comparing a family of 3 estimates
____________________________________________________________________________________________________2.3 Ellipse Minor Axis (log10)
options(width = 100)
log10_axis2_rep_anova <- aov_ez("ID", "log10_axis2", emo_data_clean, within = c("Task"), between = c("Group"))Warning: Missing values for 8 ID(s), which were removed before analysis:
FOJO_20, FOJO_21, FOJO_23, FOJO_31, FOJO_33, FOJO_47, FOJO_54, FOSA_210
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 Group
Task Young Elder
Unpleasant 44 47
Neutral 44 47
Pleasant 44 47log10_axis2_rain <- ggplot(rep_anova_data, aes(y = Task, x = log10_axis2, color = Group, fill = Group)) +
stat_halfeye(
trim = FALSE,
.width = 0,
justification = -.15,
alpha = .4,
point_colour = NA) +
geom_point(size = 2, alpha = .4, position = position_jitter(width = 0, height = .05))
suppressWarnings(print(log10_axis2_rain))
log10_axis2_afex_plot <-
afex_plot(
log10_axis2_rep_anova,
x = "Task",
trace = "Group",
error = "between",
error_arg = list(width = .15),
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"
Anova Table (Type 3 tests)
Response: log10_axis2
Effect df MSE F ges p.value
1 Group 1, 89 0.08 5.58 * .042 .020
2 Task 1.96, 174.31 0.02 0.76 .003 .467
3 Group:Task 1.96, 174.31 0.02 1.91 .006 .153
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘+’ 0.1 ‘ ’ 1
Sphericity correction method: GG $emmeans
Group emmean SE df lower.CL upper.CL
Young 0.612 0.0247 89 0.563 0.661
Elder 0.694 0.0239 89 0.646 0.741
Results are averaged over the levels of: Task
Confidence level used: 0.95
$contrasts
contrast estimate SE df t.ratio p.value
Young - Elder -0.0811 0.0343 89 -2.363 0.0203
Results are averaged over the levels of: Task
____________________________________________________________________________________________________2.4 Mean Distance (log10)
options(width = 100)
mdist_rep_anova <- aov_ez("ID", "log10_mdist", emo_data_clean, within = c("Task"), between = c("Group"))Warning: Missing values for 8 ID(s), which were removed before analysis:
FOJO_20, FOJO_21, FOJO_23, FOJO_31, FOJO_33, FOJO_47, FOJO_54, FOSA_210
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 Group
Task Young Elder
Unpleasant 44 47
Neutral 44 47
Pleasant 44 47mdist_rain <- ggplot(rep_anova_data, aes(y = Task, x = log10_mdist, color = Group, fill = Group)) +
stat_halfeye(
trim = FALSE,
.width = 0,
justification = -.15,
alpha = .4,
point_colour = NA) +
geom_point(size = 2, alpha = .4, position = position_jitter(width = 0, height = .05))
suppressWarnings(print(mdist_rain))
mdist_afex_plot <-
afex_plot(
mdist_rep_anova,
x = "Task",
trace = "Group",
error = "between",
error_arg = list(width = .15),
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"
Anova Table (Type 3 tests)
Response: log10_mdist
Effect df MSE F ges p.value
1 Group 1, 89 0.04 3.11 + .025 .081
2 Task 1.98, 176.23 0.01 8.49 *** .024 <.001
3 Group:Task 1.98, 176.23 0.01 2.01 .006 .138
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘+’ 0.1 ‘ ’ 1
Sphericity correction method: GG $emmeans
Task emmean SE df lower.CL upper.CL
Unpleasant 0.627 0.0138 89 0.600 0.654
Neutral 0.604 0.0145 89 0.575 0.633
Pleasant 0.655 0.0144 89 0.627 0.684
Results are averaged over the levels of: Group
Confidence level used: 0.95
$contrasts
contrast estimate SE df t.ratio p.value
Unpleasant - Neutral 0.0233 0.0119 89 1.953 0.1300
Unpleasant - Pleasant -0.0282 0.0126 89 -2.239 0.0703
Neutral - Pleasant -0.0514 0.0130 89 -3.960 0.0004
Results are averaged over the levels of: Group
P value adjustment: tukey method for comparing a family of 3 estimates
____________________________________________________________________________________________________2.5 Root Mean Velocity (log10)
options(width = 100)
rmv_rep_anova <- aov_ez("ID", "log10_rmv", emo_data_clean, within = c("Task"), between = c("Group"))Warning: Missing values for 8 ID(s), which were removed before analysis:
FOJO_20, FOJO_21, FOJO_23, FOJO_31, FOJO_33, FOJO_47, FOJO_54, FOSA_210
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 Group
Task Young Elder
Unpleasant 44 47
Neutral 44 47
Pleasant 44 47rmv_rain <- ggplot(rep_anova_data, aes(y = Task, x = log10_rmv, color = Group, fill = Group)) +
stat_halfeye(
trim = FALSE,
.width = 0,
justification = -.15,
alpha = .4,
point_colour = NA) +
geom_point(size = 2, alpha = .4, position = position_jitter(width = 0, height = .05))
suppressWarnings(print(rmv_rain))
rmv_afex_plot <-
afex_plot(
rmv_rep_anova,
x = "Task",
trace = "Group",
error = "between",
error_arg = list(width = .15),
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"
Anova Table (Type 3 tests)
Response: log10_rmv
Effect df MSE F ges p.value
1 Group 1, 89 0.03 15.66 *** .132 <.001
2 Task 1.92, 171.30 0.00 79.03 *** .107 <.001
3 Group:Task 1.92, 171.30 0.00 0.35 <.001 .696
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘+’ 0.1 ‘ ’ 1
Sphericity correction method: GG $emmeans
Group emmean SE df lower.CL upper.CL
Young 0.911 0.0156 89 0.880 0.942
Elder 0.996 0.0150 89 0.967 1.026
Results are averaged over the levels of: Task
Confidence level used: 0.95
$contrasts
contrast estimate SE df t.ratio p.value
Young - Elder -0.0856 0.0216 89 -3.958 0.0002
Results are averaged over the levels of: Task
____________________________________________________________________________________________________
$emmeans
Task emmean SE df lower.CL upper.CL
Unpleasant 0.967 0.0121 89 0.943 0.991
Neutral 0.902 0.0108 89 0.880 0.923
Pleasant 0.992 0.0120 89 0.968 1.016
Results are averaged over the levels of: Group
Confidence level used: 0.95
$contrasts
contrast estimate SE df t.ratio p.value
Unpleasant - Neutral 0.0655 0.00798 89 8.208 <.0001
Unpleasant - Pleasant -0.0247 0.00670 89 -3.678 0.0012
Neutral - Pleasant -0.0901 0.00749 89 -12.027 <.0001
Results are averaged over the levels of: Group
P value adjustment: tukey method for comparing a family of 3 estimates
____________________________________________________________________________________________________2.6 Root Mean Square X (log10)
options(width = 100)
rmsx_rep_anova <- aov_ez("ID", "log10_rmsx", emo_data_clean, within = c("Task"), between = c("Group"))Warning: Missing values for 8 ID(s), which were removed before analysis:
FOJO_20, FOJO_21, FOJO_23, FOJO_31, FOJO_33, FOJO_47, FOJO_54, FOSA_210
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 Group
Task Young Elder
Unpleasant 44 47
Neutral 44 47
Pleasant 44 47rmsx_rain <- ggplot(rep_anova_data, aes(y = Task, x = log10_rmsx, color = Group, fill = Group)) +
stat_halfeye(
trim = FALSE,
.width = 0,
justification = -.15,
alpha = .4,
point_colour = NA) +
geom_point(size = 2, alpha = .4, position = position_jitter(width = 0, height = .05))
suppressWarnings(print(rmsx_rain))
rmsx_afex_plot <-
afex_plot(
rmsx_rep_anova,
x = "Task",
trace = "Group",
error = "between",
error_arg = list(width = .15),
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"
Anova Table (Type 3 tests)
Response: log10_rmsx
Effect df MSE F ges p.value
1 Group 1, 89 0.09 5.88 * .043 .017
2 Task 1.91, 169.60 0.02 0.19 <.001 .820
3 Group:Task 1.91, 169.60 0.02 1.31 .005 .271
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘+’ 0.1 ‘ ’ 1
Sphericity correction method: GG $emmeans
Group emmean SE df lower.CL upper.CL
Young 0.261 0.0262 89 0.209 0.314
Elder 0.350 0.0254 89 0.300 0.400
Results are averaged over the levels of: Task
Confidence level used: 0.95
$contrasts
contrast estimate SE df t.ratio p.value
Young - Elder -0.0884 0.0365 89 -2.424 0.0174
Results are averaged over the levels of: Task
____________________________________________________________________________________________________2.7 Root Mean Square Y (log10)
options(width = 100)
rmsy_rep_anova <- aov_ez("ID", "log10_rmsy", emo_data_clean, within = c("Task"), between = c("Group"))Warning: Missing values for 8 ID(s), which were removed before analysis:
FOJO_20, FOJO_21, FOJO_23, FOJO_31, FOJO_33, FOJO_47, FOJO_54, FOSA_210
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 Group
Task Young Elder
Unpleasant 44 47
Neutral 44 47
Pleasant 44 47rmsy_rain <- ggplot(rep_anova_data, aes(y = Task, x = log10_rmsy, color = Group, fill = Group)) +
stat_halfeye(
trim = FALSE,
.width = 0,
justification = -.15,
alpha = .4,
point_colour = NA) +
geom_point(size = 2, alpha = .4, position = position_jitter(width = 0, height = .05))
suppressWarnings(print(rmsy_rain))
rmsy_afex_plot <-
afex_plot(
rmsy_rep_anova,
x = "Task",
trace = "Group",
error = "between",
error_arg = list(width = .15),
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"
Anova Table (Type 3 tests)
Response: log10_rmsy
Effect df MSE F ges p.value
