Code
cat('\014') # clean terminalCode
rm(list = ls()) # clean workspace
library(tidyverse)
library(afex)
library(emmeans)
library(GGally)
library(EnvStats)
library(easystats)
library(RLRsim)Common Humanity & Mindfulness 2023
Physiological signals anlysis, Mixed Model
Code
theme_set(
theme_minimal()
)
a_posteriori <- function(lmer_object, sig_level = .05) {
suppressWarnings(rr <- r2(lmer_object))
print(rr)
if (!is.na(rr[[1]])) {
cat(rep('_', 60), '\n', sep = '')
suppressWarnings(print(icc(lmer_object)))
cat(rep('_', 60), '\n', sep = '')
frmla <- lmer_object@call$formula
frmla1 <- update.formula(frmla, . ~ sex + age + (1 | duo))
frmla0 <- update.formula(frmla, . ~ sex + age)
datas <- lmer_object@call$data
human1 <- lmer(eval(frmla1), subset(eval(datas), group == 'humanity'))
human0 <- lm (eval(frmla0), subset(eval(datas), group == 'humanity'))
try({
suppressMessages(human <- exactLRT(human1, human0))
names(human$statistic) <- 'humanity LRT'
print(human)
}, silent = TRUE)
cat(rep('_', 60), '\n', sep = '')
mindf1 <- lmer(eval(frmla1), subset(eval(datas), group == 'mindfulness'))
mindf0 <- lm (eval(frmla0), subset(eval(datas), group == 'mindfulness'))
try({
suppressMessages(mindf <- exactLRT(mindf1, mindf0))
names(mindf$statistic) <- 'mindfulness LRT'
print(mindf)
}, silent = TRUE)
}
factors <- list('group', 'sex', NA, c('group' ,'sex'))
p_values <- anova(lmer_object)$`Pr(>F)`
if (p_values[3] <= sig_level) {
cat(rep('_', 60), '\n', sep = '')
print(paste('Slope for age =', lmer_object@beta[4]))
}
for (i in c(1:2, 4)) {
if (p_values[i] <= sig_level) {
cat(rep('_', 60), '\n', sep = '')
print(emmeans(lmer_object, factors[[i]], contr = 'pairwise'))
}
}
}Code
a_df_age <- read_csv('../../analysis_apt/data/df_age_2023_data_clean.csv', col_types = cols())
a_df_hrv <- read_csv('../data/hrv_hrf_hra_rsa_rrv_neurokit2.csv', col_types = cols()) |>
rename(id = sbj) |>
rename(group = grp) |>
left_join(y = a_df_age[c('id', 'age')], by = c('id')) |>
mutate(heart_rate = 60000 / HRV_MeanNN) |>
mutate(rsp_rate = 60000 / RRV_MeanBB) |>
mutate(HRV_lnLF_lnHF = log(HRV_LFn) - log(HRV_HFn)) |>
mutate(hr_to_hrv = heart_rate / HRV_LFn) |>
mutate(sex = if_else(sex == 'f', 'female', 'male')) |>
mutate(log10_HRV_RMSSD = log10(HRV_RMSSD)) |>
mutate(log10_HRF_PAS = log10(HRF_PAS)) |>
mutate(log10_RRV_RMSSD = log10(RRV_RMSSD)) |>
mutate_if(is.character, as.factor)
a_df_pow_peaks <- read_csv('../data/power_peaks.csv', col_types = cols()) |>
separate_wider_delim(
subj,
delim = '_',
names = c('duo', 'id')
) |>
filter(peak_size == 1) |>
left_join(y = a_df_hrv[c('id', 'sex', 'group', 'age')], by = c('id')) |>
mutate_if(is.character, as.factor)
a_df_aperiodic <- read_csv('../data/aperiodic_params.csv', col_types = cols()) |>
separate_wider_delim(
subj,
delim = '_',
names = c('duo', 'id')
) |>
mutate(log_knee = log(knee)) |>
left_join(y = a_df_hrv[c('id', 'sex', 'group', 'age')], by = c('id')) |>
mutate_if(is.character, as.factor)
a_df_ccoh <- read_csv('../data/ccoh_ave.csv', col_types = cols()) |>
rename(id = sbj) |>
rename(group = grp) |>
mutate(sex = if_else(sex == 'f', 'female', 'male')) |>
left_join(y = a_df_hrv[c('id', 'age')], by = c('id')) |>
mutate_if(is.character, as.factor)
a_df_rsa_angular <- read_csv('../data/rsa_angular_diff_rhrv.csv', col_types = cols()) |>
rename(id = sbj) |>
rename(group = grp) |>
mutate(sex = if_else(sex == 'f', 'female', 'male')) |>
mutate(log_wallraff_chi = log(wallraff_chi)) |>
mutate(log_f_aov = log(f_aov)) |>
mutate(log_watson_f = log(watson_f)) |>
left_join(y = a_df_hrv[c('id', 'age')], by = c('id')) |>
mutate_if(is.character, as.factor)
a_df_rsa_cyclic <- read_csv('../data/rsa_cycles_params.csv', col_types = cols()) |>
rename(id = sbj) |>
rename(group = grp) |>
mutate(sex = if_else(sex == 'f', 'female', 'male')) |>
left_join(y = a_df_hrv[c('id', 'age')], by = c('id')) |>
mutate_if(is.character, as.factor)
a_df_clumpiness <- read_csv('../data/hp_clumpiness.csv', col_types = cols()) |>
rename(id = sbj) |>
rename(group = grp) |>
mutate(sex = if_else(sex == 'f', 'female', 'male')) |>
left_join(y = a_df_hrv[c('id', 'age')], by = c('id')) |>
mutate_if(is.character, as.factor)1 Summary
Code
addmargins(table(a_df_hrv$group, a_df_hrv$sex))
female male Sum
humanity 16 16 32
mindfulness 16 16 32
Sum 32 32 64Code
summary(a_df_hrv) duo id sex group HRV_MeanNN
d01 : 2 s01 : 1 female:32 humanity :32 Min. : 601.0
d02 : 2 s02 : 1 male :32 mindfulness:32 1st Qu.: 752.0
d03 : 2 s03 : 1 Median : 834.9
d04 : 2 s04 : 1 Mean : 834.6
d05 : 2 s05 : 1 3rd Qu.: 910.9
d06 : 2 s06 : 1 Max. :1146.2
(Other):52 (Other):58
HRV_SDNN HRV_SDANN1 HRV_SDNNI1 HRV_RMSSD
