Postural Stability and Emotions
Parkinson, Elder & Young groups
Alvaro Rivera-Rei
17 septiembre, 2024
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$`Pr(>F)`
for (i in 1:length(p_values)) {
if (p_values[i] <= sig_level) {
print(emmeans(afex_aov, factors[[i]], contr = "pairwise"))
cat(rep("_", 100), '\n', sep = "")
}
}
}data_dir <- paste('..', 'data', sep = '/')
emo_data_name <- paste(data_dir, 'emo_data_clean.csv', sep='/')
emo_data_clean <- read.csv(emo_data_name, header = TRUE)
emo_data_clean <- emo_data_clean[(emo_data_clean$Group == 'Elder' | emo_data_clean$Group == 'Parkinson' | emo_data_clean$Group == 'Young'), ]
emo_data_clean$Group <- factor(emo_data_clean$Group, levels = c("Parkinson", "Elder", "Young"))
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$scr_peaks_number <- ifelse(is.na(emo_data_clean$log10_ave_phasic_eda), NA, emo_data_clean$scr_peaks_number)
emo_data_clean$scr_mean_amplitude <- ifelse(is.na(emo_data_clean$log10_ave_phasic_eda), NA, emo_data_clean$scr_mean_amplitude)
emo_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:440 Parkinson:144 Length:440 Unpleasant:147 Yes:440 EP_201 : 3
Class :character Elder :143 Class :character Neutral :146 EP_202 : 3
Mode :character Young :153 Mode :character Pleasant :147 EP_203 : 3
EP_204 : 3
EP_205 : 3
EP_206 : 3
(Other):422
area axis1 axis2 angle mdist
Min. : 17.68 Min. : 3.974 Min. : 0.9813 Min. :-3.092 Min. : 1.593
1st Qu.: 116.37 1st Qu.: 9.763 1st Qu.: 3.7232 1st Qu.: 1.399 1st Qu.: 3.747
Median : 200.56 Median :12.190 Median : 5.0301 Median : 1.561 Median : 4.734
Mean : 303.83 Mean :13.444 Mean : 6.0463 Mean : 1.430 Mean : 5.180
3rd Qu.: 311.18 3rd Qu.:15.341 3rd Qu.: 7.2270 3rd Qu.: 1.684 3rd Qu.: 5.875
Max. :4001.39 Max. :42.231 Max. :32.9300 Max. : 3.102 Max. :17.666
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.: 7.858 1st Qu.: 1.6277 1st Qu.: 3.828 1st Qu.:0.001262 1st Qu.:0.000122
Median : 9.728 Median : 2.2620 Median : 4.829 Median :0.001749 Median :0.000122
Mean :11.300 Mean : 2.8084 Mean : 5.266 Mean :0.002077 Mean :0.000370
3rd Qu.:12.940 3rd Qu.: 3.2879 3rd Qu.: 6.139 3rd Qu.:0.002692 3rd Qu.:0.000244
Max. :80.110 Max. :16.5939 Max. :16.609 Max. :0.011074 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.:0.000366 1st Qu.:0.004639 1st Qu.:0.001188 1st Qu.:0.000122 1st Qu.:0.000488
Median :0.000610 Median :0.005981 Median :0.001642 Median :0.000244 Median :0.000977
Mean :0.001172 Mean :0.006705 Mean :0.001754 Mean :0.000440 Mean :0.001053
3rd Qu.:0.001587 3rd Qu.:0.007935 3rd Qu.:0.002136 3rd Qu.:0.000488 3rd Qu.:0.001465
Max. :0.006836 Max. :0.042358 Max. :0.007089 Max. :0.004395 Max. :0.004028
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. :0.00232 Min. :0.002094 Min. :0.01149
1st Qu.:0.003662 1st Qu.:0.4461 1st Qu.:0.12872 1st Qu.:0.060350 1st Qu.:1.69528
Median :0.005615 Median :0.5019 Median :0.26343 Median :0.102774 Median :2.03909
Mean :0.005926 Mean :0.4973 Mean :0.34954 Mean :0.127311 Mean :2.03472
3rd Qu.:0.007263 3rd Qu.:0.5491 3rd Qu.:0.45592 3rd Qu.:0.177671 3rd Qu.:2.38488
Max. :0.037598 Max. :0.8000 Max. :1.93525 Max. :0.553328 Max. :3.72220
NA's :13 NA's :13 NA's :13 NA's :13 NA's :42
sampen_phi_rad sampen_delta_phi heart_rate rMSSD ave_phasic_eda
Min. :0.4158 Min. :0.8149 Min. : 48.39 Min. : 2.789 Min. :-0.00311
1st Qu.:0.9114 1st Qu.:1.4672 1st Qu.: 69.78 1st Qu.: 9.204 1st Qu.: 0.04333
Median :1.0219 Median :1.6088 Median : 77.46 Median : 18.706 Median : 0.17157
Mean :1.0231 Mean :1.5954 Mean : 77.93 Mean : 26.912 Mean : 0.47591
3rd Qu.:1.1258 3rd Qu.:1.7275 3rd Qu.: 85.85 3rd Qu.: 30.071 3rd Qu.: 0.55579
Max. :1.5488 Max. :2.1536 Max. :117.80 Max. :834.339 Max. : 6.13301
NA's :13 NA's :13 NA's :29 NA's :29 NA's :35
ave_tonic_eda recurrence_rate_x determinism_x ave_diag_len_x longest_diag_x
Min. :-0.335 Min. :0.006863 Min. :0.9845 Min. : 6.371 Min. : 86.0
1st Qu.: 6.029 1st Qu.:0.059306 1st Qu.:0.9992 1st Qu.: 21.461 1st Qu.: 272.5
Median : 9.823 Median :0.080541 Median :0.9996 Median : 31.261 Median : 366.0
Mean :13.172 Mean :0.086796 Mean :0.9991 Mean : 36.792 Mean : 471.3
3rd Qu.:16.972 3rd Qu.:0.105593 3rd Qu.:0.9998 3rd Qu.: 43.262 3rd Qu.: 506.5
Max. :59.139 Max. :0.774053 Max. :1.0000 Max. :647.996 Max. :7464.0
NA's :35 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. :0.9743 Min. : 3.571 Min. : 27.0 Min. : 1.309
1st Qu.:3.921 1st Qu.:0.9995 1st Qu.: 25.399 1st Qu.: 268.5 1st Qu.: 9.595
Median :4.316 Median :0.9998 Median : 37.805 Median : 384.0 Median : 12.555
Mean :4.265 Mean :0.9994 Mean : 44.841 Mean : 469.3 Mean : 17.574
3rd Qu.:4.674 3rd Qu.:0.9999 3rd Qu.: 52.932 3rd Qu.: 562.0 3rd Qu.: 17.083
Max. :7.066 Max. :1.0000 Max. :824.210 Max. :3738.0 Max. :149.007
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. : 202.2 Min. :0.5353 Min. :0.2742 Min. :0.3536 Min. : 5.00
1st Qu.: 421.5 1st Qu.:0.6928 1st Qu.:0.5139 1st Qu.:0.5481 1st Qu.:22.00
Median : 488.9 Median :0.7201 Median :0.5470 Median :0.5892 Median :24.00
Mean : 513.0 Mean :0.7176 Mean :0.5462 Mean :0.5877 Mean :23.76
3rd Qu.: 566.0 3rd Qu.:0.7468 3rd Qu.:0.5834 3rd Qu.:0.6291 3rd Qu.:26.00
Max. :1391.6 Max. :0.8138 Max. :0.9388 Max. :0.9503 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. :2.000 Min. :0.01388 Min. :0.9924 Min. : 8.478 Min. : 88.0
1st Qu.:3.000 1st Qu.:0.06327 1st Qu.:0.9996 1st Qu.: 27.314 1st Qu.: 277.0
Median :3.000 Median :0.08000 Median :0.9998 Median : 36.409 Median : 356.0
Mean :3.096 Mean :0.08244 Mean :0.9996 Mean : 39.348 Mean : 406.1
3rd Qu.:3.000 3rd Qu.:0.09771 3rd Qu.:0.9999 3rd Qu.: 49.286 3rd Qu.: 465.5
Max. :4.000 Max. :0.34314 Max. :1.0000 Max. :160.089 Max. :2576.0
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. :2.764 Min. :0.9940 Min. : 7.05 Min. : 114.0 Min. : 2.963
1st Qu.:4.186 1st Qu.:0.9998 1st Qu.: 31.12 1st Qu.: 277.5 1st Qu.:10.358
Median :4.512 Median :0.9999 Median : 40.98 Median : 362.0 Median :12.634
Mean :4.489 Mean :0.9998 Mean : 45.35 Mean : 400.1 Mean :14.121
3rd Qu.:4.826 3rd Qu.:0.9999 3rd Qu.: 55.15 3rd Qu.: 478.0 3rd Qu.:15.993
Max. :6.013 Max. :1.0000 Max. :186.71 Max. :1802.0 Max. :73.026
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
Min. : 238.9 Min. :0.6259 Min. :0.3494 Min. :0.3999 Min. : 7.0
1st Qu.: 480.2 1st Qu.:0.7390 1st Qu.:0.5259 1st Qu.:0.5617 1st Qu.:21.0
Median : 537.4 Median :0.7602 Median :0.5498 Median :0.5908 Median :22.0
Mean : 544.6 Mean :0.7566 Mean :0.5518 Mean :0.5914 Mean :22.2
3rd Qu.: 609.3 3rd Qu.:0.7792 3rd Qu.:0.5777 3rd Qu.:0.6219 3rd Qu.:24.0
Max. :1047.6 Max. :0.8367 Max. :0.7132 Max. :0.7432 Max. :34.0
NA's :13 NA's :13 NA's :13 NA's :13 NA's :13
dim_y delay_x2 dim_x2 tol_x2 delay_y2 dim_y2
Min. :2.00 Min. : 6.00 Min. :6 Min. : 1.422 Min. :10.00 Min. :5.000
1st Qu.:3.00 1st Qu.:10.50 1st Qu.:6 1st Qu.: 1.571 1st Qu.:12.50 1st Qu.:5.500