1 Group 1, 89 0.04 1.54 .012 .219
2 Task 1.86, 165.67 0.01 15.71 *** .048 <.001
3 Group:Task 1.86, 165.67 0.01 1.79 .006 .172
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘+’ 0.1 ‘ ’ 1
Sphericity correction method: GG $emmeans
Task emmean SE df lower.CL upper.CL
Unpleasant 0.644 0.0133 89 0.618 0.671
Neutral 0.606 0.0142 89 0.578 0.634
Pleasant 0.680 0.0150 89 0.650 0.709
Results are averaged over the levels of: Group
Confidence level used: 0.95
$contrasts
contrast estimate SE df t.ratio p.value
Unpleasant - Neutral 0.0382 0.0114 89 3.357 0.0033
Unpleasant - Pleasant -0.0353 0.0132 89 -2.662 0.0248
Neutral - Pleasant -0.0735 0.0145 89 -5.061 <.0001
Results are averaged over the levels of: Group
P value adjustment: tukey method for comparing a family of 3 estimates
____________________________________________________________________________________________________2.8 Mean Power Frequency X (log10)
options(width = 100)
MPFx_rep_anova <- aov_ez("ID", "log10_MPFx", emo_data_clean, within = c("Task"), between = c("Group"))Warning: Missing values for 8 ID(s), which were removed before analysis:
FOJO_20, FOJO_21, FOJO_23, FOJO_31, FOJO_33, FOJO_47, FOJO_54, FOSA_210
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 Group
Task Young Elder
Unpleasant 44 47
Neutral 44 47
Pleasant 44 47MPFx_rain <- ggplot(rep_anova_data, aes(y = Task, x = log10_MPFx, color = Group, fill = Group)) +
stat_halfeye(
trim = FALSE,
.width = 0,
justification = -.15,
alpha = .4,
point_colour = NA) +
geom_point(size = 2, alpha = .4, position = position_jitter(width = 0, height = .05))
suppressWarnings(print(MPFx_rain))
MPFx_afex_plot <-
afex_plot(
MPFx_rep_anova,
x = "Task",
trace = "Group",
error = "between",
error_arg = list(width = .15),
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"
Anova Table (Type 3 tests)
Response: log10_MPFx
Effect df MSE F ges p.value
1 Group 1, 89 0.09 3.08 + .019 .083
2 Task 1.98, 176.01 0.03 3.10 * .015 .048
3 Group:Task 1.98, 176.01 0.03 2.81 + .014 .063
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘+’ 0.1 ‘ ’ 1
Sphericity correction method: GG $emmeans
Task emmean SE df lower.CL upper.CL
Unpleasant -2.73 0.0222 89 -2.78 -2.69
Neutral -2.78 0.0244 89 -2.83 -2.73
Pleasant -2.71 0.0241 89 -2.76 -2.66
Results are averaged over the levels of: Group
Confidence level used: 0.95
$contrasts
contrast estimate SE df t.ratio p.value
Unpleasant - Neutral 0.0438 0.0257 89 1.699 0.2111
Unpleasant - Pleasant -0.0223 0.0282 89 -0.791 0.7093
Neutral - Pleasant -0.0661 0.0269 89 -2.453 0.0422
Results are averaged over the levels of: Group
P value adjustment: tukey method for comparing a family of 3 estimates
____________________________________________________________________________________________________2.9 Mean Power Frequency Y (log10)
options(width = 100)
MPFy_rep_anova <- aov_ez("ID", "log10_MPFy", emo_data_clean, within = c("Task"), between = c("Group"))Warning: Missing values for 8 ID(s), which were removed before analysis:
FOJO_20, FOJO_21, FOJO_23, FOJO_31, FOJO_33, FOJO_47, FOJO_54, FOSA_210
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 Group
Task Young Elder
Unpleasant 44 47
Neutral 44 47
Pleasant 44 47MPFy_rain <- ggplot(rep_anova_data, aes(y = Task, x = log10_MPFy, color = Group, fill = Group)) +
stat_halfeye(
trim = FALSE,
.width = 0,
justification = -.15,
alpha = .4,
point_colour = NA) +
geom_point(size = 2, alpha = .4, position = position_jitter(width = 0, height = .05))
suppressWarnings(print(MPFy_rain))
MPFy_afex_plot <-
afex_plot(
MPFy_rep_anova,
x = "Task",
trace = "Group",
error = "between",
error_arg = list(width = .15),
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"
Anova Table (Type 3 tests)
Response: log10_MPFy
Effect df MSE F ges p.value
1 Group 1, 89 0.06 8.33 ** .058 .005
2 Task 1.90, 169.31 0.02 13.01 *** .047 <.001
3 Group:Task 1.90, 169.31 0.02 2.24 .008 .112
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘+’ 0.1 ‘ ’ 1
Sphericity correction method: GG $emmeans
Group emmean SE df lower.CL upper.CL
Young -2.85 0.0212 89 -2.89 -2.80
Elder -2.76 0.0205 89 -2.80 -2.72
Results are averaged over the levels of: Task
Confidence level used: 0.95
$contrasts
contrast estimate SE df t.ratio p.value
Young - Elder -0.0852 0.0295 89 -2.886 0.0049
Results are averaged over the levels of: Task
____________________________________________________________________________________________________
$emmeans
Task emmean SE df lower.CL upper.CL
Unpleasant -2.78 0.0158 89 -2.81 -2.75
Neutral -2.86 0.0195 89 -2.90 -2.82
Pleasant -2.77 0.0188 89 -2.81 -2.73
Results are averaged over the levels of: Group
Confidence level used: 0.95
$contrasts
contrast estimate SE df t.ratio p.value
Unpleasant - Neutral 0.0761 0.0169 89 4.492 0.0001
Unpleasant - Pleasant -0.0084 0.0174 89 -0.482 0.8802
Neutral - Pleasant -0.0844 0.0202 89 -4.183 0.0002
Results are averaged over the levels of: Group
P value adjustment: tukey method for comparing a family of 3 estimates
____________________________________________________________________________________________________2.10 Forward Movement
options(width = 100)
forward_mov_rep_anova <- aov_ez("ID", "forward_mov", emo_data_clean, within = c("Task"), between = c("Group"))Warning: Missing values for 8 ID(s), which were removed before analysis:
FOJO_20, FOJO_21, FOJO_23, FOJO_31, FOJO_33, FOJO_47, FOJO_54, FOSA_210
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 Group
Task Young Elder
Unpleasant 44 47
Neutral 44 47
Pleasant 44 47forward_mov_rain <- ggplot(rep_anova_data, aes(y = Task, x = forward_mov, color = Group, fill = Group)) +
stat_halfeye(
trim = FALSE,
.width = 0,
justification = -.15,
alpha = .4,
point_colour = NA) +
geom_point(size = 2, alpha = .4, position = position_jitter(width = 0, height = .05))
suppressWarnings(print(forward_mov_rain))
forward_mov_afex_plot <-
afex_plot(
forward_mov_rep_anova,
x = "Task",
trace = "Group",
error = "between",
error_arg = list(width = .15),
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"
Anova Table (Type 3 tests)
Response: forward_mov
Effect df MSE F ges p.value
1 Group 1, 89 0.01 0.05 <.001 .826
2 Task 1.92, 171.06 0.01 5.56 ** .036 .005
3 Group:Task 1.92, 171.06 0.01 1.57 .010 .213
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘+’ 0.1 ‘ ’ 1
Sphericity correction method: GG $emmeans
Task emmean SE df lower.CL upper.CL
Unpleasant 0.517 0.00975 89 0.498 0.537
Neutral 0.496 0.00910 89 0.478 0.514
Pleasant 0.477 0.00795 89 0.462 0.493
Results are averaged over the levels of: Group
Confidence level used: 0.95
$contrasts
contrast estimate SE df t.ratio p.value
Unpleasant - Neutral 0.0213 0.0131 89 1.622 0.2418
Unpleasant - Pleasant 0.0400 0.0116 89 3.442 0.0025
Neutral - Pleasant 0.0187 0.0112 89 1.676 0.2201
Results are averaged over the levels of: Group
P value adjustment: tukey method for comparing a family of 3 estimates
____________________________________________________________________________________________________3 Entropy and Center of Pressure
3.1 Sample Entropy X (log10)
options(width = 100)
log10_sampen_x_rep_anova <- aov_ez("ID", "log10_sampen_x", emo_data_clean, within = c("Task"), between = c("Group"))Warning: Missing values for 8 ID(s), which were removed before analysis:
FOJO_20, FOJO_21, FOJO_23, FOJO_31, FOJO_33, FOJO_47, FOJO_54, FOSA_210
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 Group
Task Young Elder
Unpleasant 44 47
Neutral 44 47
Pleasant 44 47log10_sampen_x_rain <- ggplot(rep_anova_data, aes(y = Task, x = log10_sampen_x, color = Group, fill = Group)) +
stat_halfeye(
trim = FALSE,
.width = 0,
justification = -.15,
alpha = .4,
point_colour = NA) +
geom_point(size = 2, alpha = .4, position = position_jitter(width = 0, height = .05))
suppressWarnings(print(log10_sampen_x_rain))
log10_sampen_x_afex_plot <-
afex_plot(
log10_sampen_x_rep_anova,
x = "Task",
trace = "Group",
error = "between",
error_arg = list(width = .15),
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"
Anova Table (Type 3 tests)
Response: log10_sampen_x
Effect df MSE F ges p.value
1 Group 1, 89 0.30 5.49 * .036 .021
2 Task 1.89, 167.96 0.10 1.29 .006 .276
3 Group:Task 1.89, 167.96 0.10 1.16 .005 .313
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘+’ 0.1 ‘ ’ 1
Sphericity correction method: GG $emmeans
Group emmean SE df lower.CL upper.CL
Young -0.477 0.0474 89 -0.571 -0.383
Elder -0.631 0.0458 89 -0.722 -0.540
Results are averaged over the levels of: Task
Confidence level used: 0.95
$contrasts
contrast estimate SE df t.ratio p.value
Young - Elder 0.154 0.0659 89 2.343 0.0213
Results are averaged over the levels of: Task