Min. : 22.12 Min. :13.88 Min. : 17.11 Min. : 7.888
1st Qu.: 54.46 1st Qu.:23.50 1st Qu.: 49.85 1st Qu.: 29.549
Median : 75.73 Median :29.57 Median : 65.68 Median : 46.516
Mean : 81.90 Mean :34.63 Mean : 72.24 Mean : 51.833
3rd Qu.:108.64 3rd Qu.:43.21 3rd Qu.: 96.78 3rd Qu.: 66.324
Max. :159.05 Max. :81.25 Max. :155.40 Max. :176.077
HRV_SDSD HRV_CVNN HRV_CVSD HRV_MedianNN
Min. : 7.89 Min. :0.03416 Min. :0.01218 Min. : 599.0
1st Qu.: 29.56 1st Qu.:0.07439 1st Qu.:0.03945 1st Qu.: 737.2
Median : 46.53 Median :0.09030 Median :0.05588 Median : 826.5
Mean : 51.85 Mean :0.09655 Mean :0.06031 Mean : 835.3
3rd Qu.: 66.35 3rd Qu.:0.12291 3rd Qu.:0.07282 3rd Qu.: 911.5
Max. :176.14 Max. :0.18570 Max. :0.19013 Max. :1157.0
HRV_MadNN HRV_MCVNN HRV_IQRNN HRV_SDRMSSD
Min. : 22.24 Min. :0.03443 Min. : 30.00 Min. :0.7613
1st Qu.: 55.97 1st Qu.:0.07029 1st Qu.: 75.75 1st Qu.:1.4405
Median : 77.84 Median :0.08966 Median :105.50 Median :1.7400
Mean : 83.20 Mean :0.09824 Mean :115.27 Mean :1.7655
3rd Qu.:105.64 3rd Qu.:0.12389 3rd Qu.:144.94 3rd Qu.:2.0225
Max. :189.77 Max. :0.22379 Max. :265.00 Max. :2.9463
HRV_Prc20NN HRV_Prc80NN HRV_pNN50 HRV_pNN20
Min. : 575.0 Min. : 624.0 Min. : 0.00 Min. : 2.219
1st Qu.: 695.0 1st Qu.: 812.4 1st Qu.: 8.05 1st Qu.:46.858
Median : 751.0 Median : 905.0 Median :25.20 Median :60.567
Mean : 763.3 Mean : 905.7 Mean :23.99 Mean :55.342
3rd Qu.: 809.6 3rd Qu.:1008.8 3rd Qu.:33.58 3rd Qu.:68.542
Max. :1064.2 Max. :1242.0 Max. :61.55 Max. :84.320
HRV_MinNN HRV_MaxNN HRV_HTI HRV_TINN
Min. :392.0 Min. : 716.0 Min. : 6.619 Min. : 0.0
1st Qu.:540.2 1st Qu.: 964.8 1st Qu.:15.616 1st Qu.:209.0
Median :590.0 Median :1059.0 Median :20.675 Median :332.0
Mean :592.1 Mean :1083.0 Mean :21.067 Mean :303.3
3rd Qu.:624.2 3rd Qu.:1224.8 3rd Qu.:27.192 3rd Qu.:416.0
Max. :830.0 Max. :1427.0 Max. :39.000 Max. :632.8
HRV_VLF HRV_LF HRV_HF HRV_VHF
Min. :0.0006637 Min. :0.001261 Min. :0.0003042 Min. :1.095e-05
1st Qu.:0.0028007 1st Qu.:0.004354 1st Qu.:0.0016075 1st Qu.:4.098e-05
Median :0.0046367 Median :0.007936 Median :0.0026162 Median :1.008e-04
Mean :0.0059340 Mean :0.011116 Mean :0.0052010 Mean :3.053e-04
3rd Qu.:0.0087290 3rd Qu.:0.016586 3rd Qu.:0.0062517 3rd Qu.:1.743e-04
Max. :0.0205487 Max. :0.035360 Max. :0.0309629 Max. :9.837e-03
HRV_TP HRV_LFHF HRV_LFn HRV_HFn
Min. :0.002965 Min. : 0.5302 Min. :0.2272 Min. :0.03533
1st Qu.:0.009898 1st Qu.: 1.2408 1st Qu.:0.3430 1st Qu.:0.09818
Median :0.019280 Median : 2.4597 Median :0.4237 Median :0.18924
Mean :0.022556 Mean : 3.9986 Mean :0.4789 Mean :0.21302
3rd Qu.:0.030619 3rd Qu.: 4.7435 3rd Qu.:0.5838 3rd Qu.:0.29783
Max. :0.077930 Max. :16.5303 Max. :0.8859 Max. :0.52422
HRV_LnHF HRV_SD1 HRV_SD2 HRV_SD1SD2
Min. :-8.098 Min. : 5.579 Min. : 30.79 Min. :0.1722
1st Qu.:-6.434 1st Qu.: 20.901 1st Qu.: 74.69 1st Qu.:0.2554
Median :-5.946 Median : 32.905 Median :102.84 Median :0.3000
Mean :-5.799 Mean : 36.665 Mean :109.33 Mean :0.3210
3rd Qu.:-5.076 3rd Qu.: 46.919 3rd Qu.:142.58 3rd Qu.:0.3702
Max. :-3.475 Max. :124.553 Max. :214.24 Max. :0.8723
HRV_S HRV_CSI HRV_CVI HRV_CSI_Modified
Min. : 539.7 Min. :1.146 Min. :3.439 Min. : 543.2
1st Qu.: 5323.9 1st Qu.:2.702 1st Qu.:4.432 1st Qu.: 988.2
Median :11186.3 Median :3.333 Median :4.756 Median :1299.6
Mean :14851.7 Mean :3.373 Mean :4.700 Mean :1401.5
3rd Qu.:21386.1 3rd Qu.:3.915 3rd Qu.:5.037 3rd Qu.:1709.0
Max. :55872.0 Max. :5.807 Max. :5.454 Max. :2696.8
HRF_PIP HRF_IALS HRF_PSS HRF_PAS
Min. :0.2031 Min. :0.1972 Min. :0.1760 Min. :0.002227
1st Qu.:0.3122 1st Qu.:0.3035 1st Qu.:0.3756 1st Qu.:0.014716
Median :0.3838 Median :0.3711 Median :0.5420 Median :0.032264
Mean :0.3764 Mean :0.3678 Mean :0.5138 Mean :0.036832
3rd Qu.:0.4445 3rd Qu.:0.4324 3rd Qu.:0.6482 3rd Qu.:0.051006
Max. :0.5517 Max. :0.5472 Max. :0.8576 Max. :0.156550
HRA_GI HRA_SI HRA_AI HRA_PI
Min. :49.41 Min. :49.10 Min. :49.63 Min. :43.16
1st Qu.:49.88 1st Qu.:49.85 1st Qu.:49.91 1st Qu.:48.61
Median :49.96 Median :49.91 Median :50.02 Median :51.83
Mean :49.97 Mean :49.91 Mean :50.02 Mean :52.50
3rd Qu.:50.05 3rd Qu.:50.00 3rd Qu.:50.12 3rd Qu.:56.21
Max. :50.41 Max. :50.35 Max. :50.61 Max. :66.69
HRA_C1d HRA_C1a HRA_SD1d HRA_SD1a
Min. :0.4149 Min. :0.2371 Min. : 4.055 Min. : 3.833
1st Qu.:0.4986 1st Qu.:0.3818 1st Qu.:15.513 1st Qu.:14.168
Median :0.5683 Median :0.4317 Median :23.371 Median :22.701
Mean :0.5660 Mean :0.4340 Mean :28.058 Mean :23.411
3rd Qu.:0.6182 3rd Qu.:0.5014 3rd Qu.:36.411 3rd Qu.:29.076
Max. :0.7629 Max. :0.5851 Max. :95.213 Max. :80.299
HRA_C2d HRA_C2a HRA_SD2d HRA_SD2a
Min. :0.2817 Min. :0.4544 Min. : 21.81 Min. : 21.73