Median :3.00 Median :15.00 Median :6 Median : 1.720 Median :15.00 Median :6.000
Mean :3.04 Mean :12.33 Mean :6 Mean :126.246 Mean :13.67 Mean :5.667
3rd Qu.:3.00 3rd Qu.:15.50 3rd Qu.:6 3rd Qu.:188.658 3rd Qu.:15.50 3rd Qu.:6.000
Max. :4.00 Max. :16.00 Max. :6 Max. :375.596 Max. :16.00 Max. :6.000
NA's :13 NA's :437 NA's :437 NA's :437 NA's :437 NA's :437
tol_y2 rsp_rate rrv_rmssd rsa_porges scr_peaks_number
Min. : 1.689 Min. : 4.309 Min. : 117.9 Min. :-11.321 Min. : 1.000
1st Qu.: 1.874 1st Qu.:11.761 1st Qu.:1286.3 1st Qu.: -8.249 1st Qu.: 5.000
Median : 2.059 Median :14.138 Median :1827.1 Median : -7.000 Median : 8.000
Mean : 58.299 Mean :13.881 Mean :1920.7 Mean : -7.028 Mean : 9.129
3rd Qu.: 86.603 3rd Qu.:16.117 3rd Qu.:2536.5 3rd Qu.: -5.788 3rd Qu.:10.000
Max. :171.147 Max. :23.878 Max. :4469.5 Max. : -2.056 Max. :81.000
NA's :437 NA's :14 NA's :38
scr_mean_amplitude heart_rate_nk2 hrv_rmssd_nk2 num_ID log10_area
Min. :0.00000 Min. : 48.35 Min. : 2.742 201 : 6 Min. :1.247
1st Qu.:0.00001 1st Qu.: 69.65 1st Qu.: 9.352 202 : 6 1st Qu.:2.066
Median :0.00004 Median : 77.30 Median : 19.168 203 : 6 Median :2.302
Mean :0.00011 Mean : 77.80 Mean : 28.247 204 : 6 Mean :2.309
3rd Qu.:0.00014 3rd Qu.: 85.28 3rd Qu.: 30.425 205 : 6 3rd Qu.:2.493
Max. :0.00150 Max. :117.54 Max. :660.322 206 : 6 Max. :3.602
NA's :38 (Other):404 NA's :13
log10_axis1 log10_axis2 log10_mdist log10_rmv log10_rmsx
Min. :0.5992 Min. :-0.008213 Min. :0.2023 Min. :0.6446 Min. :-0.3797
1st Qu.:0.9896 1st Qu.: 0.570910 1st Qu.:0.5737 1st Qu.:0.8953 1st Qu.: 0.2116
Median :1.0860 Median : 0.701573 Median :0.6752 Median :0.9880 Median : 0.3545
Mean :1.0953 Mean : 0.716761 Mean :0.6810 Mean :1.0126 Mean : 0.3719
3rd Qu.:1.1859 3rd Qu.: 0.858955 3rd Qu.:0.7690 3rd Qu.:1.1119 3rd Qu.: 0.5169
Max. :1.6256 Max. : 1.517592 Max. :1.2471 Max. :1.9037 Max. : 1.2199
NA's :13 NA's :13 NA's :13 NA's :13 NA's :13
log10_rmsy log10_MPFx log10_MPFy log10_PEAKx log10_PEAKy
Min. :0.2033 Min. :-3.537 Min. :-3.411 Min. :-3.913 Min. :-3.913
1st Qu.:0.5829 1st Qu.:-2.899 1st Qu.:-2.925 1st Qu.:-3.913 1st Qu.:-3.913
Median :0.6838 Median :-2.757 Median :-2.785 Median :-3.913 Median :-3.612
Mean :0.6905 Mean :-2.747 Mean :-2.800 Mean :-3.715 Mean :-3.574
3rd Qu.:0.7881 3rd Qu.:-2.570 3rd Qu.:-2.670 3rd Qu.:-3.612 3rd Qu.:-3.311
Max. :1.2203 Max. :-1.956 Max. :-2.149 Max. :-2.165 Max. :-2.357
NA's :13 NA's :13 NA's :13 NA's :13 NA's :13
log10_F50x log10_F50y log10_F95x log10_F95y log10_sampen_x
Min. :-3.612 Min. :-3.612 Min. :-3.311 Min. :-2.872 Min. :-2.6345
1st Qu.:-3.436 1st Qu.:-3.311 1st Qu.:-2.334 1st Qu.:-2.436 1st Qu.:-0.8904
Median :-3.214 Median :-3.010 Median :-2.223 Median :-2.251 Median :-0.5793
Mean :-3.123 Mean :-3.062 Mean :-2.223 Mean :-2.277 Mean :-0.6485
3rd Qu.:-2.799 3rd Qu.:-2.834 3rd Qu.:-2.100 3rd Qu.:-2.139 3rd Qu.:-0.3411
Max. :-2.165 Max. :-2.395 Max. :-1.373 Max. :-1.425 Max. : 0.2867
NA's :13 NA's :13 NA's :13 NA's :13 NA's :13
log10_sampen_y log10_rMSSD log10_hrv_rmssd_nk2 log10_ave_phasic_eda log10_scr_peaks_number
Min. :-2.6791 Min. :0.4455 Min. :0.4381 Min. :-3.4462 Min. :0.0000
1st Qu.:-1.2193 1st Qu.:0.9640 1st Qu.:0.9709 1st Qu.:-1.3529 1st Qu.:0.6990
Median :-0.9881 Median :1.2720 Median :1.2826 Median :-0.7592 Median :0.9031
Mean :-1.0219 Mean :1.2464 Mean :1.2597 Mean :-0.8540 Mean :0.8786
3rd Qu.:-0.7504 3rd Qu.:1.4781 3rd Qu.:1.4832 3rd Qu.:-0.2366 3rd Qu.:1.0000
Max. :-0.2570 Max. :2.9213 Max. :2.8198 Max. : 0.7877 Max. :1.9085
NA's :13 NA's :29 NA's :38 NA's :38
log10_scr_mean_amplitude
Min. :-7.212
1st Qu.:-4.987
Median :-4.358
Mean :-4.493
3rd Qu.:-3.842
Max. :-2.824
NA's :39 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.:2.066 1st Qu.:0.9896 1st Qu.: 0.570910 1st Qu.:0.5737 1st Qu.:0.8953
Median :2.302 Median :1.0860 Median : 0.701573 Median :0.6752 Median :0.9880
Mean :2.309 Mean :1.0953 Mean : 0.716761 Mean :0.6810 Mean :1.0126
3rd Qu.:2.493 3rd Qu.:1.1859 3rd Qu.: 0.858955 3rd Qu.:0.7690 3rd Qu.:1.1119
Max. :3.602 Max. :1.6256 Max. : 1.517592 Max. :1.2471 Max. :1.9037
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.: 0.2116 1st Qu.:0.5829
Median : 0.3545 Median :0.6838
Mean : 0.3719 Mean :0.6905
3rd Qu.: 0.5169 3rd Qu.:0.7881
Max. : 1.2199 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.:-2.899 1st Qu.:-2.925 1st Qu.:-3.913 1st Qu.:-3.913 1st Qu.:-3.436
Median :-2.757 Median :-2.785 Median :-3.913 Median :-3.612 Median :-3.214
Mean :-2.747 Mean :-2.800 Mean :-3.715 Mean :-3.574 Mean :-3.123
3rd Qu.:-2.570 3rd Qu.:-2.670 3rd Qu.:-3.612 3rd Qu.:-3.311 3rd Qu.:-2.799
Max. :-1.956 Max. :-2.149 Max. :-2.165 Max. :-2.357 Max. :-2.165
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.:-2.334 1st Qu.:-2.436
Median :-3.010 Median :-2.223 Median :-2.251
Mean :-3.062 Mean :-2.223 Mean :-2.277
3rd Qu.:-2.834 3rd Qu.:-2.100 3rd Qu.:-2.139
Max. :-2.395 Max. :-1.373 Max. :-1.425
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. :-2.6345 Min. :-2.6791 Min. :0.01149 Min. :0.4158
1st Qu.:0.4461 1st Qu.:-0.8904 1st Qu.:-1.2193 1st Qu.:1.69528 1st Qu.:0.9114
Median :0.5019 Median :-0.5793 Median :-0.9881 Median :2.03909 Median :1.0219
Mean :0.4973 Mean :-0.6485 Mean :-1.0219 Mean :2.03472 Mean :1.0231
3rd Qu.:0.5491 3rd Qu.:-0.3411 3rd Qu.:-0.7504 3rd Qu.:2.38488 3rd Qu.:1.1258
Max. :0.8000 Max. : 0.2867 Max. :-0.2570 Max. :3.72220 Max. :1.5488
NA's :13 NA's :13 NA's :13 NA's :42 NA's :13
sampen_delta_phi
Min. :0.8149
1st Qu.:1.4672
Median :1.6088
Mean :1.5954
3rd Qu.:1.7275
Max. :2.1536
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 Parkinson Elder Young
Unpleasant 48 47 44
Neutral 48 47 44
Pleasant 48 47 44area_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 2, 136 0.22 25.41 *** .220 <.001
2 Task 1.98, 269.49 0.04 14.09 *** .025 <.001
3 Group:Task 3.96, 269.49 0.04 2.45 * .009 .047
---
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
Parkinson 2.53 0.0394 136 2.45 2.61
Elder 2.25 0.0398 136 2.18 2.33
Young 2.14 0.0412 136 2.06 2.22
Results are averaged over the levels of: Task
Confidence level used: 0.95
$contrasts
contrast estimate SE df t.ratio p.value
Parkinson - Elder 0.277 0.0560 136 4.937 <.0001
Parkinson - Young 0.393 0.0570 136 6.895 <.0001
Elder - Young 0.116 0.0573 136 2.030 0.1089
Results are averaged over the levels of: Task
P value adjustment: tukey method for comparing a family of 3 estimates
____________________________________________________________________________________________________
$emmeans
Task emmean SE df lower.CL upper.CL
Unpleasant 2.33 0.0274 136 2.27 2.38
Neutral 2.24 0.0250 136 2.19 2.29
Pleasant 2.36 0.0275 136 2.30 2.41
Results are averaged over the levels of: Group
Confidence level used: 0.95
$contrasts
contrast estimate SE df t.ratio p.value
Unpleasant - Neutral 0.0884 0.0239 136 3.706 0.0009
Unpleasant - Pleasant -0.0276 0.0226 136 -1.221 0.4427