____________________________________________________________________________________________________3.2 Sample Entropy Y (log10)
options(width = 100)
log10_sampen_y_rep_anova <- aov_ez("ID", "log10_sampen_y", emo_data_clean, within = c("Task"), between = c("Group"))Warning: Missing values for 8 ID(s), which were removed before analysis:
FOJO_20, FOJO_21, FOJO_23, FOJO_31, FOJO_33, FOJO_47, FOJO_54, FOSA_210
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 Group
Task Young Elder
Unpleasant 44 47
Neutral 44 47
Pleasant 44 47log10_sampen_y_rain <- ggplot(rep_anova_data, aes(y = Task, x = log10_sampen_y, color = Group, fill = Group)) +
stat_halfeye(
trim = FALSE,
.width = 0,
justification = -.15,
alpha = .4,
point_colour = NA) +
geom_point(size = 2, alpha = .4, position = position_jitter(width = 0, height = .05))
suppressWarnings(print(log10_sampen_y_rain))
log10_sampen_y_afex_plot <-
afex_plot(
log10_sampen_y_rep_anova,
x = "Task",
trace = "Group",
error = "between",
error_arg = list(width = .15),
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"
Anova Table (Type 3 tests)
Response: log10_sampen_y
Effect df MSE F ges p.value
1 Group 1, 89 0.19 0.90 .006 .345
2 Task 1.84, 163.68 0.06 1.39 .006 .251
3 Group:Task 1.84, 163.68 0.06 1.11 .005 .327
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘+’ 0.1 ‘ ’ 1
Sphericity correction method: GG 3.3 Angle Sample Entropy
options(width = 100)
sampen_phi_rad_rep_anova <- aov_ez("ID", "sampen_phi_rad", emo_data_clean, within = c("Task"), between = c("Group"))Warning: Missing values for 8 ID(s), which were removed before analysis:
FOJO_20, FOJO_21, FOJO_23, FOJO_31, FOJO_33, FOJO_47, FOJO_54, FOSA_210
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 Group
Task Young Elder
Unpleasant 44 47
Neutral 44 47
Pleasant 44 47sampen_phi_rad_rain <- ggplot(rep_anova_data, aes(y = Task, x = sampen_phi_rad, color = Group, fill = Group)) +
stat_halfeye(
trim = FALSE,
.width = 0,
justification = -.15,
alpha = .4,
point_colour = NA) +
geom_point(size = 2, alpha = .4, position = position_jitter(width = 0, height = .05))
suppressWarnings(print(sampen_phi_rad_rain))
sampen_phi_rad_afex_plot <-
afex_plot(
sampen_phi_rad_rep_anova,
x = "Task",
trace = "Group",
error = "between",
error_arg = list(width = .15),
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"
Anova Table (Type 3 tests)
Response: sampen_phi_rad
Effect df MSE F ges p.value
1 Group 1, 89 0.05 0.20 .002 .652
2 Task 2.00, 177.80 0.01 6.54 ** .018 .002
3 Group:Task 2.00, 177.80 0.01 1.68 .005 .190
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘+’ 0.1 ‘ ’ 1
Sphericity correction method: GG $emmeans
Task emmean SE df lower.CL upper.CL
Unpleasant 1.01 0.0163 89 0.977 1.04
Neutral 1.05 0.0159 89 1.020 1.08
Pleasant 1.01 0.0153 89 0.978 1.04
Results are averaged over the levels of: Group
Confidence level used: 0.95
$contrasts
contrast estimate SE df t.ratio p.value
Unpleasant - Neutral -0.042316 0.0137 89 -3.099 0.0073
Unpleasant - Pleasant 0.000468 0.0137 89 0.034 0.9994
Neutral - Pleasant 0.042784 0.0134 89 3.202 0.0053
Results are averaged over the levels of: Group
P value adjustment: tukey method for comparing a family of 3 estimates
____________________________________________________________________________________________________3.4 Resultant Vector Sample Entropy
options(width = 100)
sampen_resul_vect_rep_anova <- aov_ez("ID", "sampen_resul_vect", emo_data_clean, within = c("Task"), between = c("Group"))Warning: Missing values for 24 ID(s), which were removed before analysis:
FOJO_08, FOJO_09, FOJO_13, FOJO_16, FOJO_19, FOJO_20, FOJO_21, FOJO_23, FOJO_28, FOJO_31, ... [showing first 10 only]
Below the first few rows (in wide format) of the removed cases with missing data.
Contrasts set to contr.sum for the following variables: Grouprep_anova_data <- sampen_resul_vect_rep_anova$data$long
xtabs(~ Task + Group, data = rep_anova_data) Group
Task Young Elder
Unpleasant 34 41
Neutral 34 41
Pleasant 34 41sampen_resul_vect_rain <- ggplot(rep_anova_data, aes(y = Task, x = sampen_resul_vect, color = Group, fill = Group)) +
stat_halfeye(
trim = FALSE,
.width = 0,
justification = -.15,
alpha = .4,
point_colour = NA) +
geom_point(size = 2, alpha = .4, position = position_jitter(width = 0, height = .05))
suppressWarnings(print(sampen_resul_vect_rain))
sampen_resul_vect_afex_plot <-
afex_plot(
sampen_resul_vect_rep_anova,
x = "Task",
trace = "Group",
error = "between",
error_arg = list(width = .15),
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"
Anova Table (Type 3 tests)
Response: sampen_resul_vect
Effect df MSE F ges p.value
1 Group 1, 73 0.49 1.24 .010 .269
2 Task 1.72, 125.78 0.19 6.61 ** .035 .003
3 Group:Task 1.72, 125.78 0.19 1.70 .009 .191
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘+’ 0.1 ‘ ’ 1
Sphericity correction method: GG $emmeans
Task emmean SE df lower.CL upper.CL
Unpleasant 2.08 0.0681 73 1.94 2.21
Neutral 2.26 0.0572 73 2.15 2.38
Pleasant 2.04 0.0548 73 1.93 2.15
Results are averaged over the levels of: Group
Confidence level used: 0.95
$contrasts
contrast estimate SE df t.ratio p.value
Unpleasant - Neutral -0.1853 0.0782 73 -2.370 0.0527
Unpleasant - Pleasant 0.0401 0.0610 73 0.658 0.7885
Neutral - Pleasant 0.2254 0.0574 73 3.928 0.0006
Results are averaged over the levels of: Group
P value adjustment: tukey method for comparing a family of 3 estimates
____________________________________________________________________________________________________3.5 Angle Change Sample Entropy
options(width = 100)
sampen_delta_phi_rep_anova <- aov_ez("ID", "sampen_delta_phi", emo_data_clean, within = c("Task"), between = c("Group"))Warning: Missing values for 8 ID(s), which were removed before analysis:
FOJO_20, FOJO_21, FOJO_23, FOJO_31, FOJO_33, FOJO_47, FOJO_54, FOSA_210
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 Group
Task Young Elder
Unpleasant 44 47
Neutral 44 47
Pleasant 44 47sampen_delta_phi_rain <- ggplot(rep_anova_data, aes(y = Task, x = sampen_delta_phi, color = Group, fill = Group)) +
stat_halfeye(
trim = FALSE,
.width = 0,
justification = -.15,
alpha = .4,
point_colour = NA) +
geom_point(size = 2, alpha = .4, position = position_jitter(width = 0, height = .05))
suppressWarnings(print(sampen_delta_phi_rain))
sampen_delta_phi_afex_plot <-
afex_plot(
sampen_delta_phi_rep_anova,
x = "Task",
trace = "Group",
error = "between",
error_arg = list(width = .15),
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"
Anova Table (Type 3 tests)
Response: sampen_delta_phi
Effect df MSE F ges p.value
1 Group 1, 89 0.06 18.51 *** .109 <.001
2 Task 2.00, 177.94 0.02 1.52 .007 .222
3 Group:Task 2.00, 177.94 0.02 1.22 .006 .297
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘+’ 0.1 ‘ ’ 1
Sphericity correction method: GG $emmeans
Group emmean SE df lower.CL upper.CL
Young 1.70 0.0208 89 1.66 1.74
Elder 1.57 0.0201 89 1.53 1.61
Results are averaged over the levels of: Task
Confidence level used: 0.95
$contrasts
contrast estimate SE df t.ratio p.value
Young - Elder 0.124 0.0289 89 4.302 <.0001
Results are averaged over the levels of: Task
____________________________________________________________________________________________________4 Heart Activity
4.1 Heart Rate
options(width = 100)
heart_rate_rep_anova <- aov_ez("ID", "heart_rate", emo_data_clean, within = c("Task"), between = c("Group"))Warning: Missing values for 6 ID(s), which were removed before analysis:
FOJO_24, FOJO_37, FOSA_205, FOSA_210, FOSA_213, FOSA_241
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 Group
Task Young Elder
Unpleasant 49 44
Neutral 49 44
Pleasant 49 44heart_rate_rain <- ggplot(rep_anova_data, aes(y = Task, x = heart_rate, color = Group, fill = Group)) +
stat_halfeye(
trim = FALSE,
.width = 0,
justification = -.15,
alpha = .4,
point_colour = NA) +
geom_point(size = 2, alpha = .4, position = position_jitter(width = 0, height = .05))
suppressWarnings(print(heart_rate_rain))
heart_rate_afex_plot <-
afex_plot(
heart_rate_rep_anova,
x = "Task",
trace = "Group",
error = "between",
error_arg = list(width = .15),
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"
Anova Table (Type 3 tests)
Response: heart_rate
Effect df MSE F ges p.value
1 Group 1, 91 398.81 7.65 ** .075 .007
2 Task 1.92, 175.09 8.13 1.16 <.001 .314
3 Group:Task 1.92, 175.09 8.13 1.93 <.001 .149
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘+’ 0.1 ‘ ’ 1
Sphericity correction method: GG $emmeans
Group emmean SE df lower.CL upper.CL
Young 81.6 1.65 91 78.4 84.9
Elder 75.0 1.74 91 71.6 78.5
Results are averaged over the levels of: Task
Confidence level used: 0.95
$contrasts