1st Qu.:0.4151 1st Qu.:0.5011 1st Qu.: 51.06 1st Qu.: 55.32
Median :0.4498 Median :0.5502 Median : 71.15 Median : 75.39
Mean :0.4516 Mean :0.5484 Mean : 72.13 Mean : 81.90
3rd Qu.:0.4989 3rd Qu.:0.5849 3rd Qu.: 90.31 3rd Qu.:108.65
Max. :0.5456 Max. :0.7183 Max. :120.87 Max. :181.57
HRA_Cd HRA_Ca HRA_SDNNd HRA_SDNNa
Min. :0.3259 Min. :0.4602 Min. :15.69 Min. : 15.60
1st Qu.:0.4398 1st Qu.:0.5027 1st Qu.:38.32 1st Qu.: 40.01
Median :0.4609 Median :0.5391 Median :53.52 Median : 55.25
Mean :0.4641 Mean :0.5359 Mean :55.10 Mean : 60.45
3rd Qu.:0.4973 3rd Qu.:0.5602 3rd Qu.:70.25 3rd Qu.: 81.10
Max. :0.5398 Max. :0.6741 Max. :96.04 Max. :130.51
HRV_DFA_alpha1 HRV_MFDFA_alpha1_Width HRV_MFDFA_alpha1_Peak
Min. :0.8514 Min. :0.7717 Min. :1.095
1st Qu.:1.1631 1st Qu.:1.8686 1st Qu.:1.366
Median :1.3425 Median :2.2010 Median :1.567
Mean :1.3289 Mean :2.1872 Mean :1.595
3rd Qu.:1.5199 3rd Qu.:2.5620 3rd Qu.:1.839
Max. :1.7277 Max. :3.5068 Max. :2.372
HRV_MFDFA_alpha1_Mean HRV_MFDFA_alpha1_Max HRV_MFDFA_alpha1_Delta
Min. :1.371 Min. :-3.5953 Min. :-4.5656
1st Qu.:1.921 1st Qu.:-2.1805 1st Qu.:-2.6191
Median :2.092 Median :-1.6207 Median :-1.6314
Mean :2.116 Mean :-1.5024 Mean :-1.7203
3rd Qu.:2.321 3rd Qu.:-0.7451 3rd Qu.:-0.9644
Max. :2.795 Max. : 0.7712 Max. : 1.6485
HRV_MFDFA_alpha1_Asymmetry HRV_MFDFA_alpha1_Fluctuation
Min. :-0.91798 Min. :0.0002369
1st Qu.:-0.33684 1st Qu.:0.0012840
Median :-0.27146 Median :0.0024144
Mean :-0.27202 Mean :0.0032722
3rd Qu.:-0.15625 3rd Qu.:0.0042287
Max. :-0.01569 Max. :0.0181720
HRV_MFDFA_alpha1_Increment HRV_DFA_alpha2 HRV_MFDFA_alpha2_Width
Min. :0.04413 Min. :0.3819 Min. :0.06623
1st Qu.:0.21901 1st Qu.:0.7095 1st Qu.:0.18168
Median :0.34114 Median :0.7887 Median :0.29153
Mean :0.38364 Mean :0.7856 Mean :0.32283
3rd Qu.:0.47796 3rd Qu.:0.8814 3rd Qu.:0.46350
Max. :1.09029 Max. :1.1064 Max. :0.77810
HRV_MFDFA_alpha2_Peak HRV_MFDFA_alpha2_Mean HRV_MFDFA_alpha2_Max
Min. :0.4675 Min. :0.4603 Min. :-0.2978
1st Qu.:0.7356 1st Qu.:0.7775 1st Qu.: 0.3844
Median :0.8361 Median :0.8446 Median : 0.6177
Mean :0.8224 Mean :0.8392 Mean : 0.6493
3rd Qu.:0.9130 3rd Qu.:0.9294 3rd Qu.: 0.9703
Max. :1.2191 Max. :1.1321 Max. : 1.5636
HRV_MFDFA_alpha2_Delta HRV_MFDFA_alpha2_Asymmetry HRV_MFDFA_alpha2_Fluctuation
Min. :-1.06183 Min. :-1.0000 Min. :2.526e-06
1st Qu.:-0.47716 1st Qu.:-0.7395 1st Qu.:1.073e-05
Median :-0.16280 Median :-0.4684 Median :3.640e-05
Mean :-0.17611 Mean :-0.4864 Mean :5.990e-05
3rd Qu.: 0.06308 3rd Qu.:-0.1799 3rd Qu.:7.050e-05
Max. : 1.11061 Max. : 0.0000 Max. :3.030e-04
HRV_MFDFA_alpha2_Increment HRV_ApEn HRV_SampEn HRV_ShanEn
Min. :0.0004625 Min. :0.8034 Min. :0.6221 Min. :6.414
1st Qu.:0.0028003 1st Qu.:1.0624 1st Qu.:0.9967 1st Qu.:7.656
Median :0.0065677 Median :1.2707 Median :1.2245 Median :8.039
Mean :0.0084463 Mean :1.2496 Mean :1.2213 Mean :7.981
3rd Qu.:0.0119822 3rd Qu.:1.3999 3rd Qu.:1.4094 3rd Qu.:8.398
Max. :0.0312495 Max. :1.5786 Max. :1.6783 Max. :8.773
HRV_FuzzyEn HRV_MSEn HRV_CMSEn HRV_RCMSEn
Min. :0.6638 Min. :0.5883 Min. :1.343 Min. :1.838
1st Qu.:0.8023 1st Qu.:1.1264 1st Qu.:1.381 1st Qu.:2.044
Median :0.9330 Median :1.2900 Median :1.403 Median :2.097
Mean :0.9419 Mean :1.2467 Mean :1.400 Mean :2.096
3rd Qu.:1.0801 3rd Qu.:1.4056 3rd Qu.:1.420 3rd Qu.:2.173
Max. :1.3261 Max. :1.6275 Max. :1.453 Max. :2.294
HRV_CD HRV_HFD HRV_KFD HRV_LZC
Min. :1.220 Min. :1.355 Min. :2.367 Min. :0.4583
1st Qu.:1.549 1st Qu.:1.593 1st Qu.:2.963 1st Qu.:0.6001
Median :1.655 Median :1.691 Median :3.292 Median :0.6643
Mean :1.616 Mean :1.692 Mean :3.338 Mean :0.6734
3rd Qu.:1.699 3rd Qu.:1.779 3rd Qu.:3.577 3rd Qu.:0.7357
Max. :1.799 Max. :1.923 Max. :5.023 Max. :0.9584
RSA_P2T_Mean RSA_P2T_Mean_log RSA_P2T_SD RSA_P2T_NoRSA
Min. : 18.38 Min. :2.911 Min. : 16.78 Min. : 0.00
1st Qu.: 73.74 1st Qu.:4.299 1st Qu.: 47.09 1st Qu.: 0.00
Median :113.55 Median :4.732 Median : 69.43 Median : 1.00
Mean :120.71 Mean :4.616 Mean : 76.52 Mean : 2.25
3rd Qu.:162.91 3rd Qu.:5.093 3rd Qu.:108.27 3rd Qu.: 3.00
Max. :353.91 Max. :5.869 Max. :173.61 Max. :17.00
RSA_PorgesBohrer RSA_Gates_Mean RSA_Gates_Mean_log RSA_Gates_SD
Min. :-7.287 Min. :7.291 Min. :1.987 Min. :0.07904
1st Qu.:-5.658 1st Qu.:7.891 1st Qu.:2.066 1st Qu.:0.15099
Median :-4.861 Median :8.156 Median :2.099 Median :0.19904
Mean :-5.109 Mean :8.153 Mean :2.097 Mean :0.20784
3rd Qu.:-4.432 3rd Qu.:8.446 3rd Qu.:2.134 3rd Qu.:0.26238
Max. :-3.297 Max. :8.904 Max. :2.186 Max. :0.37965
RRV_RMSSD RRV_MeanBB RRV_SDBB RRV_SDSD
Min. : 959.8 Min. : 3125 Min. : 801.7 Min. : 961.2
1st Qu.:2067.0 1st Qu.: 4555 1st Qu.:1872.0 1st Qu.:2072.3
Median :2803.1 Median : 5634 Median :2357.4 Median :2812.3