Neutral - Pleasant -0.1161 0.0220 136 -5.279 <.0001
Results are averaged over the levels of: Group
P value adjustment: tukey method for comparing a family of 3 estimates
____________________________________________________________________________________________________
$emmeans
Group Task emmean SE df lower.CL upper.CL
Parkinson Unpleasant 2.58 0.0466 136 2.48 2.67
Elder Unpleasant 2.29 0.0471 136 2.20 2.39
Young Unpleasant 2.12 0.0487 136 2.02 2.21
Parkinson Neutral 2.42 0.0425 136 2.33 2.50
Elder Neutral 2.20 0.0430 136 2.12 2.29
Young Neutral 2.10 0.0444 136 2.01 2.18
Parkinson Pleasant 2.60 0.0468 136 2.51 2.69
Elder Pleasant 2.27 0.0473 136 2.17 2.36
Young Pleasant 2.20 0.0488 136 2.11 2.30
Confidence level used: 0.95
$contrasts
contrast estimate SE df t.ratio p.value
Parkinson Unpleasant - Elder Unpleasant 0.28402 0.0663 136 4.285 0.0011
Parkinson Unpleasant - Young Unpleasant 0.46000 0.0674 136 6.824 <.0001
Parkinson Unpleasant - Parkinson Neutral 0.15809 0.0406 136 3.896 0.0047
Parkinson Unpleasant - Elder Neutral 0.37182 0.0634 136 5.865 <.0001
Parkinson Unpleasant - Young Neutral 0.47941 0.0644 136 7.447 <.0001
Parkinson Unpleasant - Parkinson Pleasant -0.02274 0.0385 136 -0.591 0.9996
Parkinson Unpleasant - Elder Pleasant 0.30927 0.0664 136 4.659 0.0003
Parkinson Unpleasant - Young Pleasant 0.37454 0.0675 136 5.547 <.0001
Elder Unpleasant - Young Unpleasant 0.17597 0.0678 136 2.597 0.1976
Elder Unpleasant - Parkinson Neutral -0.12594 0.0635 136 -1.985 0.5567
Elder Unpleasant - Elder Neutral 0.08779 0.0410 136 2.141 0.4503
Elder Unpleasant - Young Neutral 0.19539 0.0647 136 3.018 0.0721
Elder Unpleasant - Parkinson Pleasant -0.30677 0.0664 136 -4.621 0.0003
Elder Unpleasant - Elder Pleasant 0.02525 0.0389 136 0.649 0.9993
Elder Unpleasant - Young Pleasant 0.09052 0.0679 136 1.334 0.9194
Young Unpleasant - Parkinson Neutral -0.30191 0.0646 136 -4.671 0.0002
Young Unpleasant - Elder Neutral -0.08818 0.0649 136 -1.358 0.9113
Young Unpleasant - Young Neutral 0.01941 0.0424 136 0.458 0.9999
Young Unpleasant - Parkinson Pleasant -0.48274 0.0675 136 -7.150 <.0001
Young Unpleasant - Elder Pleasant -0.15072 0.0679 136 -2.221 0.3982
Young Unpleasant - Young Pleasant -0.08545 0.0402 136 -2.125 0.4609
Parkinson Neutral - Elder Neutral 0.21373 0.0604 136 3.537 0.0157
Parkinson Neutral - Young Neutral 0.32132 0.0615 136 5.228 <.0001
Parkinson Neutral - Parkinson Pleasant -0.18083 0.0374 136 -4.836 0.0001
Parkinson Neutral - Elder Pleasant 0.15118 0.0636 136 2.378 0.3044
Parkinson Neutral - Young Pleasant 0.21646 0.0648 136 3.343 0.0288
Elder Neutral - Young Neutral 0.10759 0.0618 136 1.742 0.7197
Elder Neutral - Parkinson Pleasant -0.39456 0.0635 136 -6.213 <.0001
Elder Neutral - Elder Pleasant -0.06255 0.0378 136 -1.655 0.7723
Elder Neutral - Young Pleasant 0.00273 0.0651 136 0.042 1.0000
Young Neutral - Parkinson Pleasant -0.50216 0.0645 136 -7.787 <.0001
Young Neutral - Elder Pleasant -0.17014 0.0648 136 -2.624 0.1867
Young Neutral - Young Pleasant -0.10487 0.0391 136 -2.685 0.1631
Parkinson Pleasant - Elder Pleasant 0.33202 0.0665 136 4.993 0.0001
Parkinson Pleasant - Young Pleasant 0.39729 0.0676 136 5.874 <.0001
Elder Pleasant - Young Pleasant 0.06527 0.0680 136 0.960 0.9887
P value adjustment: tukey method for comparing a family of 9 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 Parkinson Elder Young
Unpleasant 48 47 44
Neutral 48 47 44
Pleasant 48 47 44log10_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 2, 136 0.05 20.88 *** .189 <.001
2 Task 1.97, 267.89 0.01 22.72 *** .039 <.001
3 Group:Task 3.94, 267.89 0.01 1.43 .005 .225
---
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
Parkinson 1.19 0.0186 136 1.156 1.23
Elder 1.06 0.0188 136 1.027 1.10
Young 1.03 0.0194 136 0.991 1.07
Results are averaged over the levels of: Task
Confidence level used: 0.95
$contrasts
contrast estimate SE df t.ratio p.value
Parkinson - Elder 0.1282 0.0264 136 4.858 <.0001
Parkinson - Young 0.1634 0.0268 136 6.086 <.0001
Elder - Young 0.0352 0.0270 136 1.303 0.3961
Results are averaged over the levels of: Task
P value adjustment: tukey method for comparing a family of 3 estimates
____________________________________________________________________________________________________
$emmeans
Task emmean SE df lower.CL upper.CL
Unpleasant 1.10 0.0120 136 1.08 1.12
Neutral 1.06 0.0123 136 1.03 1.08
Pleasant 1.13 0.0132 136 1.10 1.16
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 0.0100 136 4.188 0.0001
Unpleasant - Pleasant -0.0297 0.0109 136 -2.730 0.0195
Neutral - Pleasant -0.0717 0.0111 136 -6.448 <.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 Parkinson Elder Young
Unpleasant 48 47 44
Neutral 48 47 44
Pleasant 48 47 44log10_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 2, 136 0.09 20.21 *** .174 <.001
2 Task 1.95, 265.09 0.02 5.00 ** .011 .008
3 Group:Task 3.90, 265.09 0.02 2.37 + .010 .055
---
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
Parkinson 0.842 0.0254 136 0.792 0.892
Elder 0.694 0.0257 136 0.643 0.744
Young 0.612 0.0266 136 0.560 0.665
Results are averaged over the levels of: Task
Confidence level used: 0.95
$contrasts
contrast estimate SE df t.ratio p.value
Parkinson - Elder 0.1484 0.0362 136 4.103 0.0002
Parkinson - Young 0.2295 0.0368 136 6.240 <.0001
Elder - Young 0.0811 0.0370 136 2.195 0.0757
Results are averaged over the levels of: Task
P value adjustment: tukey method for comparing a family of 3 estimates
____________________________________________________________________________________________________
$emmeans
Task emmean SE df lower.CL upper.CL
Unpleasant 0.732 0.0186 136 0.695 0.769
Neutral 0.686 0.0166 136 0.653 0.718
Pleasant 0.730 0.0180 136 0.694 0.766
Results are averaged over the levels of: Group
Confidence level used: 0.95
$contrasts
contrast estimate SE df t.ratio p.value
Unpleasant - Neutral 0.04650 0.0178 136 2.606 0.0273
Unpleasant - Pleasant 0.00207 0.0163 136 0.127 0.9912
Neutral - Pleasant -0.04443 0.0156 136 -2.851 0.0139
Results are averaged over the levels of: Group
P value adjustment: tukey method for comparing a family of 3 estimates
____________________________________________________________________________________________________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 Parkinson Elder Young
Unpleasant 48 47 44
Neutral 48 47 44
Pleasant 48 47 44mdist_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 2, 136 0.05 24.40 *** .217 <.001
2 Task 1.98, 268.63 0.01 18.33 *** .030 <.001
3 Group:Task 3.95, 268.63 0.01 1.80 .006 .130
---
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
Parkinson 0.785 0.0185 136 0.748 0.821
Elder 0.650 0.0187 136 0.613 0.687
Young 0.607 0.0193 136 0.569 0.645
Results are averaged over the levels of: Task
Confidence level used: 0.95
$contrasts