contrast estimate SE df t.ratio p.value
Young - Elder 6.62 2.39 91 2.765 0.0069
Results are averaged over the levels of: Task
____________________________________________________________________________________________________4.2 Heart Rate, NeuroKit2 method
options(width = 100)
heart_rate_nk2_rep_anova <- aov_ez("ID", "heart_rate_nk2", emo_data_clean, within = c("Task"), between = c("Group"))Warning: Missing values for 1 ID(s), which were removed before analysis:
FOSA_210
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 Group
Task Young Elder
Unpleasant 51 47
Neutral 51 47
Pleasant 51 47heart_rate_nk2_rain <- ggplot(rep_anova_data, aes(y = Task, x = heart_rate_nk2, color = Group, fill = Group)) +
stat_halfeye(
trim = FALSE,
.width = 0,
justification = -.15,
alpha = .4,
point_colour = NA) +
geom_point(size = 2, alpha = .4, position = position_jitter(width = 0, height = .05))
suppressWarnings(print(heart_rate_nk2_rain))
heart_rate_nk2_afex_plot <-
afex_plot(
heart_rate_nk2_rep_anova,
x = "Task",
trace = "Group",
error = "between",
error_arg = list(width = .15),
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"
Anova Table (Type 3 tests)
Response: heart_rate_nk2
Effect df MSE F ges p.value
1 Group 1, 96 400.33 6.46 * .061 .013
2 Task 1.89, 181.75 8.40 1.28 <.001 .280
3 Group:Task 1.89, 181.75 8.40 2.89 + .001 .061
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘+’ 0.1 ‘ ’ 1
Sphericity correction method: GG $emmeans
Group emmean SE df lower.CL upper.CL
Young 81.1 1.62 96 77.9 84.3
Elder 75.1 1.68 96 71.8 78.5
Results are averaged over the levels of: Task
Confidence level used: 0.95
$contrasts
contrast estimate SE df t.ratio p.value
Young - Elder 5.93 2.34 96 2.541 0.0127
Results are averaged over the levels of: Task
____________________________________________________________________________________________________4.3 Root Mean Square of the Successive Differences (log10)
options(width = 100)
rMSSD_rep_anova <- aov_ez("ID", "log10_rMSSD", emo_data_clean, within = c("Task"), between = c("Group"))Warning: Missing values for 6 ID(s), which were removed before analysis:
FOJO_24, FOJO_37, FOSA_205, FOSA_210, FOSA_213, FOSA_241
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 Group
Task Young Elder
Unpleasant 49 44
Neutral 49 44
Pleasant 49 44log10_rMSSD_rain <- ggplot(rep_anova_data, aes(y = Task, x = log10_rMSSD, color = Group, fill = Group)) +
stat_halfeye(
trim = FALSE,
.width = 0,
justification = -.15,
alpha = .4,
point_colour = NA) +
geom_point(size = 2, alpha = .4, position = position_jitter(width = 0, height = .05))
suppressWarnings(print(log10_rMSSD_rain))
rMSSD_afex_plot <-
afex_plot(
rMSSD_rep_anova,
x = "Task",
trace = "Group",
error = "between",
error_arg = list(width = .15),
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"
Anova Table (Type 3 tests)
Response: log10_rMSSD
Effect df MSE F ges p.value
1 Group 1, 91 0.19 20.33 *** .160 <.001
2 Task 1.58, 143.85 0.02 4.50 * .007 .019
3 Group:Task 1.58, 143.85 0.02 0.91 .001 .386
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘+’ 0.1 ‘ ’ 1
Sphericity correction method: GG $emmeans
Group emmean SE df lower.CL upper.CL
Young 1.39 0.0361 91 1.32 1.46
Elder 1.15 0.0381 91 1.08 1.23
Results are averaged over the levels of: Task
Confidence level used: 0.95
$contrasts
contrast estimate SE df t.ratio p.value
Young - Elder 0.237 0.0525 91 4.509 <.0001
Results are averaged over the levels of: Task
____________________________________________________________________________________________________
$emmeans
Task emmean SE df lower.CL upper.CL
Unpleasant 1.29 0.0279 91 1.24 1.35
Neutral 1.28 0.0298 91 1.22 1.34
Pleasant 1.24 0.0276 91 1.18 1.29
Results are averaged over the levels of: Group
Confidence level used: 0.95
$contrasts
contrast estimate SE df t.ratio p.value
Unpleasant - Neutral 0.0151 0.0228 91 0.664 0.7851
Unpleasant - Pleasant 0.0551 0.0190 91 2.897 0.0130
Neutral - Pleasant 0.0400 0.0141 91 2.836 0.0154
Results are averaged over the levels of: Group
P value adjustment: tukey method for comparing a family of 3 estimates
____________________________________________________________________________________________________4.4 Root Mean Square of the Successive Differences (log10), NeuroKit2 method
options(width = 100)
log10_hrv_rmssd_nk2_rep_anova <- aov_ez("ID", "log10_hrv_rmssd_nk2", emo_data_clean, within = c("Task"), between = c("Group"))Warning: Missing values for 1 ID(s), which were removed before analysis:
FOSA_210
Below the first few rows (in wide format) of the removed cases with missing data.
Contrasts set to contr.sum for the following variables: Grouprep_anova_data <- log10_hrv_rmssd_nk2_rep_anova$data$long
xtabs(~ Task + Group, data = rep_anova_data) Group
Task Young Elder
Unpleasant 51 47
Neutral 51 47
Pleasant 51 47log10_hrv_rmssd_nk2_rain <- ggplot(rep_anova_data, aes(y = Task, x = log10_hrv_rmssd_nk2, color = Group, fill = Group)) +
stat_halfeye(
trim = FALSE,
.width = 0,
justification = -.15,
alpha = .4,
point_colour = NA) +
geom_point(size = 2, alpha = .4, position = position_jitter(width = 0, height = .05))
suppressWarnings(print(log10_hrv_rmssd_nk2_rain))
log10_hrv_rmssd_nk2_afex_plot <-
afex_plot(
log10_hrv_rmssd_nk2_rep_anova,
x = "Task",
trace = "Group",
error = "between",
error_arg = list(width = .15),
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"
Anova Table (Type 3 tests)
Response: log10_hrv_rmssd_nk2
Effect df MSE F ges p.value
1 Group 1, 96 0.20 16.00 *** .128 <.001
2 Task 1.92, 184.42 0.01 1.34 .002 .264
3 Group:Task 1.92, 184.42 0.01 0.42 <.001 .649
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘+’ 0.1 ‘ ’ 1
Sphericity correction method: GG $emmeans
Group emmean SE df lower.CL upper.CL
Young 1.40 0.0365 96 1.33 1.47
Elder 1.19 0.0381 96 1.11 1.26
Results are averaged over the levels of: Task
Confidence level used: 0.95
$contrasts
contrast estimate SE df t.ratio p.value
Young - Elder 0.211 0.0528 96 4.000 0.0001
Results are averaged over the levels of: Task
____________________________________________________________________________________________________4.5 ECG-Derived Respiration
options(width = 100)
rsp_rate_rep_anova <- aov_ez("ID", "rsp_rate", emo_data_clean, within = c("Task"), between = c("Group"))Warning: Missing values for 1 ID(s), which were removed before analysis:
FOSA_210
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 Group
Task Young Elder
Unpleasant 51 47
Neutral 51 47
Pleasant 51 47rsp_rate_rain <- ggplot(rep_anova_data, aes(y = Task, x = rsp_rate, color = Group, fill = Group)) +
stat_halfeye(
trim = FALSE,
.width = 0,
justification = -.15,
alpha = .4,
point_colour = NA) +
geom_point(size = 2, alpha = .4, position = position_jitter(width = 0, height = .05))
suppressWarnings(print(rsp_rate_rain))
rsp_rate_afex_plot <-
afex_plot(
rsp_rate_rep_anova,
x = "Task",
trace = "Group",
error = "between",
error_arg = list(width = .15),
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"
Anova Table (Type 3 tests)
Response: rsp_rate
Effect df MSE F ges p.value
1 Group 1, 96 24.12 7.54 ** .058 .007
2 Task 1.86, 178.38 3.48 10.37 *** .022 <.001
3 Group:Task 1.86, 178.38 3.48 1.80 .004 .171
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘+’ 0.1 ‘ ’ 1
Sphericity correction method: GG $emmeans
Group emmean SE df lower.CL upper.CL
Young 12.6 0.397 96 11.8 13.4
Elder 14.2 0.414 96 13.4 15.0
Results are averaged over the levels of: Task
Confidence level used: 0.95
$contrasts
contrast estimate SE df t.ratio p.value
Young - Elder -1.57 0.573 96 -2.745 0.0072
Results are averaged over the levels of: Task
____________________________________________________________________________________________________
$emmeans
Task emmean SE df lower.CL upper.CL
Unpleasant 12.9 0.325 96 12.3 13.6
Neutral 13.2 0.311 96 12.6 13.9
Pleasant 14.1 0.332 96 13.4 14.7
Results are averaged over the levels of: Group
Confidence level used: 0.95
$contrasts
contrast estimate SE df t.ratio p.value
Unpleasant - Neutral -0.305 0.276 96 -1.102 0.5149
Unpleasant - Pleasant -1.132 0.272 96 -4.158 0.0002
Neutral - Pleasant -0.827 0.219 96 -3.779 0.0008
Results are averaged over the levels of: Group
P value adjustment: tukey method for comparing a family of 3 estimates
____________________________________________________________________________________________________4.6 Root mean square of successive differences of the breath-to-breath intervals
options(width = 100)
rrv_rmssd_rep_anova <- aov_ez("ID", "rrv_rmssd", emo_data_clean, within = c("Task"), between = c("Group"))Warning: Missing values for 5 ID(s), which were removed before analysis:
FOJO_41, FOSA_205, FOSA_210, FOSA_213, FOSA_229