Mean :3089.3 Mean : 6081 Mean :2546.9 Mean :3097.9
3rd Qu.:3701.2 3rd Qu.: 7595 3rd Qu.:3174.9 3rd Qu.:3709.8
Max. :8505.1 Max. :10367 Max. :5892.8 Max. :8540.6
RRV_CVBB RRV_CVSD RRV_MedianBB RRV_MadBB
Min. :0.2087 Min. :0.2425 Min. : 2756 Min. : 443.3
1st Qu.:0.3376 1st Qu.:0.3783 1st Qu.: 4190 1st Qu.:1071.5
Median :0.4068 Median :0.4980 Median : 4927 Median :1781.0
Mean :0.4192 Mean :0.5067 Mean : 5603 Mean :1783.0
3rd Qu.:0.4867 3rd Qu.:0.6008 3rd Qu.: 7266 3rd Qu.:2266.0
Max. :0.6368 Max. :0.8636 Max. :10747 Max. :4152.8
RRV_MCVBB RRV_VLF RRV_LF RRV_HF
Min. :0.1279 Min. :0.0007644 Min. :6.745e-05 Min. :3.737e-07
1st Qu.:0.2448 1st Qu.:0.0055229 1st Qu.:1.275e-03 1st Qu.:1.708e-05
Median :0.2922 Median :0.0083958 Median :2.097e-03 Median :5.331e-05
Mean :0.3114 Mean :0.0090367 Mean :2.953e-03 Mean :1.005e-04
3rd Qu.:0.3895 3rd Qu.:0.0121473 3rd Qu.:3.741e-03 3rd Qu.:1.159e-04
Max. :0.5676 Max. :0.0216785 Max. :1.188e-02 Max. :1.086e-03
RRV_LFHF RRV_LFn RRV_HFn RRV_SD1
Min. : 9.542 Min. :0.0484 Min. :0.0001092 Min. : 679.7
1st Qu.: 25.861 1st Qu.:0.1490 1st Qu.:0.0015639 1st Qu.:1465.4
Median : 43.844 Median :0.2123 Median :0.0052822 Median :1988.6
Mean : 107.957 Mean :0.2231 Mean :0.0069891 Mean :2190.5
3rd Qu.: 95.167 3rd Qu.:0.2834 3rd Qu.:0.0093055 3rd Qu.:2623.2
Max. :1786.515 Max. :0.4519 Max. :0.0449068 Max. :6039.1
RRV_SD2 RRV_SD2SD1 RRV_ApEn RRV_SampEn
Min. : 907.4 Min. :0.9509 Min. :0.5907 Min. :0.752
1st Qu.:2125.1 1st Qu.:1.1221 1st Qu.:0.8709 1st Qu.:1.308
Median :2684.3 Median :1.2886 Median :0.9765 Median :1.521
Mean :2831.0 Mean :1.3644 Mean :0.9549 Mean :1.512
3rd Qu.:3432.7 3rd Qu.:1.5004 3rd Qu.:1.0686 3rd Qu.:1.747
Max. :6948.5 Max. :2.7890 Max. :1.2668 Max. :2.179
RRV_DFA_alpha2 RRV_MFDFA_alpha2_Width RRV_MFDFA_alpha2_Peak
Min. :0.3693 Min. :0.06482 Min. :0.4013
1st Qu.:0.6488 1st Qu.:0.41459 1st Qu.:0.7154
Median :0.7351 Median :0.69647 Median :0.8634
Mean :0.7513 Mean :0.68092 Mean :0.8369
3rd Qu.:0.8617 3rd Qu.:0.88363 3rd Qu.:0.9423
Max. :1.1308 Max. :1.48613 Max. :1.3574
RRV_MFDFA_alpha2_Mean RRV_MFDFA_alpha2_Max RRV_MFDFA_alpha2_Delta
Min. :0.2565 Min. :-0.4437 Min. :-1.23020
1st Qu.:0.6953 1st Qu.: 0.2266 1st Qu.:-0.25577
Median :0.8609 Median : 0.4727 Median :-0.03016
Mean :0.8421 Mean : 0.4365 Mean :-0.01535
3rd Qu.:0.9895 3rd Qu.: 0.6386 3rd Qu.: 0.27522
Max. :1.4754 Max. : 1.0826 Max. : 1.07768
RRV_MFDFA_alpha2_Asymmetry RRV_MFDFA_alpha2_Fluctuation
Min. :-0.8910 Min. :8.492e-07
1st Qu.:-0.6045 1st Qu.:4.380e-05
Median :-0.4857 Median :1.181e-04
Mean :-0.4687 Mean :1.893e-04
3rd Qu.:-0.3867 3rd Qu.:2.342e-04
Max. : 0.0000 Max. :1.707e-03
RRV_MFDFA_alpha2_Increment RRV_DFA_alpha1 RRV_MFDFA_alpha1_Width
Min. :0.0007131 Min. :0.4483 Min. :0.6709
1st Qu.:0.0121300 1st Qu.:0.6195 1st Qu.:1.1946
Median :0.0262180 Median :0.7408 Median :1.5249
Mean :0.0343030 Mean :0.7403 Mean :1.5342
3rd Qu.:0.0449049 3rd Qu.:0.8268 3rd Qu.:1.8591
Max. :0.1970674 Max. :1.0598 Max. :2.3953
NA's :17 NA's :17
RRV_MFDFA_alpha1_Peak RRV_MFDFA_alpha1_Mean RRV_MFDFA_alpha1_Max
Min. :0.6197 Min. :0.4405 Min. :-1.65554
1st Qu.:0.8896 1st Qu.:1.0492 1st Qu.:-0.75060
Median :0.9918 Median :1.2330 Median :-0.43390
Mean :0.9744 Mean :1.2145 Mean :-0.40671
3rd Qu.:1.0540 3rd Qu.:1.3954 3rd Qu.:-0.04968
Max. :1.2260 Max. :1.7470 Max. : 0.98519
NA's :17 NA's :17 NA's :17
RRV_MFDFA_alpha1_Delta RRV_MFDFA_alpha1_Asymmetry RRV_MFDFA_alpha1_Fluctuation
Min. :-2.1389 Min. :-0.89707 Min. :0.000133
1st Qu.:-1.2574 1st Qu.:-0.43744 1st Qu.:0.000466
Median :-0.8318 Median :-0.34943 Median :0.000849
Mean :-0.7130 Mean :-0.35152 Mean :0.001403
3rd Qu.:-0.2943 3rd Qu.:-0.24635 3rd Qu.:0.002143
Max. : 1.2944 Max. :-0.03072 Max. :0.004655
NA's :17 NA's :17 NA's :17
RRV_MFDFA_alpha1_Increment age heart_rate rsp_rate
Min. :0.02943 Min. :18.00 Min. :52.35 Min. : 5.788
1st Qu.:0.08693 1st Qu.:21.00 1st Qu.:65.88 1st Qu.: 7.902
Median :0.16802 Median :22.00 Median :71.87 Median :10.649
Mean :0.18746 Mean :23.77 Mean :73.26 Mean :10.932
3rd Qu.:0.27977 3rd Qu.:25.00 3rd Qu.:79.79 3rd Qu.:13.171
Max. :0.50396 Max. :48.00 Max. :99.84 Max. :19.199
NA's :17
HRV_lnLF_lnHF hr_to_hrv log10_HRV_RMSSD log10_HRF_PAS
Min. :-0.6345 Min. : 71.12 Min. :0.897 Min. :-2.6522
1st Qu.: 0.2157 1st Qu.:127.57 1st Qu.:1.471 1st Qu.:-1.8322
Median : 0.8977 Median :164.10 Median :1.668 Median :-1.4913
Mean : 0.9513 Mean :174.54 Mean :1.643 Mean :-1.5751
3rd Qu.: 1.5567 3rd Qu.:223.79 3rd Qu.:1.822 3rd Qu.:-1.2924
Max. : 2.8052 Max. :401.01 Max. :2.246 Max. :-0.8053
log10_RRV_RMSSD
Min. :2.982
1st Qu.:3.315
Median :3.448
Mean :3.444
3rd Qu.:3.568