contrast estimate SE df t.ratio p.value
Parkinson - Elder 0.1342 0.0263 136 5.113 <.0001
Parkinson - Young 0.1775 0.0267 136 6.646 <.0001
Elder - Young 0.0432 0.0268 136 1.611 0.2444
Results are averaged over the levels of: Task
P value adjustment: tukey method for comparing a family of 3 estimates
____________________________________________________________________________________________________
$emmeans
Task emmean SE df lower.CL upper.CL
Unpleasant 0.684 0.0120 136 0.660 0.707
Neutral 0.648 0.0121 136 0.624 0.672
Pleasant 0.710 0.0130 136 0.684 0.736
Results are averaged over the levels of: Group
Confidence level used: 0.95
$contrasts
contrast estimate SE df t.ratio p.value
Unpleasant - Neutral 0.0355 0.00979 136 3.623 0.0012
Unpleasant - Pleasant -0.0263 0.01016 136 -2.594 0.0282
Neutral - Pleasant -0.0618 0.01077 136 -5.740 <.0001
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 Parkinson Elder Young
Unpleasant 48 47 44
Neutral 48 47 44
Pleasant 48 47 44rmv_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 2, 136 0.05 30.06 *** .281 <.001
2 Task 1.99, 270.50 0.00 99.90 *** .079 <.001
3 Group:Task 3.98, 270.50 0.00 0.98 .002 .421
---
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
Parkinson 1.126 0.0194 136 1.088 1.165
Elder 0.996 0.0196 136 0.958 1.035
Young 0.911 0.0203 136 0.871 0.951
Results are averaged over the levels of: Task
Confidence level used: 0.95
$contrasts
contrast estimate SE df t.ratio p.value
Parkinson - Elder 0.1297 0.0276 136 4.701 <.0001
Parkinson - Young 0.2153 0.0281 136 7.675 <.0001
Elder - Young 0.0856 0.0282 136 3.037 0.0080
Results are averaged over the levels of: Task
P value adjustment: tukey method for comparing a family of 3 estimates
____________________________________________________________________________________________________
$emmeans
Task emmean SE df lower.CL upper.CL
Unpleasant 1.028 0.0130 136 1.002 1.054
Neutral 0.954 0.0110 136 0.932 0.976
Pleasant 1.052 0.0124 136 1.027 1.076
Results are averaged over the levels of: Group
Confidence level used: 0.95
$contrasts
contrast estimate SE df t.ratio p.value
Unpleasant - Neutral 0.0741 0.00747 136 9.919 <.0001
Unpleasant - Pleasant -0.0235 0.00706 136 -3.327 0.0032
Neutral - Pleasant -0.0976 0.00708 136 -13.779 <.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 Parkinson Elder Young
Unpleasant 48 47 44
Neutral 48 47 44
Pleasant 48 47 44rmsx_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 2, 136 0.10 19.63 *** .165 <.001
2 Task 1.92, 260.83 0.03 2.42 + .006 .093
3 Group:Task 3.84, 260.83 0.03 2.26 + .010 .066
---
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
Parkinson 0.502 0.0270 136 0.449 0.556
Elder 0.350 0.0273 136 0.296 0.404
Young 0.261 0.0282 136 0.206 0.317
Results are averaged over the levels of: Task
Confidence level used: 0.95
$contrasts
contrast estimate SE df t.ratio p.value
Parkinson - Elder 0.1523 0.0384 136 3.968 0.0003
Parkinson - Young 0.2407 0.0390 136 6.166 <.0001
Elder - Young 0.0884 0.0392 136 2.254 0.0660
Results are averaged over the levels of: Task
P value adjustment: tukey method for comparing a family of 3 estimates
____________________________________________________________________________________________________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 Parkinson Elder Young
Unpleasant 48 47 44
Neutral 48 47 44
Pleasant 48 47 44rmsy_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 2, 136 0.05 20.80 *** .187 <.001
2 Task 1.91, 259.73 0.01 27.69 *** .048 <.001
3 Group:Task 3.82, 259.73 0.01 1.13 .004 .342
---
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
Parkinson 0.785 0.0180 136 0.749 0.820
Elder 0.658 0.0181 136 0.622 0.694
Young 0.628 0.0188 136 0.591 0.666
Results are averaged over the levels of: Task
Confidence level used: 0.95
$contrasts
contrast estimate SE df t.ratio p.value
Parkinson - Elder 0.1265 0.0255 136 4.956 <.0001
Parkinson - Young 0.1562 0.0260 136 6.017 <.0001
Elder - Young 0.0297 0.0261 136 1.139 0.4920
Results are averaged over the levels of: Task
P value adjustment: tukey method for comparing a family of 3 estimates
____________________________________________________________________________________________________
$emmeans
Task emmean SE df lower.CL upper.CL
Unpleasant 0.695 0.0113 136 0.672 0.717
Neutral 0.649 0.0123 136 0.625 0.674
Pleasant 0.727 0.0129 136 0.702 0.753
Results are averaged over the levels of: Group
Confidence level used: 0.95
$contrasts
contrast estimate SE df t.ratio p.value
Unpleasant - Neutral 0.0452 0.00952 136 4.749 <.0001
Unpleasant - Pleasant -0.0325 0.01037 136 -3.134 0.0060
Neutral - Pleasant -0.0777 0.01148 136 -6.768 <.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 Parkinson Elder Young
Unpleasant 48 47 44
Neutral 48 47 44
Pleasant 48 47 44MPFx_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 2, 136 0.10 1.42 .013 .246
2 Task 1.98, 269.09 0.03 3.78 * .010 .025
3 Group:Task 3.96, 269.09 0.03 1.82 .010 .125
---
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 0.0187 136 -2.77 -2.70
Neutral -2.78 0.0192 136 -2.82 -2.74
Pleasant -2.73 0.0214 136 -2.77 -2.68
Results are averaged over the levels of: Group
Confidence level used: 0.95
$contrasts
contrast estimate SE df t.ratio p.value
Unpleasant - Neutral 0.0464 0.0200 136 2.318 0.0567
Unpleasant - Pleasant -0.0071 0.0216 136 -0.329 0.9422
Neutral - Pleasant -0.0535 0.0218 136 -2.456 0.0403
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 Parkinson Elder Young
Unpleasant 48 47 44
Neutral 48 47 44
Pleasant 48 47 44MPFy_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 2, 136 0.08 3.02 + .031 .052
2 Task 1.96, 266.06 0.02 19.95 *** .039 <.001
3 Group:Task 3.91, 266.06 0.02 1.09 .004 .362
---
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 -2.78 0.0155 136 -2.81 -2.75
Neutral -2.86 0.0172 136 -2.89 -2.82
Pleasant -2.77 0.0168 136 -2.80 -2.74
Results are averaged over the levels of: Group
Confidence level used: 0.95
$contrasts
contrast estimate SE df t.ratio p.value
Unpleasant - Neutral 0.07899 0.0151 136 5.224 <.0001
Unpleasant - Pleasant -0.00737 0.0142 136 -0.520 0.8616
Neutral - Pleasant -0.08636 0.0161 136 -5.355 <.0001
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 Parkinson Elder Young
Unpleasant 48 47 44
Neutral 48 47 44
Pleasant 48 47 44forward_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 2, 136 0.01 0.05 <.001 .949
2 Task 1.96, 266.96 0.01 2.22 .009 .111
3 Group:Task 3.93, 266.96 0.01 4.14 ** .034 .003
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘+’ 0.1 ‘ ’ 1
Sphericity correction method: GG $emmeans
Group Task emmean SE df lower.CL upper.CL
Parkinson Unpleasant 0.479 0.0129 136 0.453 0.504
Elder Unpleasant 0.507 0.0130 136 0.481 0.533
Young Unpleasant 0.528 0.0134 136 0.501 0.554
p_values Neutral 0.514 0.0117 136 0.491 0.537
Elder Neutral 0.500 0.0119 136 0.477 0.523