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 Group
Task Young Elder
Unpleasant 50 44
Neutral 50 44
Pleasant 50 44rrv_rmssd_rain <- ggplot(rep_anova_data, aes(y = Task, x = rrv_rmssd, color = Group, fill = Group)) +
stat_halfeye(
trim = FALSE,
.width = 0,
justification = -.15,
alpha = .4,
point_colour = NA) +
geom_point(size = 2, alpha = .4, position = position_jitter(width = 0, height = .05))
suppressWarnings(print(rrv_rmssd_rain))
rrv_rmssd_afex_plot <-
afex_plot(
rrv_rmssd_rep_anova,
x = "Task",
trace = "Group",
error = "between",
error_arg = list(width = .15),
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"
Anova Table (Type 3 tests)
Response: rrv_rmssd
Effect df MSE F ges p.value
1 Group 1, 92 1455774.96 0.26 .002 .611
2 Task 1.79, 164.41 636023.44 8.58 *** .039 <.001
3 Group:Task 1.79, 164.41 636023.44 0.25 .001 .758
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘+’ 0.1 ‘ ’ 1
Sphericity correction method: GG $emmeans
Task emmean SE df lower.CL upper.CL
Unpleasant 2212 100.1 92 2013 2411
Neutral 1885 104.0 92 1679 2092
Pleasant 1772 82.9 92 1608 1937
Results are averaged over the levels of: Group
Confidence level used: 0.95
$contrasts
contrast estimate SE df t.ratio p.value
Unpleasant - Neutral 327 127.8 92 2.556 0.0325
Unpleasant - Pleasant 439 99.8 92 4.404 0.0001
Neutral - Pleasant 113 100.7 92 1.121 0.5038
Results are averaged over the levels of: Group
P value adjustment: tukey method for comparing a family of 3 estimates
____________________________________________________________________________________________________4.7 Respiratory Sinus Arrhythmia, RSA (Porges-Bohrer algorithm)
options(width = 100)
rsa_porges_rep_anova <- aov_ez("ID", "rsa_porges", emo_data_clean, within = c("Task"), between = c("Group"))Warning: Missing values for 1 ID(s), which were removed before analysis:
FOSA_210
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 Group
Task Young Elder
Unpleasant 51 47
Neutral 51 47
Pleasant 51 47rsa_porges_rain <- ggplot(rep_anova_data, aes(y = Task, x = rsa_porges, color = Group, fill = Group)) +
stat_halfeye(
trim = FALSE,
.width = 0,
justification = -.15,
alpha = .4,
point_colour = NA) +
geom_point(size = 2, alpha = .4, position = position_jitter(width = 0, height = .05))
suppressWarnings(print(rsa_porges_rain))
rsa_porges_afex_plot <-
afex_plot(
rsa_porges_rep_anova,
x = "Task",
trace = "Group",
error = "between",
error_arg = list(width = .15),
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"
Anova Table (Type 3 tests)
Response: rsa_porges
Effect df MSE F ges p.value
1 Group 1, 96 3.79 59.03 *** .352 <.001
2 Task 1.99, 190.94 0.25 4.31 * .005 .015
3 Group:Task 1.99, 190.94 0.25 0.48 <.001 .617
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘+’ 0.1 ‘ ’ 1
Sphericity correction method: GG $emmeans
Group emmean SE df lower.CL upper.CL
Young -5.82 0.157 96 -6.13 -5.51
Elder -7.57 0.164 96 -7.89 -7.24
Results are averaged over the levels of: Task
Confidence level used: 0.95
$contrasts
contrast estimate SE df t.ratio p.value
Young - Elder 1.75 0.227 96 7.683 <.0001
Results are averaged over the levels of: Task
____________________________________________________________________________________________________
$emmeans
Task emmean SE df lower.CL upper.CL
Unpleasant -6.66 0.115 96 -6.88 -6.43
Neutral -6.61 0.122 96 -6.86 -6.37
Pleasant -6.81 0.126 96 -7.06 -6.56
Results are averaged over the levels of: Group
Confidence level used: 0.95
$contrasts
contrast estimate SE df t.ratio p.value
Unpleasant - Neutral -0.0419 0.0717 96 -0.584 0.8290
Unpleasant - Pleasant 0.1583 0.0697 96 2.272 0.0647
Neutral - Pleasant 0.2002 0.0743 96 2.694 0.0225
Results are averaged over the levels of: Group
P value adjustment: tukey method for comparing a family of 3 estimates
____________________________________________________________________________________________________5 Electrodermal Activity
Resultados muuuuuy tentativos por la calidad errática de los registros
5.1 Mean Phasic Component (log10)
options(width = 100)
log10_ave_phasic_eda_rep_anova = aov_ez("ID", "log10_ave_phasic_eda", emo_data_clean, within = c("Task"), between = c("Group"))Warning: Missing values for 11 ID(s), which were removed before analysis:
FOJO_12, FOJO_18, FOJO_22, FOJO_24, FOJO_26, FOJO_27, FOJO_29, FOJO_33, FOJO_47, FOJO_52, ... [showing first 10 only]
Below the first few rows (in wide format) of the removed cases with missing data.
Contrasts set to contr.sum for the following variables: Grouprep_anova_data <- log10_ave_phasic_eda_rep_anova$data$long
xtabs(~ Task + Group, data = rep_anova_data) Group
Task Young Elder
Unpleasant 41 47
Neutral 41 47
Pleasant 41 47log10_ave_phasic_eda_rain <- ggplot(rep_anova_data, aes(y = Task, x = log10_ave_phasic_eda, color = Group, fill = Group)) +
stat_halfeye(
trim = FALSE,
.width = 0,
justification = -.15,
alpha = .4,
point_colour = NA) +
geom_point(size = 2, alpha = .4, position = position_jitter(width = 0, height = .05))
suppressWarnings(print(log10_ave_phasic_eda_rain))
log10_ave_phasic_eda_afex_plot <-
afex_plot(
log10_ave_phasic_eda_rep_anova,
x = "Task",
trace = "Group",
error = "between",
error_arg = list(width = .15),
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"
Anova Table (Type 3 tests)
Response: log10_ave_phasic_eda
Effect df MSE F ges p.value
1 Group 1, 86 1.48 4.87 * .046 .030
2 Task 1.91, 164.13 0.14 49.85 *** .083 <.001
3 Group:Task 1.91, 164.13 0.14 1.96 .004 .146
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘+’ 0.1 ‘ ’ 1
Sphericity correction method: GG $emmeans
Group emmean SE df lower.CL upper.CL
Young -0.592 0.110 86 -0.81 -0.374
Elder -0.923 0.102 86 -1.13 -0.719
Results are averaged over the levels of: Task
Confidence level used: 0.95
$contrasts
contrast estimate SE df t.ratio p.value
Young - Elder 0.331 0.15 86 2.208 0.0299
Results are averaged over the levels of: Task
____________________________________________________________________________________________________
$emmeans
Task emmean SE df lower.CL upper.CL
Unpleasant -0.477 0.0684 86 -0.613 -0.341
Neutral -1.036 0.0898 86 -1.215 -0.857
Pleasant -0.759 0.0852 86 -0.928 -0.589
Results are averaged over the levels of: Group
Confidence level used: 0.95
$contrasts
contrast estimate SE df t.ratio p.value
Unpleasant - Neutral 0.559 0.0578 86 9.659 <.0001
Unpleasant - Pleasant 0.281 0.0496 86 5.674 <.0001
Neutral - Pleasant -0.277 0.0599 86 -4.629 <.0001
Results are averaged over the levels of: Group
P value adjustment: tukey method for comparing a family of 3 estimates
____________________________________________________________________________________________________5.2 Number of SCR Peaks (log10)
options(width = 100)
log10_scr_peaks_number_rep_anova <- aov_ez("ID", "log10_scr_peaks_number", emo_data_clean, within = c("Task"), between = c("Group"))Warning: Missing values for 1 ID(s), which were removed before analysis:
FOSA_210
Below the first few rows (in wide format) of the removed cases with missing data.
Contrasts set to contr.sum for the following variables: Grouprep_anova_data <- log10_scr_peaks_number_rep_anova$data$long
xtabs(~ Task + Group, data = rep_anova_data) Group
Task Young Elder
Unpleasant 51 47
Neutral 51 47
Pleasant 51 47log10_scr_peaks_number_rain <- ggplot(rep_anova_data, aes(y = Task, x = log10_scr_peaks_number, color = Group, fill = Group)) +
stat_halfeye(
trim = FALSE,
.width = 0,
justification = -.15,
alpha = .4,
point_colour = NA) +
geom_point(size = 2, alpha = .4, position = position_jitter(width = 0, height = .05))
suppressWarnings(print(log10_scr_peaks_number_rain))
log10_scr_peaks_number_afex_plot <-
afex_plot(
log10_scr_peaks_number_rep_anova,
x = "Task",
trace = "Group",
error = "between",
error_arg = list(width = .15),
dodge = -.5,
mapping = c("color"),
point_arg = list(size = 4)
)Warning: Panel(s) show a mixed within-between-design.
Error bars do not allow comparisons across all means.
Suppress error bars with: error = "none"
Anova Table (Type 3 tests)
Response: log10_scr_peaks_number
Effect df MSE F ges p.value
1 Group 1, 96 0.07 0.00 <.001 .958
2 Task 1.69, 162.69 0.08 1.67 .011 .196
3 Group:Task 1.69, 162.69 0.08 0.57 .004 .539
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘+’ 0.1 ‘ ’ 1
Sphericity correction method: GG 5.3 Mean SCR Amplitude (log10)
options(width = 100)
log10_scr_mean_amplitude_rep_anova <- aov_ez("ID", "log10_scr_mean_amplitude", emo_data_clean, within = c("Task"), between = c("Group"))Warning: Missing values for 2 ID(s), which were removed before analysis:
FOSA_210, FOSA_213
Below the first few rows (in wide format) of the removed cases with missing data.