Max. :3.930
2 Time Domain Heart Rate Variability (HRV)
“Heart rate variability time-domain indices quantify the amount of HRV observed during monitoring periods that may range from <1 min to >24 h. These metrics include the SDNN, SDRR, SDANN, SDNN Index, RMSSD, NN50, pNN50, HR Max - HR Min, the HRV triangular index (HTI), and the Triangular Interpolation of the NN Interval Histogram” (Shaffer and Ginsberg 2017)
Code
some_params <- c('heart_rate', 'HRV_SDNN', 'HRV_CVNN', 'log10_HRV_RMSSD', 'HRV_IQRNN', 'HRV_HTI')
ggpairs(a_df_hrv,
columns = some_params,
aes(colour = group, alpha = .25),
progress = FALSE,
lower = list(continuous = wrap('points')))
2.1 Heart Rate (HR)
Code
heart_rate_lmer <- lmer(heart_rate ~ group*sex + age + (1|duo), a_df_hrv)
afex_plot(
heart_rate_lmer,
x = 'sex',
trace = 'group',
error_arg = list(width = .15),
dodge = .3,
data_arg = list(
position =
position_jitterdodge(
jitter.width = .1,
dodge.width = 0.3 ## needs to be same as dodge
)),
mapping = c('color'),
point_arg = list(size = 4)
)
Code
anova(heart_rate_lmer)Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
group 60.19 60.19 1 59 0.6805 0.4127314
sex 1283.99 1283.99 1 59 14.5177 0.0003336 ***
age 74.44 74.44 1 59 0.8416 0.3626619
group:sex 54.29 54.29 1 59 0.6139 0.4364625
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Code
a_posteriori(heart_rate_lmer)Random effect variances not available. Returned R2 does not account for random effects.
# R2 for Mixed Models
Conditional R2: NA
Marginal R2: 0.196
____________________________________________________________NOTE: Results may be misleading due to involvement in interactions$emmeans
sex emmean SE df lower.CL upper.CL
female 77.8 1.68 28.1 74.4 81.3
male 68.7 1.68 28.1 65.2 72.1
Results are averaged over the levels of: group
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95
$contrasts
contrast estimate SE df t.ratio p.value
female - male 9.15 2.4 28.8 3.805 0.0007
Results are averaged over the levels of: group
Degrees-of-freedom method: kenward-roger 2.2 HRV_SDNN
Code
sdnn_lmer <- lmer(HRV_SDNN ~ group*sex + age + (1|duo), a_df_hrv)
afex_plot(
sdnn_lmer,
x = 'sex',
trace = 'group',
error_arg = list(width = .15),
dodge = .3,
data_arg = list(
position =
position_jitterdodge(
jitter.width = .1,
dodge.width = 0.3 ## needs to be same as dodge
)),
mapping = c('color'),
point_arg = list(size = 4)
)
Code
anova(sdnn_lmer)Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
group 150.7 150.7 1 29.038 0.1911 0.665239
sex 10452.6 10452.6 1 29.196 13.2536 0.001044 **
age 675.1 675.1 1 55.032 0.8561 0.358885
group:sex 281.2 281.2 1 28.963 0.3566 0.555047
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Code
a_posteriori(sdnn_lmer)# R2 for Mixed Models
Conditional R2: 0.297
Marginal R2: 0.223
____________________________________________________________
# Intraclass Correlation Coefficient
Adjusted ICC: 0.095
Unadjusted ICC: 0.074
____________________________________________________________
simulated finite sample distribution of LRT. (p-value based on 10000
simulated values)
data:
humanity LRT = 0.54129, p-value = 0.1721
____________________________________________________________boundary (singular) fit: see help('isSingular')Warning in cov2cor(Vr): diag(V) had non-positive or NA entries; the non-finite
result may be dubious____________________________________________________________NOTE: Results may be misleading due to involvement in interactions$emmeans
sex emmean SE df lower.CL upper.CL
female 67.6 5.52 28.1 56.3 78.9
male 96.2 5.52 28.1 84.9 107.5
Results are averaged over the levels of: group
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95
$contrasts
contrast estimate SE df t.ratio p.value
female - male -28.6 7.88 28.8 -3.636 0.0011
Results are averaged over the levels of: group
Degrees-of-freedom method: kenward-roger 2.3 HRV_CVNN
Code
cvnn_lmer <- lmer(HRV_CVNN ~ group*sex + age + (1|duo), a_df_hrv)
afex_plot(
cvnn_lmer,
x = 'sex',
trace = 'group',
error_arg = list(width = .15),
dodge = .3,
data_arg = list(
position =
position_jitterdodge(
jitter.width = .1,
dodge.width = 0.3 ## needs to be same as dodge
)),
mapping = c('color'),
point_arg = list(size = 4)
)
Code
anova(cvnn_lmer)Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
group 0.0011675 0.0011675 1 59 1.2494 0.268198
sex 0.0088804 0.0088804 1 59 9.5037 0.003116 **
age 0.0020636 0.0020636 1 59 2.2084 0.142585
group:sex 0.0000440 0.0000440 1 59 0.0471 0.829026
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Code
a_posteriori(cvnn_lmer)Random effect variances not available. Returned R2 does not account for random effects.