Young Neutral 0.492 0.0123 136 0.468 0.516
Parkinson Pleasant 0.505 0.0112 136 0.483 0.527
Elder Pleasant 0.488 0.0113 136 0.465 0.510
Young Pleasant 0.467 0.0117 136 0.444 0.490
Confidence level used: 0.95
$contrasts
contrast estimate SE df t.ratio p.value
Parkinson Unpleasant - Elder Unpleasant -0.02801 0.0183 136 -1.532 0.8383
Parkinson Unpleasant - Young Unpleasant -0.04894 0.0186 136 -2.633 0.1831
Parkinson Unpleasant - Parkinson Neutral -0.03503 0.0165 136 -2.119 0.4649
Parkinson Unpleasant - Elder Neutral -0.02118 0.0175 136 -1.211 0.9529
Parkinson Unpleasant - Young Neutral -0.01317 0.0178 136 -0.741 0.9981
Parkinson Unpleasant - Parkinson Pleasant -0.02593 0.0160 136 -1.621 0.7916
Parkinson Unpleasant - Elder Pleasant -0.00870 0.0171 136 -0.507 0.9999
Parkinson Unpleasant - Young Pleasant 0.01177 0.0174 136 0.676 0.9990
Elder Unpleasant - Young Unpleasant -0.02093 0.0187 136 -1.120 0.9702
Elder Unpleasant - Parkinson Neutral -0.00702 0.0175 136 -0.401 1.0000
Elder Unpleasant - Elder Neutral 0.00683 0.0167 136 0.409 1.0000
Elder Unpleasant - Young Neutral 0.01484 0.0179 136 0.831 0.9957
Elder Unpleasant - Parkinson Pleasant 0.00208 0.0172 136 0.121 1.0000
Elder Unpleasant - Elder Pleasant 0.01931 0.0162 136 1.195 0.9564
Elder Unpleasant - Young Pleasant 0.03978 0.0175 136 2.273 0.3659
Young Unpleasant - Parkinson Neutral 0.01391 0.0178 136 0.780 0.9972
Young Unpleasant - Elder Neutral 0.02776 0.0179 136 1.549 0.8298
Young Unpleasant - Young Neutral 0.03577 0.0173 136 2.072 0.4968
Young Unpleasant - Parkinson Pleasant 0.02302 0.0175 136 1.315 0.9253
Young Unpleasant - Elder Pleasant 0.04025 0.0176 136 2.290 0.3557
Young Unpleasant - Young Pleasant 0.06071 0.0167 136 3.634 0.0115
Parkinson Neutral - Elder Neutral 0.01385 0.0167 136 0.830 0.9958
Parkinson Neutral - Young Neutral 0.02186 0.0170 136 1.288 0.9334
Parkinson Neutral - Parkinson Pleasant 0.00910 0.0147 136 0.620 0.9995
Parkinson Neutral - Elder Pleasant 0.02633 0.0163 136 1.613 0.7960
Parkinson Neutral - Young Pleasant 0.04679 0.0166 136 2.821 0.1189
Elder Neutral - Young Neutral 0.00801 0.0171 136 0.469 0.9999
Elder Neutral - Parkinson Pleasant -0.00475 0.0163 136 -0.291 1.0000
Elder Neutral - Elder Pleasant 0.01248 0.0148 136 0.841 0.9953
Elder Neutral - Young Pleasant 0.03295 0.0167 136 1.976 0.5630
Young Neutral - Parkinson Pleasant -0.01276 0.0166 136 -0.767 0.9975
Young Neutral - Elder Pleasant 0.00448 0.0167 136 0.268 1.0000
Young Neutral - Young Pleasant 0.02494 0.0153 136 1.626 0.7890
Parkinson Pleasant - Elder Pleasant 0.01723 0.0160 136 1.080 0.9762
Parkinson Pleasant - Young Pleasant 0.03769 0.0162 136 2.323 0.3362
Elder Pleasant - Young Pleasant 0.02046 0.0163 136 1.255 0.9424
P value adjustment: tukey method for comparing a family of 9 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 Parkinson Elder Young
Unpleasant 48 47 44
Neutral 48 47 44
Pleasant 48 47 44log10_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 2, 136 0.35 12.39 *** .099 <.001
2 Task 1.94, 264.49 0.12 0.22 <.001 .794
3 Group:Task 3.89, 264.49 0.12 2.30 + .013 .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
Parkinson -0.829 0.0491 136 -0.926 -0.732
Elder -0.631 0.0497 136 -0.730 -0.533
Young -0.477 0.0513 136 -0.578 -0.375
Results are averaged over the levels of: Task
Confidence level used: 0.95
$contrasts
contrast estimate SE df t.ratio p.value
Parkinson - Elder -0.198 0.0699 136 -2.829 0.0148
Parkinson - Young -0.352 0.0711 136 -4.955 <.0001
Elder - Young -0.154 0.0714 136 -2.163 0.0814
Results are averaged over the levels of: Task
P value adjustment: tukey method for comparing a family of 3 estimates
____________________________________________________________________________________________________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 Parkinson Elder Young
Unpleasant 48 47 44
Neutral 48 47 44
Pleasant 48 47 44log10_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 2, 136 0.26 9.85 *** .092 <.001
2 Task 1.90, 257.89 0.06 3.46 * .008 .035
3 Group:Task 3.79, 257.89 0.06 1.04 .005 .383
---
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
Parkinson -1.177 0.0426 136 -1.26 -1.092
Elder -0.922 0.0431 136 -1.01 -0.837
Young -0.973 0.0445 136 -1.06 -0.885
Results are averaged over the levels of: Task
Confidence level used: 0.95
$contrasts
contrast estimate SE df t.ratio p.value
Parkinson - Elder -0.2543 0.0606 136 -4.198 0.0001
Parkinson - Young -0.2035 0.0616 136 -3.304 0.0035
Elder - Young 0.0508 0.0619 136 0.820 0.6913
Results are averaged over the levels of: Task
P value adjustment: tukey method for comparing a family of 3 estimates
____________________________________________________________________________________________________
$emmeans
Task emmean SE df lower.CL upper.CL
Unpleasant -1.014 0.0261 136 -1.07 -0.962
Neutral -0.993 0.0306 136 -1.05 -0.932
Pleasant -1.065 0.0326 136 -1.13 -1.000
Results are averaged over the levels of: Group
Confidence level used: 0.95
$contrasts
contrast estimate SE df t.ratio p.value
Unpleasant - Neutral -0.0215 0.0251 136 -0.855 0.6697
Unpleasant - Pleasant 0.0509 0.0285 136 1.786 0.1781
Neutral - Pleasant 0.0724 0.0309 136 2.342 0.0535
Results are averaged over the levels of: Group
P value adjustment: tukey method for comparing a family of 3 estimates
____________________________________________________________________________________________________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 Parkinson Elder Young
Unpleasant 48 47 44
Neutral 48 47 44
Pleasant 48 47 44sampen_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 2, 136 0.07 0.08 <.001 .927
2 Task 2.00, 271.74 0.01 7.44 *** .011 <.001
3 Group:Task 4.00, 271.74 0.01 0.85 .003 .498
---
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 0.0143 136 0.981 1.04
Neutral 1.05 0.0149 136 1.019 1.08
Pleasant 1.01 0.0144 136 0.982 1.04
Results are averaged over the levels of: Group
Confidence level used: 0.95
$contrasts
contrast estimate SE df t.ratio p.value
Unpleasant - Neutral -0.0391 0.0114 136 -3.415 0.0024
Unpleasant - Pleasant -0.0010 0.0117 136 -0.085 0.9960
Neutral - Pleasant 0.0381 0.0115 136 3.314 0.0034
Results are averaged over the levels of: Group
P value adjustment: tukey method for comparing a family of 3 estimates
____________________________________________________________________________________________________3.4 Resultant Vector Sample Entropy
options(width = 100)
sampen_resul_vect_rep_anova <- aov_ez("ID", "sampen_resul_vect", emo_data_clean, within = c("Task"), between = c("Group"))Warning: Missing values for 28 ID(s), which were removed before analysis:
EP_208, EP_229, EP_233, EP_234, FOJO_08, FOJO_09, FOJO_13, FOJO_16, FOJO_19, FOJO_20, ... [showing first 10 only]
Below the first few rows (in wide format) of the removed cases with missing data.