Contrasts set to contr.sum for the following variables: Grouprep_anova_data <- log10_scr_mean_amplitude_rep_anova$data$long
xtabs(~ Task + Group, data = rep_anova_data) Group
Task Young Elder
Unpleasant 51 46
Neutral 51 46
Pleasant 51 46log10_scr_mean_amplitude_rain <- ggplot(rep_anova_data, aes(y = Task, x = log10_scr_mean_amplitude, color = Group, fill = Group)) +
stat_halfeye(
trim = FALSE,
.width = 0,
justification = -.15,
alpha = .4,
point_colour = NA) +
geom_point(size = 2, alpha = .4, position = position_jitter(width = 0, height = .05))
suppressWarnings(print(log10_scr_mean_amplitude_rain))
log10_scr_mean_amplitude_afex_plot <-
afex_plot(
log10_scr_mean_amplitude_rep_anova,
x = "Task",
trace = "Group",
error = "between",
error_arg = list(width = .15),
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"
Anova Table (Type 3 tests)
Response: log10_scr_mean_amplitude
Effect df MSE F ges p.value
1 Group 1, 95 2.20 1.18 .010 .280
2 Task 1.83, 174.24 0.28 17.78 *** .034 <.001
3 Group:Task 1.83, 174.24 0.28 1.35 .003 .262
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘+’ 0.1 ‘ ’ 1
Sphericity correction method: GG $emmeans
Task emmean SE df lower.CL upper.CL
Unpleasant -4.25 0.0935 95 -4.43 -4.06
Neutral -4.68 0.0998 95 -4.88 -4.48
Pleasant -4.42 0.0970 95 -4.61 -4.22
Results are averaged over the levels of: Group
Confidence level used: 0.95
$contrasts
contrast estimate SE df t.ratio p.value
Unpleasant - Neutral 0.432 0.0729 95 5.929 <.0001
Unpleasant - Pleasant 0.168 0.0630 95 2.674 0.0238
Neutral - Pleasant -0.264 0.0822 95 -3.215 0.0050
Results are averaged over the levels of: Group
P value adjustment: tukey method for comparing a family of 3 estimates
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sk + Group, data = rep_anova_data)
log10_axis1_rain <- ggplot(rep_anova_data, aes(y = Task, x = log10_axis1, color = Group, fill = Group)) +
  stat_halfeye(
    trim   = FALSE,
    .width = 0,
    justification = -.15,
    alpha  = .4,
    point_colour = NA) +
  geom_point(size = 2, alpha = .4, position = position_jitter(width = 0, height = .05))
suppressWarnings(print(log10_axis1_rain))
log10_axis1_afex_plot <-
  afex_plot(
    log10_axis1_rep_anova,
    x = "Task",
    trace = "Group",
    error = "between",
    error_arg = list(width = .15),
    dodge = -.5,
    mapping = c("color"),
    point_arg = list(size = 4)
  )
suppressWarnings(print(log10_axis1_afex_plot))
nice(log10_axis1_rep_anova)
a_posteriori(log10_axis1_rep_anova)
```

## Ellipse Minor Axis (log10)
```{r log10_axis2, fig.width = 12}
options(width = 100)
log10_axis2_rep_anova <- aov_ez("ID", "log10_axis2", emo_data_clean, within = c("Task"), between = c("Group"))
rep_anova_data        <- log10_axis2_rep_anova$data$long
xtabs(~ Task + Group, data = rep_anova_data)
log10_axis2_rain <- ggplot(rep_anova_data, aes(y = Task, x = log10_axis2, color = Group, fill = Group)) +
  stat_halfeye(
    trim   = FALSE,
    .width = 0,
    justification = -.15,
    alpha  = .4,
    point_colour = NA) +
  geom_point(size = 2, alpha = .4, position = position_jitter(width = 0, height = .05))
suppressWarnings(print(log10_axis2_rain))
log10_axis2_afex_plot <-
  afex_plot(
    log10_axis2_rep_anova,
    x = "Task",
    trace = "Group",
    error = "between",
    error_arg = list(width = .15),
    dodge = -.5,
    mapping = c("color"),
    point_arg = list(size = 4)
  )
suppressWarnings(print(log10_axis2_afex_plot))
nice(log10_axis2_rep_anova)
a_posteriori(log10_axis2_rep_anova)
```

## Mean Distance (log10)
```{r mdist, fig.width = 12}
options(width = 100)
mdist_rep_anova <- aov_ez("ID", "log10_mdist", emo_data_clean, within = c("Task"), between = c("Group"))
rep_anova_data  <- mdist_rep_anova$data$long
xtabs(~ Task + Group, data = rep_anova_data)
mdist_rain <- ggplot(rep_anova_data, aes(y = Task, x = log10_mdist, color = Group, fill = Group)) +
  stat_halfeye(
    trim   = FALSE,
    .width = 0,
    justification = -.15,
    alpha  = .4,
    point_colour = NA) +
  geom_point(size = 2, alpha = .4, position = position_jitter(width = 0, height = .05))
suppressWarnings(print(mdist_rain))
mdist_afex_plot <-
  afex_plot(
    mdist_rep_anova,
    x = "Task",
    trace = "Group",
    error = "between",
    error_arg = list(width = .15),
    dodge = -.5,
    mapping = c("color"),
    point_arg = list(size = 4)
  )
suppressWarnings(print(mdist_afex_plot))
nice(mdist_rep_anova)
a_posteriori(mdist_rep_anova)
```

## Root Mean Velocity (log10)
```{r rmv, fig.width = 12}
options(width = 100)
rmv_rep_anova  <- aov_ez("ID", "log10_rmv", emo_data_clean, within = c("Task"), between = c("Group"))
rep_anova_data <- rmv_rep_anova$data$long
xtabs(~ Task + Group, data = rep_anova_data)
rmv_rain <- ggplot(rep_anova_data, aes(y = Task, x = log10_rmv, color = Group, fill = Group)) +
  stat_halfeye(
    trim   = FALSE,
    .width = 0,
    justification = -.15,
    alpha  = .4,
    point_colour = NA) +
  geom_point(size = 2, alpha = .4, position = position_jitter(width = 0, height = .05))
suppressWarnings(print(rmv_rain))
rmv_afex_plot <-
  afex_plot(
    rmv_rep_anova,
    x = "Task",
    trace = "Group",
    error = "between",
    error_arg = list(width = .15),
    dodge = -.5,
    mapping = c("color"),
    point_arg = list(size = 4)
  )
suppressWarnings(print(rmv_afex_plot))
nice(rmv_rep_anova)
a_posteriori(rmv_rep_anova)
```

## Root Mean Square X (log10)
```{r rmsx, fig.width = 12}
options(width = 100)
rmsx_rep_anova <- aov_ez("ID", "log10_rmsx", emo_data_clean, within = c("Task"), between = c("Group"))
rep_anova_data <- rmsx_rep_anova$data$long
xtabs(~ Task + Group, data = rep_anova_data)
rmsx_rain <- ggplot(rep_anova_data, aes(y = Task, x = log10_rmsx, color = Group, fill = Group)) +
  stat_halfeye(
    trim   = FALSE,
    .width = 0,
    justification = -.15,
    alpha  = .4,
    point_colour = NA) +
  geom_point(size = 2, alpha = .4, position = position_jitter(width = 0, height = .05))
suppressWarnings(print(rmsx_rain))
rmsx_afex_plot <-
  afex_plot(
    rmsx_rep_anova,
    x = "Task",
    trace = "Group",
    error = "between",
    error_arg = list(width = .15),
    dodge = -.5,
    mapping = c("color"),
    point_arg = list(size = 4)
  )
suppressWarnings(print(rmsx_afex_plot))
nice(rmsx_rep_anova)
a_posteriori(rmsx_rep_anova)
```

## Root Mean Square Y (log10)
```{r rmsy, fig.width = 12}
options(width = 100)
rmsy_rep_anova <- aov_ez("ID", "log10_rmsy", emo_data_clean, within = c("Task"), between = c("Group"))
rep_anova_data <- rmsy_rep_anova$data$long
xtabs(~ Task + Group, data = rep_anova_data)
rmsy_rain <- ggplot(rep_anova_data, aes(y = Task, x = log10_rmsy, color = Group, fill = Group)) +
  stat_halfeye(
    trim   = FALSE,
    .width = 0,
    justification = -.15,
    alpha  = .4,
    point_colour = NA) +
  geom_point(size = 2, alpha = .4, position = position_jitter(width = 0, height = .05))
suppressWarnings(print(rmsy_rain))
rmsy_afex_plot <-
  afex_plot(
    rmsy_rep_anova,
    x = "Task",
    trace = "Group",
    error = "between",
    error_arg = list(width = .15),
    dodge = -.5,
    mapping = c("color"),
    point_arg = list(size = 4)
  )
suppressWarnings(print(rmsy_afex_plot))
nice(rmsy_rep_anova)
a_posteriori(rmsy_rep_anova)
```

## Mean Power Frequency X (log10)
```{r MPFx, fig.width = 12}
options(width = 100)
MPFx_rep_anova <- aov_ez("ID", "log10_MPFx", emo_data_clean, within = c("Task"), between = c("Group"))
rep_anova_data <- MPFx_rep_anova$data$long
xtabs(~ Task + Group, data = rep_anova_data)
MPFx_rain <- ggplot(rep_anova_data, aes(y = Task, x = log10_MPFx, color = Group, fill = Group)) +
  stat_halfeye(
    trim   = FALSE,
    .width = 0,
    justification = -.15,
    alpha  = .4,
    point_colour = NA) +
  geom_point(size = 2, alpha = .4, position = position_jitter(width = 0, height = .05))
suppressWarnings(print(MPFx_rain))
MPFx_afex_plot <-
  afex_plot(
    MPFx_rep_anova,
    x = "Task",
    trace = "Group",
    error = "between",
    error_arg = list(width = .15),
    dodge = -.5,
    mapping = c("color"),
    point_arg = list(size = 4)
  )
suppressWarnings(print(MPFx_afex_plot))
nice(MPFx_rep_anova)
a_posteriori(MPFx_rep_anova)
```

## Mean Power Frequency Y (log10)
```{r MPFy, fig.width = 12}
options(width = 100)
MPFy_rep_anova <- aov_ez("ID", "log10_MPFy", emo_data_clean, within = c("Task"), between = c("Group"))
rep_anova_data <- MPFy_rep_anova$data$long
xtabs(~ Task + Group, data = rep_anova_data)
MPFy_rain <- ggplot(rep_anova_data, aes(y = Task, x = log10_MPFy, color = Group, fill = Group)) +
  stat_halfeye(
    trim   = FALSE,
    .width = 0,
    justification = -.15,
    alpha  = .4,
    point_colour = NA) +
  geom_point(size = 2, alpha = .4, position = position_jitter(width = 0, height = .05))