# R2 for Mixed Models
Conditional R2: NA
Marginal R2: 0.194
____________________________________________________________NOTE: Results may be misleading due to involvement in interactions$emmeans
sex emmean SE df lower.CL upper.CL
female 0.0845 0.00546 28.1 0.0733 0.0957
male 0.1086 0.00546 28.1 0.0974 0.1198
Results are averaged over the levels of: group
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95
$contrasts
contrast estimate SE df t.ratio p.value
female - male -0.0241 0.00781 28.8 -3.079 0.0045
Results are averaged over the levels of: group
Degrees-of-freedom method: kenward-roger 2.4 log10_HRV_RMSSD
Code
log10_rmssd_lmer <- lmer(log10_HRV_RMSSD ~ group*sex + age + (1|duo), a_df_hrv)
afex_plot(
log10_rmssd_lmer,
x = 'sex',
trace = 'group',
error_arg = list(width = .15),
dodge = .3,
data_arg = list(
position =
position_jitterdodge(
jitter.width = .1,
dodge.width = 0.3 ## needs to be same as dodge
)),
mapping = c('color'),
point_arg = list(size = 4)
)
Code
anova(log10_rmssd_lmer)Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
group 0.00203 0.00203 1 59 0.0327 0.857137
sex 0.55994 0.55994 1 59 9.0334 0.003889 **
age 0.02437 0.02437 1 59 0.3932 0.533065
group:sex 0.06048 0.06048 1 59 0.9756 0.327314
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Code
a_posteriori(log10_rmssd_lmer)Random effect variances not available. Returned R2 does not account for random effects.
# R2 for Mixed Models
Conditional R2: NA
Marginal R2: 0.159
____________________________________________________________NOTE: Results may be misleading due to involvement in interactions$emmeans
sex emmean SE df lower.CL upper.CL
female 1.55 0.0445 28.1 1.46 1.64
male 1.74 0.0445 28.1 1.65 1.83
Results are averaged over the levels of: group
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95
$contrasts
contrast estimate SE df t.ratio p.value
female - male -0.191 0.0636 28.8 -3.002 0.0055
Results are averaged over the levels of: group
Degrees-of-freedom method: kenward-roger 2.5 HRV_IQRNN
Code
iqrnn_lmer <- lmer(HRV_IQRNN ~ group*sex + age + (1|duo), a_df_hrv)
afex_plot(
iqrnn_lmer,
x = 'sex',
trace = 'group',
error_arg = list(width = .15),
dodge = .3,
data_arg = list(
position =
position_jitterdodge(
jitter.width = .1,
dodge.width = 0.3 ## needs to be same as dodge
)),
mapping = c('color'),
point_arg = list(size = 4)
)
Code
anova(iqrnn_lmer)Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
group 524.2 524.2 1 29.067 0.2564 0.616412
sex 18402.6 18402.6 1 29.221 9.0024 0.005469 **
age 878.8 878.8 1 54.084 0.4299 0.514808
group:sex 356.5 356.5 1 28.993 0.1744 0.679303
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Code
a_posteriori(iqrnn_lmer)# R2 for Mixed Models
Conditional R2: 0.274
Marginal R2: 0.164
____________________________________________________________
# Intraclass Correlation Coefficient
Adjusted ICC: 0.132
Unadjusted ICC: 0.110
____________________________________________________________
simulated finite sample distribution of LRT. (p-value based on 10000
simulated values)
data:
humanity LRT = 1.1236, p-value = 0.1092
____________________________________________________________
simulated finite sample distribution of LRT. (p-value based on 10000
simulated values)
data:
mindfulness LRT = 0, p-value = 0.3952
____________________________________________________________NOTE: Results may be misleading due to involvement in interactions$emmeans
sex emmean SE df lower.CL upper.CL
female 95.6 9.21 28.1 76.7 114
male 135.0 9.21 28.1 116.1 154
Results are averaged over the levels of: group
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95
$contrasts
contrast estimate SE df t.ratio p.value
female - male -39.4 13.2 28.8 -2.997 0.0056
Results are averaged over the levels of: group
Degrees-of-freedom method: kenward-roger 2.6 HRV_HTI
“HRVI is a simple geometrical measure of HRV that can be derived from standard ECG recordings and expresses overall HRV.17 Depressed HRVI reflects sympathovagal imbalance, but does not distinguish between particular changes in sympathetic and vagal activity.” (Hämmerle et al. 2020)
Code
hti_lmer <- lmer(HRV_HTI ~ group*sex + age + (1|duo), a_df_hrv)
afex_plot(
hti_lmer,
x = 'sex',
trace = 'group',
error_arg = list(width = .15),
dodge = .3,
data_arg = list(
position =
position_jitterdodge(
jitter.width = .1,
dodge.width = 0.3 ## needs to be same as dodge
)),
mapping = c('color'),
point_arg = list(size = 4)
)
Code
anova(hti_lmer)Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
group 9.328 9.328 1 59 0.1988 0.65730
sex 312.018 312.018 1 59 6.6504 0.01243 *
age 71.140 71.140 1 59 1.5163 0.22307
group:sex 13.667 13.667 1 59 0.2913 0.59141
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Code
a_posteriori(hti_lmer)Random effect variances not available. Returned R2 does not account for random effects.