Contrasts set to contr.sum for the following variables: Grouprep_anova_data <- sampen_resul_vect_rep_anova$data$long
xtabs(~ Task + Group, data = rep_anova_data) Group
Task Parkinson Elder Young
Unpleasant 44 41 34
Neutral 44 41 34
Pleasant 44 41 34sampen_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 2, 116 0.55 7.00 ** .071 .001
2 Task 1.83, 212.39 0.17 13.70 *** .041 <.001
3 Group:Task 3.66, 212.39 0.17 1.44 .009 .224
---
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
Parkinson 1.83 0.0645 116 1.70 1.96
Elder 2.07 0.0668 116 1.94 2.21
Young 2.18 0.0734 116 2.03 2.32
Results are averaged over the levels of: Task
Confidence level used: 0.95
$contrasts
contrast estimate SE df t.ratio p.value
Parkinson - Elder -0.245 0.0929 116 -2.634 0.0258
Parkinson - Young -0.349 0.0977 116 -3.568 0.0015
Elder - Young -0.104 0.0992 116 -1.047 0.5487
Results are averaged over the levels of: Task
P value adjustment: tukey method for comparing a family of 3 estimates
____________________________________________________________________________________________________
$emmeans
Task emmean SE df lower.CL upper.CL
Unpleasant 1.95 0.0557 116 1.84 2.06
Neutral 2.18 0.0464 116 2.09 2.28
Pleasant 1.95 0.0459 116 1.86 2.04
Results are averaged over the levels of: Group
Confidence level used: 0.95
$contrasts
contrast estimate SE df t.ratio p.value
Unpleasant - Neutral -0.23822 0.0592 116 -4.021 0.0003
Unpleasant - Pleasant -0.00598 0.0485 116 -0.123 0.9916
Neutral - Pleasant 0.23224 0.0471 116 4.928 <.0001
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 Parkinson Elder Young
Unpleasant 48 47 44
Neutral 48 47 44
Pleasant 48 47 44sampen_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 2, 136 0.08 13.94 *** .121 <.001
2 Task 2.00, 271.74 0.02 1.32 .003 .269
3 Group:Task 4.00, 271.74 0.02 0.96 .005 .430
---
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
Parkinson 1.52 0.0237 136 1.47 1.57
Elder 1.57 0.0240 136 1.53 1.62
Young 1.70 0.0248 136 1.65 1.75
Results are averaged over the levels of: Task
Confidence level used: 0.95
$contrasts
contrast estimate SE df t.ratio p.value
Parkinson - Elder -0.0531 0.0337 136 -1.574 0.2605
Parkinson - Young -0.1773 0.0343 136 -5.168 <.0001
Elder - Young -0.1242 0.0345 136 -3.603 0.0013
Results are averaged over the levels of: Task
P value adjustment: tukey method for comparing a family of 3 estimates
____________________________________________________________________________________________________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 14 ID(s), which were removed before analysis:
EP_202, EP_204, EP_209, EP_222, EP_231, EP_235, EP_241, EP_245, FOJO_24, FOJO_37, ... [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: Group Group
Task Parkinson Elder Young
Unpleasant 40 44 49
Neutral 40 44 49
Pleasant 40 44 49heart_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 2, 130 390.93 4.07 * .057 .019
2 Task 1.93, 250.85 8.60 3.14 * <.001 .047
3 Group:Task 3.86, 250.85 8.60 1.30 <.001 .272
---
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
Parkinson 77.2 1.80 130 73.7 80.8
Elder 75.0 1.72 130 71.6 78.4
Young 81.6 1.63 130 78.4 84.9
Results are averaged over the levels of: Task
Confidence level used: 0.95
$contrasts
contrast estimate SE df t.ratio p.value
Parkinson - Elder 2.24 2.49 130 0.898 0.6423
Parkinson - Young -4.38 2.43 130 -1.801 0.1732
Elder - Young -6.62 2.37 130 -2.793 0.0165
Results are averaged over the levels of: Task
P value adjustment: tukey method for comparing a family of 3 estimates
____________________________________________________________________________________________________
$emmeans
Task emmean SE df lower.CL upper.CL
Unpleasant 77.6 1.010 130 75.6 79.6
Neutral 77.8 0.988 130 75.8 79.7
Pleasant 78.5 1.044 130 76.4 80.5
Results are averaged over the levels of: Group
Confidence level used: 0.95
$contrasts
contrast estimate SE df t.ratio p.value
Unpleasant - Neutral -0.116 0.336 130 -0.347 0.9358
Unpleasant - Pleasant -0.821 0.387 130 -2.123 0.0892
Neutral - Pleasant -0.704 0.339 130 -2.081 0.0978
Results are averaged over the levels of: Group
P value adjustment: tukey method for comparing a family of 3 estimates
____________________________________________________________________________________________________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 Parkinson Elder Young
Unpleasant 48 47 51
Neutral 48 47 51
Pleasant 48 47 51heart_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 2, 143 376.05 3.60 * .046 .030
2 Task 1.77, 253.38 10.31 1.49 <.001 .229
3 Group:Task 3.54, 253.38 10.31 1.26 <.001 .287
---
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
Parkinson 77.1 1.62 143 73.9 80.3
Elder 75.1 1.63 143 71.9 78.4
Young 81.1 1.57 143 78.0 84.2
Results are averaged over the levels of: Task
Confidence level used: 0.95
$contrasts
contrast estimate SE df t.ratio p.value
Parkinson - Elder 1.95 2.30 143 0.851 0.6720
Parkinson - Young -3.98 2.25 143 -1.768 0.1843
Elder - Young -5.93 2.26 143 -2.621 0.0261
Results are averaged over the levels of: Task
P value adjustment: tukey method for comparing a family of 3 estimates
____________________________________________________________________________________________________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 14 ID(s), which were removed before analysis:
EP_202, EP_204, EP_209, EP_222, EP_231, EP_235, EP_241, EP_245, FOJO_24, FOJO_37, ... [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: Group Group
Task Parkinson Elder Young
Unpleasant 40 44 49
Neutral 40 44 49
Pleasant 40 44 49log10_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 2, 130 0.31 8.08 *** .092 <.001
2 Task 1.97, 256.37 0.04 0.86 .001 .424
3 Group:Task 3.94, 256.37 0.04 1.26 .004 .287
---
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
Parkinson 1.16 0.0505 130 1.06 1.26
Elder 1.15 0.0482 130 1.06 1.25
Young 1.39 0.0456 130 1.30 1.48
Results are averaged over the levels of: Task
Confidence level used: 0.95
$contrasts
contrast estimate SE df t.ratio p.value
Parkinson - Elder 0.0128 0.0698 130 0.183 0.9817
Parkinson - Young -0.2240 0.0681 130 -3.290 0.0037
Elder - Young -0.2368 0.0664 130 -3.568 0.0015
Results are averaged over the levels of: Task
P value adjustment: tukey method for comparing a family of 3 estimates
____________________________________________________________________________________________________4.4 Root Mean Square of the Successive Differences (log10), NeuroKit2 method
options(width = 100)
log10_hrv_rmssd_nk2_rep_anova <- aov_ez("ID", "log10_hrv_rmssd_nk2", emo_data_clean, within = c("Task"), between = c("Group"))Warning: Missing values for 1 ID(s), which were removed before analysis:
FOSA_210
Below the first few rows (in wide format) of the removed cases with missing data.
Contrasts set to contr.sum for the following variables: Grouprep_anova_data <- log10_hrv_rmssd_nk2_rep_anova$data$long
xtabs(~ Task + Group, data = rep_anova_data) Group
Task Parkinson Elder Young
Unpleasant 48 47 51
Neutral 48 47 51
Pleasant 48 47 51log10_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 2, 143 0.31 7.23 ** .074 .001
2 Task 1.80, 257.77 0.05 1.58 .002 .211
3 Group:Task 3.61, 257.77 0.05 1.34 .004 .258
---
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
Parkinson 1.18 0.0466 143 1.09 1.28
Elder 1.19 0.0471 143 1.09 1.28
Young 1.40 0.0452 143 1.31 1.49
Results are averaged over the levels of: Task
Confidence level used: 0.95
$contrasts
contrast estimate SE df t.ratio p.value
Parkinson - Elder -0.00363 0.0662 143 -0.055 0.9983
Parkinson - Young -0.21474 0.0649 143 -3.310 0.0034
Elder - Young -0.21111 0.0652 143 -3.236 0.0043
Results are averaged over the levels of: Task
P value adjustment: tukey method for comparing a family of 3 estimates
____________________________________________________________________________________________________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 Parkinson Elder Young
Unpleasant 48 47 51
Neutral 48 47 51
Pleasant 48 47 51rsp_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 2, 143 21.46 9.08 *** .087 <.001
2 Task 1.99, 284.78 3.65 5.06 ** .009 .007
3 Group:Task 3.98, 284.78 3.65 2.97 * .010 .020
---
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
Parkinson 14.8 0.386 143 14.1 15.6
Elder 14.2 0.390 143 13.4 15.0
Young 12.6 0.375 143 11.9 13.4
Results are averaged over the levels of: Task
Confidence level used: 0.95
$contrasts
contrast estimate SE df t.ratio p.value
Parkinson - Elder 0.648 0.549 143 1.181 0.4662