suppressWarnings(print(MPFy_rain))
MPFy_afex_plot <-
  afex_plot(
    MPFy_rep_anova,
    x = "Task",
    trace = "Group",
    error = "between",
    error_arg = list(width = .15),
    dodge = -.5,
    mapping = c("color"),
    point_arg = list(size = 4)
  )
suppressWarnings(print(MPFy_afex_plot))
nice(MPFy_rep_anova)
a_posteriori(MPFy_rep_anova)
```

## Forward Movement
```{r forward_mov, fig.width = 12}
options(width = 100)
forward_mov_rep_anova <- aov_ez("ID", "forward_mov", emo_data_clean, within = c("Task"), between = c("Group"))
rep_anova_data        <- forward_mov_rep_anova$data$long
xtabs(~ Task + Group, data = rep_anova_data)
forward_mov_rain <- ggplot(rep_anova_data, aes(y = Task, x = forward_mov, color = Group, fill = Group)) +
  stat_halfeye(
    trim   = FALSE,
    .width = 0,
    justification = -.15,
    alpha  = .4,
    point_colour = NA) +
  geom_point(size = 2, alpha = .4, position = position_jitter(width = 0, height = .05))
suppressWarnings(print(forward_mov_rain))
forward_mov_afex_plot <-
  afex_plot(
    forward_mov_rep_anova,
    x = "Task",
    trace = "Group",
    error = "between",
    error_arg = list(width = .15),
    dodge = -.5,
    mapping = c("color"),
    point_arg = list(size = 4)
  )
suppressWarnings(print(forward_mov_afex_plot))
nice(forward_mov_rep_anova)
a_posteriori(forward_mov_rep_anova)
```

# Entropy and Center of Pressure
## Sample Entropy X (log10)
```{r sampen_x, fig.width = 12}
options(width = 100)
log10_sampen_x_rep_anova <- aov_ez("ID", "log10_sampen_x", emo_data_clean, within = c("Task"), between = c("Group"))
rep_anova_data           <- log10_sampen_x_rep_anova$data$long
xtabs(~ Task + Group, data = rep_anova_data)
log10_sampen_x_rain <- ggplot(rep_anova_data, aes(y = Task, x = log10_sampen_x, color = Group, fill = Group)) +
  stat_halfeye(
    trim   = FALSE,
    .width = 0,
    justification = -.15,
    alpha  = .4,
    point_colour = NA) +
  geom_point(size = 2, alpha = .4, position = position_jitter(width = 0, height = .05))
suppressWarnings(print(log10_sampen_x_rain))
log10_sampen_x_afex_plot <-
  afex_plot(
    log10_sampen_x_rep_anova,
    x = "Task",
    trace = "Group",
    error = "between",
    error_arg = list(width = .15),
    dodge = -.5,
    mapping = c("color"),
    point_arg = list(size = 4)
  )
suppressWarnings(print(log10_sampen_x_afex_plot))
nice(log10_sampen_x_rep_anova)
a_posteriori(log10_sampen_x_rep_anova)
```

## Sample Entropy Y (log10)
```{r sampen_y, fig.width = 12}
options(width = 100)
log10_sampen_y_rep_anova <- aov_ez("ID", "log10_sampen_y", emo_data_clean, within = c("Task"), between = c("Group"))
rep_anova_data           <- log10_sampen_y_rep_anova$data$long
xtabs(~ Task + Group, data = rep_anova_data)
log10_sampen_y_rain <- ggplot(rep_anova_data, aes(y = Task, x = log10_sampen_y, color = Group, fill = Group)) +
  stat_halfeye(
    trim   = FALSE,
    .width = 0,
    justification = -.15,
    alpha  = .4,
    point_colour = NA) +
  geom_point(size = 2, alpha = .4, position = position_jitter(width = 0, height = .05))
suppressWarnings(print(log10_sampen_y_rain))
log10_sampen_y_afex_plot <-
  afex_plot(
    log10_sampen_y_rep_anova,
    x = "Task",
    trace = "Group",
    error = "between",
    error_arg = list(width = .15),
    dodge = -.5,
    mapping = c("color"),
    point_arg = list(size = 4)
  )
suppressWarnings(print(log10_sampen_y_afex_plot))
nice(log10_sampen_y_rep_anova)
a_posteriori(log10_sampen_y_rep_anova)
```

## Angle Sample Entropy
```{r sampen_phi_rad, fig.width = 12}
options(width = 100)
sampen_phi_rad_rep_anova <- aov_ez("ID", "sampen_phi_rad", emo_data_clean, within = c("Task"), between = c("Group"))
rep_anova_data           <- sampen_phi_rad_rep_anova$data$long
xtabs(~ Task + Group, data = rep_anova_data)
sampen_phi_rad_rain <- ggplot(rep_anova_data, aes(y = Task, x = sampen_phi_rad, color = Group, fill = Group)) +
  stat_halfeye(
    trim   = FALSE,
    .width = 0,
    justification = -.15,
    alpha  = .4,
    point_colour = NA) +
  geom_point(size = 2, alpha = .4, position = position_jitter(width = 0, height = .05))
suppressWarnings(print(sampen_phi_rad_rain))
sampen_phi_rad_afex_plot <-
  afex_plot(
    sampen_phi_rad_rep_anova,
    x = "Task",
    trace = "Group",
    error = "between",
    error_arg = list(width = .15),
    dodge = -.5,
    mapping = c("color"),
    point_arg = list(size = 4)
  )
suppressWarnings(print(sampen_phi_rad_afex_plot))
nice(sampen_phi_rad_rep_anova)
a_posteriori(sampen_phi_rad_rep_anova)
```

## Resultant Vector Sample Entropy
```{r sampen_resul_vect, fig.width = 12}
options(width = 100)
sampen_resul_vect_rep_anova <- aov_ez("ID", "sampen_resul_vect", emo_data_clean, within = c("Task"), between = c("Group"))
rep_anova_data              <- sampen_resul_vect_rep_anova$data$long
xtabs(~ Task + Group, data = rep_anova_data)
sampen_resul_vect_rain <- ggplot(rep_anova_data, aes(y = Task, x = sampen_resul_vect, color = Group, fill = Group)) +
  stat_halfeye(
    trim   = FALSE,
    .width = 0,
    justification = -.15,
    alpha  = .4,
    point_colour = NA) +
  geom_point(size = 2, alpha = .4, position = position_jitter(width = 0, height = .05))
suppressWarnings(print(sampen_resul_vect_rain))
sampen_resul_vect_afex_plot <-
  afex_plot(
    sampen_resul_vect_rep_anova,
    x = "Task",
    trace = "Group",
    error = "between",
    error_arg = list(width = .15),
    dodge = -.5,
    mapping = c("color"),
    point_arg = list(size = 4)
  )
suppressWarnings(print(sampen_resul_vect_afex_plot))
nice(sampen_resul_vect_rep_anova)
a_posteriori(sampen_resul_vect_rep_anova)
```

## Angle Change Sample Entropy
```{r sampen_delta_phi, fig.width = 12}
options(width = 100)
sampen_delta_phi_rep_anova <- aov_ez("ID", "sampen_delta_phi", emo_data_clean, within = c("Task"), between = c("Group"))
rep_anova_data             <- sampen_delta_phi_rep_anova$data$long
xtabs(~ Task + Group, data = rep_anova_data)
sampen_delta_phi_rain <- ggplot(rep_anova_data, aes(y = Task, x = sampen_delta_phi, color = Group, fill = Group)) +
  stat_halfeye(
    trim   = FALSE,
    .width = 0,
    justification = -.15,
    alpha  = .4,
    point_colour = NA) +
  geom_point(size = 2, alpha = .4, position = position_jitter(width = 0, height = .05))
suppressWarnings(print(sampen_delta_phi_rain))
sampen_delta_phi_afex_plot <-
  afex_plot(
    sampen_delta_phi_rep_anova,
    x = "Task",
    trace = "Group",
    error = "between",
    error_arg = list(width = .15),
    dodge = -.5,
    mapping = c("color"),
    point_arg = list(size = 4)
  )
suppressWarnings(print(sampen_delta_phi_afex_plot))
nice(sampen_delta_phi_rep_anova)
a_posteriori(sampen_delta_phi_rep_anova)
```

# Heart Activity
## Heart Rate
```{r heart_rate, fig.width = 12}
options(width = 100)
heart_rate_rep_anova <- aov_ez("ID", "heart_rate", emo_data_clean, within = c("Task"), between = c("Group"))
rep_anova_data       <- heart_rate_rep_anova$data$long
xtabs(~ Task + Group, data = rep_anova_data)
heart_rate_rain <- ggplot(rep_anova_data, aes(y = Task, x = heart_rate, color = Group, fill = Group)) +
  stat_halfeye(
    trim   = FALSE,
    .width = 0,
    justification = -.15,
    alpha  = .4,
    point_colour = NA) +
  geom_point(size = 2, alpha = .4, position = position_jitter(width = 0, height = .05))
suppressWarnings(print(heart_rate_rain))
heart_rate_afex_plot <-
  afex_plot(
    heart_rate_rep_anova,
    x = "Task",
    trace = "Group",
    error = "between",
    error_arg = list(width = .15),
    dodge = -.5,
    mapping = c("color"),
    point_arg = list(size = 4)
  )
suppressWarnings(print(heart_rate_afex_plot))
nice(heart_rate_rep_anova)
a_posteriori(heart_rate_rep_anova)
```

## Heart Rate, NeuroKit2 method
```{r heart_rate_nk2, fig.width = 12}
options(width = 100)
heart_rate_nk2_rep_anova <- aov_ez("ID", "heart_rate_nk2", emo_data_clean, within = c("Task"), between = c("Group"))
rep_anova_data       <- heart_rate_nk2_rep_anova$data$long
xtabs(~ Task + Group, data = rep_anova_data)
heart_rate_nk2_rain <- ggplot(rep_anova_data, aes(y = Task, x = heart_rate_nk2, color = Group, fill = Group)) +
  stat_halfeye(
    trim   = FALSE,
    .width = 0,
    justification = -.15,
    alpha  = .4,
    point_colour = NA) +
  geom_point(size = 2, alpha = .4, position = position_jitter(width = 0, height = .05))
suppressWarnings(print(heart_rate_nk2_rain))
heart_rate_nk2_afex_plot <-
  afex_plot(
    heart_rate_nk2_rep_anova,
    x = "Task",
    trace = "Group",
    error = "between",
    error_arg = list(width = .15),
    dodge = -.5,
    mapping = c("color"),
    point_arg = list(size = 4)
  )
suppressWarnings(print(heart_rate_nk2_afex_plot))
nice(heart_rate_nk2_rep_anova)