# R2 for Mixed Models
Conditional R2: NA
Marginal R2: 0.137
____________________________________________________________NOTE: Results may be misleading due to involvement in interactions$emmeans
sex emmean SE df lower.CL upper.CL
female 18.8 1.22 28.1 16.3 21.3
male 23.3 1.22 28.1 20.8 25.8
Results are averaged over the levels of: group
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95
$contrasts
contrast estimate SE df t.ratio p.value
female - male -4.51 1.75 28.8 -2.575 0.0154
Results are averaged over the levels of: group
Degrees-of-freedom method: kenward-roger 3 Frequency Domain HRV
“Analogous to the electroencephalogram, we can use Fast Fourier Transformation (FFT) or autoregressive (AR) modeling to separate HRV into its component ULF, VLF, LF, and HF rhythms that operate within different frequency ranges. This is analogous to a prism that refracts light into its component wavelengths” (Shaffer and Ginsberg 2017)
Code
some_params <- c('HRV_LFn', 'HRV_HFn', 'HRV_lnLF_lnHF', 'hr_to_hrv')
ggpairs(a_df_hrv,
columns = some_params,
aes(colour = group, alpha = .25),
progress = FALSE,
lower = list(continuous = wrap('points')))
Code
rosnerTest(a_df_hrv$hr_to_hrv)
Results of Outlier Test
-------------------------
Test Method: Rosner's Test for Outliers
Hypothesized Distribution: Normal
Data: a_df_hrv$hr_to_hrv
Sample Size: 64
Test Statistics: R.1 = 3.206847
R.2 = 3.482916
R.3 = 2.674651
Test Statistic Parameter: k = 3
Alternative Hypothesis: Up to 3 observations are not
from the same Distribution.
Type I Error: 5%
Number of Outliers Detected: 2
i Mean.i SD.i Value Obs.Num R.i+1 lambda.i+1 Outlier
1 0 174.5414 70.62028 401.0099 46 3.206847 3.224177 TRUE
2 1 170.9467 65.01775 397.3981 35 3.482916 3.218230 TRUE
3 2 167.2942 58.67188 324.2210 39 2.674651 3.212165 FALSECode
ggpairs(a_df_hrv[a_df_hrv$hr_to_hrv < 397, ],
columns = some_params,
aes(colour = group, alpha = .25),
progress = FALSE,
lower = list(continuous = wrap('points')))
3.1 HRV_LFn
Code
lfn_lmer <- lmer(HRV_LFn ~ group*sex + age + (1|duo), a_df_hrv[a_df_hrv$hr_to_hrv < 397, ])
afex_plot(
lfn_lmer,
x = 'sex',
trace = 'group',
error_arg = list(width = .15),
dodge = .3,
data_arg = list(
position =
position_jitterdodge(
jitter.width = .1,
dodge.width = 0.3 ## needs to be same as dodge
)),
mapping = c('color'),
point_arg = list(size = 4)
)
Code
anova(lfn_lmer)Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
group 0.023012 0.023012 1 30.078 0.9551 0.33621
sex 0.092410 0.092410 1 30.262 3.8356 0.05945 .
age 0.031864 0.031864 1 52.636 1.3226 0.25533
group:sex 0.099780 0.099780 1 29.989 4.1415 0.05077 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Code
a_posteriori(lfn_lmer)# R2 for Mixed Models
Conditional R2: 0.245
Marginal R2: 0.169
____________________________________________________________
# Intraclass Correlation Coefficient
Adjusted ICC: 0.091
Unadjusted ICC: 0.076
____________________________________________________________
simulated finite sample distribution of LRT. (p-value based on 10000
simulated values)
data:
humanity LRT = 0.061964, p-value = 0.3096
____________________________________________________________
simulated finite sample distribution of LRT. (p-value based on 10000
simulated values)
data:
mindfulness LRT = 0, p-value = 0.4073.2 HRV_HFn
Code
hfn_lmer <- lmer(HRV_HFn ~ group*sex + age + (1|duo), a_df_hrv[a_df_hrv$hr_to_hrv < 397, ])
afex_plot(
hfn_lmer,
x = 'sex',
trace = 'group',
error_arg = list(width = .15),
dodge = .3,
data_arg = list(
position =
position_jitterdodge(
jitter.width = .1,
dodge.width = 0.3 ## needs to be same as dodge
)),
mapping = c('color'),
point_arg = list(size = 4)
)
Code
anova(hfn_lmer)Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
group 0.034852 0.034852 1 29.562 2.4301 0.12966
sex 0.068892 0.068892 1 29.752 4.8036 0.03637 *
age 0.000425 0.000425 1 53.704 0.0297 0.86390
group:sex 0.070163 0.070163 1 29.471 4.8923 0.03488 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Code
a_posteriori(hfn_lmer)# R2 for Mixed Models
Conditional R2: 0.219
Marginal R2: 0.184
____________________________________________________________
# Intraclass Correlation Coefficient
Adjusted ICC: 0.043
Unadjusted ICC: 0.035
____________________________________________________________
simulated finite sample distribution of LRT. (p-value based on 10000
simulated values)
data:
humanity LRT = 3.1742, p-value = 0.0274
____________________________________________________________boundary (singular) fit: see help('isSingular')Warning in cov2cor(Vr): diag(V) had non-positive or NA entries; the non-finite
result may be dubious____________________________________________________________NOTE: Results may be misleading due to involvement in interactions$emmeans
sex emmean SE df lower.CL upper.CL
female 0.249 0.0232 29.4 0.202 0.297
male 0.178 0.0224 26.8 0.132 0.224
Results are averaged over the levels of: group
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95
$contrasts
contrast estimate SE df t.ratio p.value
female - male 0.0714 0.0327 29.1 2.186 0.0370
Results are averaged over the levels of: group
Degrees-of-freedom method: kenward-roger
____________________________________________________________
$emmeans
group sex emmean SE df lower.CL upper.CL
humanity female 0.310 0.0314 26.3 0.246 0.375
mindfulness female 0.188 0.0352 34.2 0.116 0.259
humanity male 0.167 0.0314 26.4 0.103 0.232
mindfulness male 0.188 0.0314 26.4 0.124 0.253
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95
$contrasts
contrast estimate SE df t.ratio p.value
humanity female - mindfulness female 0.122507 0.0479 31.5 2.559 0.0699
humanity female - humanity male 0.143263 0.0442 26.0 3.241 0.0161
humanity female - mindfulness male 0.122121 0.0442 26.0 2.763 0.0478
mindfulness female - humanity male 0.020755 0.0480 31.7 0.432 0.9725
mindfulness female - mindfulness male -0.000386 0.0480 31.6 -0.008 1.0000
humanity male - mindfulness male -0.021141 0.0442 26.0 -0.478 0.9632
Degrees-of-freedom method: kenward-roger
P value adjustment: tukey method for comparing a family of 4 estimates 3.3 HRV_lnLF_lnHF
Code
lfhf_lmer <- lmer(HRV_lnLF_lnHF ~ group*sex + age + (1|duo), a_df_hrv[a_df_hrv$hr_to_hrv < 397, ])
afex_plot(
lfhf_lmer,
x = 'sex',
trace = 'group',
error_arg = list(width = .15),
dodge = .3,
data_arg = list(
position =
position_jitterdodge(
jitter.width = .1,
dodge.width = 0.3 ## needs to be same as dodge
)),
mapping = c('color'),
point_arg = list(size = 4)
)
Code
anova(lfhf_lmer)Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
group 1.9973 1.9973 1 30.134 2.6413 0.11453
sex 2.8800 2.8800 1 30.325 3.8085 0.06028 .