Parkinson - Young 2.222 0.538 143 4.131 0.0002
Elder - Young 1.574 0.541 143 2.910 0.0116
Results are averaged over the levels of: Task
P value adjustment: tukey method for comparing a family of 3 estimates
____________________________________________________________________________________________________
$emmeans
Task emmean SE df lower.CL upper.CL
Unpleasant 13.6 0.257 143 13.1 14.1
Neutral 13.8 0.244 143 13.3 14.3
Pleasant 14.3 0.268 143 13.7 14.8
Results are averaged over the levels of: Group
Confidence level used: 0.95
$contrasts
contrast estimate SE df t.ratio p.value
Unpleasant - Neutral -0.228 0.226 143 -1.010 0.5720
Unpleasant - Pleasant -0.697 0.228 143 -3.058 0.0075
Neutral - Pleasant -0.469 0.216 143 -2.170 0.0799
Results are averaged over the levels of: Group
P value adjustment: tukey method for comparing a family of 3 estimates
____________________________________________________________________________________________________
$emmeans
Group Task emmean SE df lower.CL upper.CL
Parkinson Unpleasant 14.9 0.447 143 14.0 15.8
Elder Unpleasant 13.6 0.452 143 12.7 14.5
Young Unpleasant 12.3 0.434 143 11.4 13.2
Parkinson Neutral 15.0 0.426 143 14.1 15.8
Elder Neutral 14.3 0.430 143 13.5 15.2
Young Neutral 12.2 0.413 143 11.4 13.0
Parkinson Pleasant 14.7 0.466 143 13.8 15.6
Elder Pleasant 14.7 0.471 143 13.8 15.6
Young Pleasant 13.4 0.452 143 12.5 14.3
Confidence level used: 0.95
$contrasts
contrast estimate SE df t.ratio p.value
Parkinson Unpleasant - Elder Unpleasant 1.3083 0.636 143 2.058 0.5063
Parkinson Unpleasant - Young Unpleasant 2.5854 0.623 143 4.149 0.0018
Parkinson Unpleasant - Parkinson Neutral -0.0750 0.394 143 -0.191 1.0000
Parkinson Unpleasant - Elder Neutral 0.5738 0.621 143 0.925 0.9912
Parkinson Unpleasant - Young Neutral 2.7104 0.609 143 4.452 0.0006
Parkinson Unpleasant - Parkinson Pleasant 0.1731 0.397 143 0.436 1.0000
Parkinson Unpleasant - Elder Pleasant 0.1611 0.650 143 0.248 1.0000
Parkinson Unpleasant - Young Pleasant 1.4690 0.636 143 2.309 0.3436
Elder Unpleasant - Young Unpleasant 1.2771 0.627 143 2.038 0.5195
Elder Unpleasant - Parkinson Neutral -1.3834 0.621 143 -2.228 0.3936
Elder Unpleasant - Elder Neutral -0.7345 0.398 143 -1.845 0.6522
Elder Unpleasant - Young Neutral 1.4021 0.612 143 2.290 0.3552
Elder Unpleasant - Parkinson Pleasant -1.1353 0.649 143 -1.748 0.7156
Elder Unpleasant - Elder Pleasant -1.1472 0.401 143 -2.858 0.1081
Elder Unpleasant - Young Pleasant 0.1607 0.639 143 0.251 1.0000
Young Unpleasant - Parkinson Neutral -2.6604 0.608 143 -4.376 0.0008
Young Unpleasant - Elder Neutral -2.0116 0.611 143 -3.292 0.0332
Young Unpleasant - Young Neutral 0.1250 0.382 143 0.327 1.0000
Young Unpleasant - Parkinson Pleasant -2.4123 0.637 143 -3.788 0.0067
Young Unpleasant - Elder Pleasant -2.4243 0.641 143 -3.785 0.0068
Young Unpleasant - Young Pleasant -1.1164 0.385 143 -2.897 0.0981
Parkinson Neutral - Elder Neutral 0.6489 0.605 143 1.072 0.9773
Parkinson Neutral - Young Neutral 2.7855 0.593 143 4.695 0.0002
Parkinson Neutral - Parkinson Pleasant 0.2481 0.377 143 0.659 0.9992
Parkinson Neutral - Elder Pleasant 0.2362 0.635 143 0.372 1.0000
Parkinson Neutral - Young Pleasant 1.5441 0.621 143 2.486 0.2479
Elder Neutral - Young Neutral 2.1366 0.596 143 3.582 0.0134
Elder Neutral - Parkinson Pleasant -0.4008 0.634 143 -0.632 0.9994
Elder Neutral - Elder Pleasant -0.4127 0.380 143 -1.085 0.9756
Elder Neutral - Young Pleasant 0.8952 0.624 143 1.434 0.8828
Young Neutral - Parkinson Pleasant -2.5373 0.623 143 -4.073 0.0024
Young Neutral - Elder Pleasant -2.5493 0.627 143 -4.068 0.0025
Young Neutral - Young Pleasant -1.2414 0.365 143 -3.399 0.0240
Parkinson Pleasant - Elder Pleasant -0.0119 0.663 143 -0.018 1.0000
Parkinson Pleasant - Young Pleasant 1.2960 0.650 143 1.995 0.5495
Elder Pleasant - Young Pleasant 1.3079 0.653 143 2.002 0.5444
P value adjustment: tukey method for comparing a family of 9 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 11 ID(s), which were removed before analysis:
EP_202, EP_209, EP_224, EP_231, EP_240, EP_247, FOJO_41, FOSA_205, FOSA_210, FOSA_213, ... [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: Group Group
Task Parkinson Elder Young
Unpleasant 42 44 50
Neutral 42 44 50
Pleasant 42 44 50rrv_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 2, 133 1347056.26 1.35 .011 .262
2 Task 1.87, 249.02 576788.16 4.63 * .015 .012
3 Group:Task 3.74, 249.02 576788.16 2.64 * .017 .038
---
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 2031 80.0 133 1872 2189
Neutral 1888 83.4 133 1723 2053
Pleasant 1759 67.7 133 1625 1893
Results are averaged over the levels of: Group
Confidence level used: 0.95
$contrasts
contrast estimate SE df t.ratio p.value
Unpleasant - Neutral 142 100.3 133 1.419 0.3341
Unpleasant - Pleasant 272 81.9 133 3.316 0.0034
Neutral - Pleasant 129 84.8 133 1.527 0.2818
Results are averaged over the levels of: Group
P value adjustment: tukey method for comparing a family of 3 estimates
____________________________________________________________________________________________________
$emmeans
Group Task emmean SE df lower.CL upper.CL
Parkinson Unpleasant 1668 144 133 1384 1952
Elder Unpleasant 2175 140 133 1897 2452
Young Unpleasant 2249 132 133 1988 2509
Parkinson Neutral 1895 150 133 1599 2191
Elder Neutral 1810 146 133 1521 2099
Young Neutral 1960 137 133 1689 2232
Parkinson Pleasant 1732 122 133 1492 1973
Elder Pleasant 1774 119 133 1539 2009
Young Pleasant 1770 111 133 1550 1991
Confidence level used: 0.95
$contrasts
contrast estimate SE df t.ratio p.value
Parkinson Unpleasant - Elder Unpleasant -506.63 201 133 -2.524 0.2301
Parkinson Unpleasant - Young Unpleasant -580.29 195 133 -2.980 0.0800
Parkinson Unpleasant - Parkinson Neutral -226.56 180 133 -1.259 0.9411
Parkinson Unpleasant - Elder Neutral -141.53 205 133 -0.691 0.9988
Parkinson Unpleasant - Young Neutral -292.09 199 133 -1.471 0.8668
Parkinson Unpleasant - Parkinson Pleasant -63.87 147 133 -0.434 1.0000
Parkinson Unpleasant - Elder Pleasant -105.91 186 133 -0.568 0.9997
Parkinson Unpleasant - Young Pleasant -102.07 182 133 -0.562 0.9997
Elder Unpleasant - Young Unpleasant -73.66 192 133 -0.383 1.0000
Elder Unpleasant - Parkinson Neutral 280.06 205 133 1.365 0.9087
Elder Unpleasant - Elder Neutral 365.10 176 133 2.077 0.4933
Elder Unpleasant - Young Neutral 214.53 196 133 1.094 0.9743
Elder Unpleasant - Parkinson Pleasant 442.76 186 133 2.385 0.3007
Elder Unpleasant - Elder Pleasant 400.72 144 133 2.790 0.1283
Elder Unpleasant - Young Pleasant 404.56 179 133 2.258 0.3750
Young Unpleasant - Parkinson Neutral 353.73 199 133 1.775 0.6983
Young Unpleasant - Elder Neutral 438.77 197 133 2.231 0.3923
Young Unpleasant - Young Neutral 288.20 165 133 1.748 0.7158
Young Unpleasant - Parkinson Pleasant 516.42 179 133 2.883 0.1024
Young Unpleasant - Elder Pleasant 474.39 177 133 2.676 0.1666
Young Unpleasant - Young Pleasant 478.23 135 133 3.549 0.0152
Parkinson Neutral - Elder Neutral 85.04 209 133 0.406 1.0000
Parkinson Neutral - Young Neutral -65.53 203 133 -0.323 1.0000
Parkinson Neutral - Parkinson Pleasant 162.69 152 133 1.069 0.9776
Parkinson Neutral - Elder Pleasant 120.66 191 133 0.632 0.9994
Parkinson Neutral - Young Pleasant 124.50 187 133 0.667 0.9991
Elder Neutral - Young Neutral -150.57 200 133 -0.751 0.9979
Elder Neutral - Parkinson Pleasant 77.65 190 133 0.408 1.0000
Elder Neutral - Elder Pleasant 35.62 149 133 0.240 1.0000
Elder Neutral - Young Pleasant 39.46 184 133 0.215 1.0000
Young Neutral - Parkinson Pleasant 228.22 183 133 1.245 0.9447
Young Neutral - Elder Pleasant 186.19 181 133 1.026 0.9827
Young Neutral - Young Pleasant 190.03 139 133 1.363 0.9097
Parkinson Pleasant - Elder Pleasant -42.03 170 133 -0.247 1.0000
Parkinson Pleasant - Young Pleasant -38.19 165 133 -0.232 1.0000
Elder Pleasant - Young Pleasant 3.84 163 133 0.024 1.0000
P value adjustment: tukey method for comparing a family of 9 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 Parkinson Elder Young
Unpleasant 48 47 51
Neutral 48 47 51
Pleasant 48 47 51rsa_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 2, 143 5.27 32.36 *** .265 <.001
2 Task 1.97, 282.03 0.69 2.05 .003 .131
3 Group:Task 3.94, 282.03 0.69 3.47 ** .010 .009
---
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
Parkinson -7.76 0.191 143 -8.14 -7.38
Elder -7.57 0.193 143 -7.95 -7.19
Young -5.82 0.186 143 -6.19 -5.46
Results are averaged over the levels of: Task
Confidence level used: 0.95
$contrasts