a_posteriori(heart_rate_nk2_rep_anova)
```

## Root Mean Square of the Successive Differences (log10)
```{r rMSSD, fig.width = 12}
options(width = 100)
rMSSD_rep_anova <- aov_ez("ID", "log10_rMSSD", emo_data_clean, within = c("Task"), between = c("Group"))
rep_anova_data  <- rMSSD_rep_anova$data$long
xtabs(~ Task + Group, data = rep_anova_data)
log10_rMSSD_rain <- ggplot(rep_anova_data, aes(y = Task, x = log10_rMSSD, color = Group, fill = Group)) +
  stat_halfeye(
    trim   = FALSE,
    .width = 0,
    justification = -.15,
    alpha  = .4,
    point_colour = NA) +
  geom_point(size = 2, alpha = .4, position = position_jitter(width = 0, height = .05))
suppressWarnings(print(log10_rMSSD_rain))
rMSSD_afex_plot <-
  afex_plot(
    rMSSD_rep_anova,
    x = "Task",
    trace = "Group",
    error = "between",
    error_arg = list(width = .15),
    dodge = -.5,
    mapping = c("color"),
    point_arg = list(size = 4)
  )
suppressWarnings(print(rMSSD_afex_plot))
nice(rMSSD_rep_anova)
a_posteriori(rMSSD_rep_anova)
```

## Root Mean Square of the Successive Differences (log10), NeuroKit2 method
```{r log10_hrv_rmssd_nk2, fig.width = 12}
options(width = 100)
log10_hrv_rmssd_nk2_rep_anova <- aov_ez("ID", "log10_hrv_rmssd_nk2", emo_data_clean, within = c("Task"), between = c("Group"))
rep_anova_data  <- log10_hrv_rmssd_nk2_rep_anova$data$long
xtabs(~ Task + Group, data = rep_anova_data)
log10_hrv_rmssd_nk2_rain <- ggplot(rep_anova_data, aes(y = Task, x = log10_hrv_rmssd_nk2, color = Group, fill = Group)) +
  stat_halfeye(
    trim   = FALSE,
    .width = 0,
    justification = -.15,
    alpha  = .4,
    point_colour = NA) +
  geom_point(size = 2, alpha = .4, position = position_jitter(width = 0, height = .05))
suppressWarnings(print(log10_hrv_rmssd_nk2_rain))
log10_hrv_rmssd_nk2_afex_plot <-
  afex_plot(
    log10_hrv_rmssd_nk2_rep_anova,
    x = "Task",
    trace = "Group",
    error = "between",
    error_arg = list(width = .15),
    dodge = -.5,
    mapping = c("color"),
    point_arg = list(size = 4)
  )
suppressWarnings(print(log10_hrv_rmssd_nk2_afex_plot))
nice(log10_hrv_rmssd_nk2_rep_anova)
a_posteriori(log10_hrv_rmssd_nk2_rep_anova)
```

## ECG-Derived Respiration
```{r rsp_rate, fig.width = 12}
options(width = 100)
rsp_rate_rep_anova <- aov_ez("ID", "rsp_rate", emo_data_clean, within = c("Task"), between = c("Group"))
rep_anova_data     <- rsp_rate_rep_anova$data$long
xtabs(~ Task + Group, data = rep_anova_data)
rsp_rate_rain <- ggplot(rep_anova_data, aes(y = Task, x = rsp_rate, color = Group, fill = Group)) +
  stat_halfeye(
    trim   = FALSE,
    .width = 0,
    justification = -.15,
    alpha  = .4,
    point_colour = NA) +
  geom_point(size = 2, alpha = .4, position = position_jitter(width = 0, height = .05))
suppressWarnings(print(rsp_rate_rain))
rsp_rate_afex_plot <-
  afex_plot(
    rsp_rate_rep_anova,
    x = "Task",
    trace = "Group",
    error = "between",
    error_arg = list(width = .15),
    dodge = -.5,
    mapping = c("color"),
    point_arg = list(size = 4)
  )
suppressWarnings(print(rsp_rate_afex_plot))
nice(rsp_rate_rep_anova)
a_posteriori(rsp_rate_rep_anova)
```

## Root mean square of successive differences of the breath-to-breath intervals
```{r rrv_rmssd, fig.width = 12}
options(width = 100)
rrv_rmssd_rep_anova <- aov_ez("ID", "rrv_rmssd", emo_data_clean, within = c("Task"), between = c("Group"))
rep_anova_data      <- rrv_rmssd_rep_anova$data$long
xtabs(~ Task + Group, data = rep_anova_data)
rrv_rmssd_rain <- ggplot(rep_anova_data, aes(y = Task, x = rrv_rmssd, color = Group, fill = Group)) +
  stat_halfeye(
    trim   = FALSE,
    .width = 0,
    justification = -.15,
    alpha  = .4,
    point_colour = NA) +
  geom_point(size = 2, alpha = .4, position = position_jitter(width = 0, height = .05))
suppressWarnings(print(rrv_rmssd_rain))
rrv_rmssd_afex_plot <-
  afex_plot(
    rrv_rmssd_rep_anova,
    x = "Task",
    trace = "Group",
    error = "between",
    error_arg = list(width = .15),
    dodge = -.5,
    mapping = c("color"),
    point_arg = list(size = 4)
  )
suppressWarnings(print(rrv_rmssd_afex_plot))
nice(rrv_rmssd_rep_anova)
a_posteriori(rrv_rmssd_rep_anova)
```

## Respiratory Sinus Arrhythmia, RSA (Porges-Bohrer algorithm)
```{r rsa_porges, fig.width = 12}
options(width = 100)
rsa_porges_rep_anova <- aov_ez("ID", "rsa_porges", emo_data_clean, within = c("Task"), between = c("Group"))
rep_anova_data       <- rsa_porges_rep_anova$data$long
xtabs(~ Task + Group, data = rep_anova_data)
rsa_porges_rain <- ggplot(rep_anova_data, aes(y = Task, x = rsa_porges, color = Group, fill = Group)) +
  stat_halfeye(
    trim   = FALSE,
    .width = 0,
    justification = -.15,
    alpha  = .4,
    point_colour = NA) +
  geom_point(size = 2, alpha = .4, position = position_jitter(width = 0, height = .05))
suppressWarnings(print(rsa_porges_rain))
rsa_porges_afex_plot <-
  afex_plot(
    rsa_porges_rep_anova,
    x = "Task",
    trace = "Group",
    error = "between",
    error_arg = list(width = .15),
    dodge = -.5,
    mapping = c("color"),
    point_arg = list(size = 4)
  )
suppressWarnings(print(rsa_porges_afex_plot))
nice(rsa_porges_rep_anova)
a_posteriori(rsa_porges_rep_anova)
```

# Electrodermal Activity
Resultados muuuuuy tentativos por la calidad errática de los registros

## Mean Phasic Component (log10)
```{r ave_phasic_eda, fig.width = 12}
options(width = 100)
log10_ave_phasic_eda_rep_anova = aov_ez("ID", "log10_ave_phasic_eda", emo_data_clean, within = c("Task"), between = c("Group"))
rep_anova_data       <- log10_ave_phasic_eda_rep_anova$data$long
xtabs(~ Task + Group, data = rep_anova_data)
log10_ave_phasic_eda_rain <- ggplot(rep_anova_data, aes(y = Task, x = log10_ave_phasic_eda, color = Group, fill = Group)) +
  stat_halfeye(
    trim   = FALSE,
    .width = 0,
    justification = -.15,
    alpha  = .4,
    point_colour = NA) +
  geom_point(size = 2, alpha = .4, position = position_jitter(width = 0, height = .05))
suppressWarnings(print(log10_ave_phasic_eda_rain))
log10_ave_phasic_eda_afex_plot <-
  afex_plot(
    log10_ave_phasic_eda_rep_anova,
    x = "Task",
    trace = "Group",
    error = "between",
    error_arg = list(width = .15),
    dodge = -.5,
    mapping = c("color"),
    point_arg = list(size = 4)
  )
suppressWarnings(print(log10_ave_phasic_eda_afex_plot))
nice(log10_ave_phasic_eda_rep_anova)
a_posteriori(log10_ave_phasic_eda_rep_anova)
```

## Number of SCR Peaks (log10)
```{r scr_peaks_number, fig.width = 12}
options(width = 100)
log10_scr_peaks_number_rep_anova <- aov_ez("ID", "log10_scr_peaks_number", emo_data_clean, within = c("Task"), between = c("Group"))
rep_anova_data                   <- log10_scr_peaks_number_rep_anova$data$long
xtabs(~ Task + Group, data = rep_anova_data)
log10_scr_peaks_number_rain <- ggplot(rep_anova_data, aes(y = Task, x = log10_scr_peaks_number, color = Group, fill = Group)) +
  stat_halfeye(
    trim   = FALSE,
    .width = 0,
    justification = -.15,
    alpha  = .4,
    point_colour = NA) +
  geom_point(size = 2, alpha = .4, position = position_jitter(width = 0, height = .05))
suppressWarnings(print(log10_scr_peaks_number_rain))
log10_scr_peaks_number_afex_plot <-
  afex_plot(
    log10_scr_peaks_number_rep_anova,
    x = "Task",
    trace = "Group",
    error = "between",
    error_arg = list(width = .15),
    dodge = -.5,
    mapping = c("color"),
    point_arg = list(size = 4)
  )
suppressWarnings(print(log10_scr_peaks_number_afex_plot))
nice(log10_scr_peaks_number_rep_anova)
a_posteriori(log10_scr_peaks_number_rep_anova)
```

## Mean SCR Amplitude (log10)
```{r scr_mean_amplitude, fig.width = 12}
options(width = 100)
log10_scr_mean_amplitude_rep_anova <- aov_ez("ID", "log10_scr_mean_amplitude", emo_data_clean, within = c("Task"), between = c("Group"))
rep_anova_data                     <- log10_scr_mean_amplitude_rep_anova$data$long
xtabs(~ Task + Group, data = rep_anova_data)
log10_scr_mean_amplitude_rain <- ggplot(rep_anova_data, aes(y = Task, x = log10_scr_mean_amplitude, color = Group, fill = Group)) +
  stat_halfeye(
    trim   = FALSE,
    .width = 0,
    justification = -.15,
    alpha  = .4,
    point_colour = NA) +
  geom_point(size = 2, alpha = .4, position = position_jitter(width = 0, height = .05))
suppressWarnings(print(log10_scr_mean_amplitude_rain))
log10_scr_mean_amplitude_afex_plot <-
  afex_plot(
    log10_scr_mean_amplitude_rep_anova,
    x = "Task",
    trace = "Group",
    error = "between",
    error_arg = list(width = .15),
    dodge = -.5,
    mapping = c("color"),
    point_arg = list(size = 4)
  )
suppressWarnings(print(log10_scr_mean_amplitude_afex_plot))
nice(log10_scr_mean_amplitude_rep_anova)
a_posteriori(log10_scr_mean_amplitude_rep_anova)
```