contrast estimate SE df t.ratio p.value
Parkinson - Elder -0.194 0.272 143 -0.715 0.7553
Parkinson - Young -1.940 0.267 143 -7.278 <.0001
Elder - Young -1.745 0.268 143 -6.513 <.0001
Results are averaged over the levels of: Task
P value adjustment: tukey method for comparing a family of 3 estimates
____________________________________________________________________________________________________
$emmeans
Group Task emmean SE df lower.CL upper.CL
Parkinson Unpleasant -7.50 0.211 143 -7.92 -7.09
Elder Unpleasant -7.57 0.214 143 -7.99 -7.14
Young Unpleasant -5.75 0.205 143 -6.15 -5.34
Parkinson Neutral -8.12 0.202 143 -8.52 -7.72
Elder Neutral -7.48 0.204 143 -7.89 -7.08
Young Neutral -5.74 0.196 143 -6.13 -5.36
Parkinson Pleasant -7.67 0.229 143 -8.12 -7.21
Elder Pleasant -7.65 0.232 143 -8.11 -7.20
Young Pleasant -5.98 0.222 143 -6.42 -5.54
Confidence level used: 0.95
$contrasts
contrast estimate SE df t.ratio p.value
Parkinson Unpleasant - Elder Unpleasant 0.06270 0.300 143 0.209 1.0000
Parkinson Unpleasant - Young Unpleasant -1.75624 0.294 143 -5.965 <.0001
Parkinson Unpleasant - Parkinson Neutral 0.61352 0.159 143 3.865 0.0051
Parkinson Unpleasant - Elder Neutral -0.01895 0.294 143 -0.065 1.0000
Parkinson Unpleasant - Young Neutral -1.75833 0.288 143 -6.103 <.0001
Parkinson Unpleasant - Parkinson Pleasant 0.16382 0.176 143 0.931 0.9908
Parkinson Unpleasant - Elder Pleasant 0.15052 0.314 143 0.480 0.9999
Parkinson Unpleasant - Young Pleasant -1.52737 0.307 143 -4.978 0.0001
Elder Unpleasant - Young Unpleasant -1.81894 0.296 143 -6.145 <.0001
Elder Unpleasant - Parkinson Neutral 0.55081 0.294 143 1.874 0.6324
Elder Unpleasant - Elder Neutral -0.08166 0.160 143 -0.509 0.9999
Elder Unpleasant - Young Neutral -1.82104 0.290 143 -6.284 <.0001
Elder Unpleasant - Parkinson Pleasant 0.10111 0.313 143 0.323 1.0000
Elder Unpleasant - Elder Pleasant 0.08782 0.178 143 0.494 0.9999
Elder Unpleasant - Young Pleasant -1.59007 0.308 143 -5.156 <.0001
Young Unpleasant - Parkinson Neutral 2.36976 0.288 143 8.236 <.0001
Young Unpleasant - Elder Neutral 1.73729 0.289 143 6.007 <.0001
Young Unpleasant - Young Neutral -0.00209 0.154 143 -0.014 1.0000
Young Unpleasant - Parkinson Pleasant 1.92005 0.308 143 6.242 <.0001
Young Unpleasant - Elder Pleasant 1.90676 0.309 143 6.163 <.0001
Young Unpleasant - Young Pleasant 0.22887 0.171 143 1.340 0.9173
Parkinson Neutral - Elder Neutral -0.63247 0.287 143 -2.204 0.4091
Parkinson Neutral - Young Neutral -2.37185 0.281 143 -8.432 <.0001
Parkinson Neutral - Parkinson Pleasant -0.44970 0.169 143 -2.668 0.1691
Parkinson Neutral - Elder Pleasant -0.46299 0.307 143 -1.506 0.8509
Parkinson Neutral - Young Pleasant -2.14089 0.300 143 -7.127 <.0001
Elder Neutral - Young Neutral -1.73938 0.283 143 -6.150 <.0001
Elder Neutral - Parkinson Pleasant 0.18277 0.307 143 0.595 0.9996
Elder Neutral - Elder Pleasant 0.16948 0.170 143 0.995 0.9858
Elder Neutral - Young Pleasant -1.50842 0.302 143 -4.997 0.0001
Young Neutral - Parkinson Pleasant 1.92215 0.302 143 6.374 <.0001
Young Neutral - Elder Pleasant 1.90886 0.303 143 6.291 <.0001
Young Neutral - Young Pleasant 0.23096 0.164 143 1.412 0.8915
Parkinson Pleasant - Elder Pleasant -0.01329 0.326 143 -0.041 1.0000
Parkinson Pleasant - Young Pleasant -1.69118 0.319 143 -5.293 <.0001
Elder Pleasant - Young Pleasant -1.67789 0.321 143 -5.223 <.0001
P value adjustment: tukey method for comparing a family of 9 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 27 ID(s), which were removed before analysis:
EP_202, EP_204, EP_205, EP_207, EP_208, EP_209, EP_217, EP_219, EP_221, EP_223, ... [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 Parkinson Elder Young
Unpleasant 32 47 41
Neutral 32 47 41
Pleasant 32 47 41log10_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 2, 117 1.30 3.47 * .047 .034
2 Task 1.96, 229.77 0.13 74.83 *** .096 <.001
3 Group:Task 3.93, 229.77 0.13 1.21 .003 .306
---
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
Parkinson -0.931 0.1165 117 -1.161 -0.700
Elder -0.923 0.0961 117 -1.113 -0.733
Young -0.592 0.1029 117 -0.796 -0.388
Results are averaged over the levels of: Task
Confidence level used: 0.95
$contrasts
contrast estimate SE df t.ratio p.value
Parkinson - Elder -0.00758 0.151 117 -0.050 0.9986
Parkinson - Young -0.33867 0.155 117 -2.179 0.0790
Elder - Young -0.33109 0.141 117 -2.351 0.0528
Results are averaged over the levels of: Task
P value adjustment: tukey method for comparing a family of 3 estimates
____________________________________________________________________________________________________
$emmeans
Task emmean SE df lower.CL upper.CL
Unpleasant -0.532 0.0569 117 -0.645 -0.419
Neutral -1.109 0.0722 117 -1.252 -0.966
Pleasant -0.804 0.0700 117 -0.943 -0.666
Results are averaged over the levels of: Group
Confidence level used: 0.95
$contrasts
contrast estimate SE df t.ratio p.value
Unpleasant - Neutral 0.577 0.0478 117 12.056 <.0001
Unpleasant - Pleasant 0.273 0.0440 117 6.190 <.0001
Neutral - Pleasant -0.304 0.0495 117 -6.150 <.0001
Results are averaged over the levels of: Group
P value adjustment: tukey method for comparing a family of 3 estimates
____________________________________________________________________________________________________5.2 Number of SCR Peaks (log10)
options(width = 100)
log10_scr_peaks_number_rep_anova <- aov_ez("ID", "log10_scr_peaks_number", emo_data_clean, within = c("Task"), between = c("Group"))Warning: Missing values for 27 ID(s), which were removed before analysis:
EP_202, EP_204, EP_205, EP_207, EP_208, EP_209, EP_217, EP_219, EP_221, EP_223, ... [showing first 10 only]
Below the first few rows (in wide format) of the removed cases with missing data.
Contrasts set to contr.sum for the following variables: Grouprep_anova_data <- log10_scr_peaks_number_rep_anova$data$long
xtabs(~ Task + Group, data = rep_anova_data) Group
Task Parkinson Elder Young
Unpleasant 32 47 41
Neutral 32 47 41
Pleasant 32 47 41log10_scr_peaks_number_rain <- ggplot(rep_anova_data, aes(y = Task, x = log10_scr_peaks_number, color = Group, fill = Group)) +
stat_halfeye(
trim = FALSE,
.width = 0,
justification = -.15,
alpha = .4,
point_colour = NA) +
geom_point(size = 2, alpha = .4, position = position_jitter(width = 0, height = .05))
suppressWarnings(print(log10_scr_peaks_number_rain))
log10_scr_peaks_number_afex_plot <-
afex_plot(
log10_scr_peaks_number_rep_anova,
x = "Task",
trace = "Group",
error = "between",
error_arg = list(width = .15),
dodge = -.5,
mapping = c("color"),
point_arg = list(size = 4)
)Warning: Panel(s) show a mixed within-between-design.
Error bars do not allow comparisons across all means.
Suppress error bars with: error = "none"
Anova Table (Type 3 tests)
Response: log10_scr_peaks_number
Effect df MSE F ges p.value
1 Group 2, 117 0.07 0.47 .004 .626
2 Task 1.77, 207.47 0.05 1.07 .005 .338
3 Group:Task 3.55, 207.47 0.05 1.19 .011 .316
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘+’ 0.1 ‘ ’ 1
Sphericity correction method: GG 5.3 Mean SCR Amplitude (log10)
options(width = 100)
log10_scr_mean_amplitude_rep_anova <- aov_ez("ID", "log10_scr_mean_amplitude", emo_data_clean, within = c("Task"), between = c("Group"))Warning: Missing values for 28 ID(s), which were removed before analysis:
EP_202, EP_204, EP_205, EP_207, EP_208, EP_209, EP_217, EP_219, EP_221, EP_223, ... [showing first 10 only]
Below the first few rows (in wide format) of the removed cases with missing data.
Contrasts set to contr.sum for the following variables: Grouprep_anova_data <- log10_scr_mean_amplitude_rep_anova$data$long
xtabs(~ Task + Group, data = rep_anova_data) Group
Task Parkinson Elder Young
Unpleasant 32 46 41
Neutral 32 46 41
Pleasant 32 46 41log10_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 2, 116 1.60 1.72 .024 .183
2 Task 1.86, 215.25 0.18 43.70 *** .062 <.001
3 Group:Task 3.71, 215.25 0.18 1.68 .005 .161
---
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 -4.22 0.0701 116 -4.36 -4.08
Neutral -4.73 0.0779 116 -4.88 -4.57
Pleasant -4.45 0.0756 116 -4.60 -4.30
Results are averaged over the levels of: Group
Confidence level used: 0.95
$contrasts
contrast estimate SE df t.ratio p.value
Unpleasant - Neutral 0.506 0.0591 116 8.550 <.0001
Unpleasant - Pleasant 0.223 0.0463 116 4.823 <.0001
Neutral - Pleasant -0.283 0.0564 116 -5.011 <.0001
Results are averaged over the levels of: Group
P value adjustment: tukey method for comparing a family of 3 estimates
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