age 0.1704 0.1704 1 54.253 0.2253 0.63691
group:sex 3.5085 3.5085 1 30.042 4.6396 0.03939 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Code
a_posteriori(lfhf_lmer)# R2 for Mixed Models
Conditional R2: 0.193
Marginal R2: 0.173
____________________________________________________________
# Intraclass Correlation Coefficient
Adjusted ICC: 0.024
Unadjusted ICC: 0.020
____________________________________________________________
simulated finite sample distribution of LRT. (p-value based on 10000
simulated values)
data:
humanity LRT = 0.34642, p-value = 0.2181
____________________________________________________________boundary (singular) fit: see help('isSingular')Warning in cov2cor(Vr): diag(V) had non-positive or NA entries; the non-finite
result may be dubious____________________________________________________________
$emmeans
group sex emmean SE df lower.CL upper.CL
humanity female 0.309 0.224 26.3 -0.150 0.769
mindfulness female 1.185 0.252 34.2 0.673 1.696
humanity male 1.262 0.224 26.4 0.801 1.722
mindfulness male 1.140 0.224 26.4 0.680 1.600
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95
$contrasts
contrast estimate SE df t.ratio p.value
humanity female - mindfulness female -0.8756 0.342 31.5 -2.563 0.0694
humanity female - humanity male -0.9525 0.315 26.0 -3.025 0.0267
humanity female - mindfulness male -0.8305 0.315 26.0 -2.638 0.0627
mindfulness female - humanity male -0.0768 0.343 31.8 -0.224 0.9960
mindfulness female - mindfulness male 0.0451 0.343 31.7 0.132 0.9992
humanity male - mindfulness male 0.1220 0.315 26.0 0.387 0.9798
Degrees-of-freedom method: kenward-roger
P value adjustment: tukey method for comparing a family of 4 estimates 3.4 HR to HRV Ratio
“The current findings revealed that Heart rate/LF non-linearly increased during incremental exercise along with noradrenaline and metabolic parameters, in contrast to LF/HF. In addition, Heart rate/LF was strongly correlated with noradrenaline and metabolic parameters from rest throughout the exercise stages, indicating that Heart rate/LF reflects sympathetic nervous activation during exercise. Thus, Heart rate/LF may provide as an index of sympathetic nervous activity in HRV.” (Tanoue et al. 2022)
Code
hr_to_hrv_lmer <- lmer(hr_to_hrv ~ group*sex + age + (1|duo), a_df_hrv[a_df_hrv$hr_to_hrv < 397, ])
afex_plot(
hr_to_hrv_lmer,
x = 'sex',
trace = 'group',
error_arg = list(width = .15),
dodge = .3,
data_arg = list(
position =
position_jitterdodge(
jitter.width = .1,
dodge.width = 0.3 ## needs to be same as dodge
)),
mapping = c('color'),
point_arg = list(size = 4)
)
Code
anova(hr_to_hrv_lmer)Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
group 2917 2917 1 30.176 1.2445 0.2734083
sex 33068 33068 1 30.359 14.1096 0.0007327 ***
age 3001 3001 1 52.369 1.2805 0.2629602
group:sex 9057 9057 1 30.088 3.8644 0.0586071 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Code
a_posteriori(hr_to_hrv_lmer)# R2 for Mixed Models
Conditional R2: 0.355
Marginal R2: 0.281
____________________________________________________________
# Intraclass Correlation Coefficient
Adjusted ICC: 0.103
Unadjusted ICC: 0.074
____________________________________________________________
simulated finite sample distribution of LRT. (p-value based on 10000
simulated values)
data:
humanity LRT = 0, p-value = 0.4024
____________________________________________________________
simulated finite sample distribution of LRT. (p-value based on 10000
simulated values)
data:
mindfulness LRT = 0.026917, p-value = 0.3586
____________________________________________________________NOTE: Results may be misleading due to involvement in interactions$emmeans
sex emmean SE df lower.CL upper.CL
female 194 9.95 29.4 173 214
male 141 9.59 26.9 122 161
Results are averaged over the levels of: group
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95
$contrasts
contrast estimate SE df t.ratio p.value
female - male 52.4 14 29.1 3.747 0.0008
Results are averaged over the levels of: group
Degrees-of-freedom method: kenward-roger 4 HR Periodic Components, greater peak (borrowed from EEG PSD analysis)
“In the frequency domain, oscillations manifest as narrow- band peaks of power above the aperiodic component (Fig. 1a) Examining predefined frequency regions in the power spectrum, or applying narrowband filtering (for example, 8-12 Hz for the alpha band) without parameterization, can lead to a misrepresentation and misinterpretation of physiological phenomena, because appar- ent changes in narrowband power can reflect several different phys- iological processes” (Donoghue et al. 2020)
Code
some_params <- c('frequency', 'power', 'bandwidth')
ggpairs(a_df_pow_peaks,
columns = some_params,
aes(colour = group, alpha = .25),
progress = FALSE,
lower = list(continuous = wrap('points')))
No outliers:
Code
rosnerTest(a_df_pow_peaks$power)
Results of Outlier Test
-------------------------
Test Method: Rosner's Test for Outliers
Hypothesized Distribution: Normal
Data: a_df_pow_peaks$power
Sample Size: 64
Test Statistics: R.1 = 3.576880
R.2 = 2.562102
R.3 = 2.397863
Test Statistic Parameter: k = 3
Alternative Hypothesis: Up to 3 observations are not
from the same Distribution.
Type I Error: 5%
Number of Outliers Detected: 1
i Mean.i SD.i Value Obs.Num R.i+1 lambda.i+1 Outlier
1 0 0.3754621 0.2039442 1.1049460 58 3.576880 3.224177 TRUE
2 1 0.3638830 0.1831525 0.8331384 32 2.562102 3.218230 FALSE
3 2 0.3563143 0.1744325 0.7745796 12 2.397863 3.212165 FALSECode
rosnerTest(a_df_pow_peaks$bandwidth)
Results of Outlier Test
-------------------------
Test Method: Rosner's Test for Outliers
Hypothesized Distribution: Normal
Data: a_df_pow_peaks$bandwidth
Sample Size: 64
Test Statistics: R.1 = 3.804966
R.2 = 3.103980
R.3 = 2.958843
Test Statistic Parameter: k = 3
Alternative Hypothesis: Up to 3 observations are not
from the same Distribution.
Type I Error: 5%
Number of Outliers Detected: 1
i Mean.i SD.i Value Obs.Num R.i+1 lambda.i+1 Outlier
1 0 0.06569515 0.02327964 0.1542734 10 3.804966 3.224177 TRUE
2 1 0.06428915 0.02054565 0.1280624 63 3.103980 3.218230 FALSE
3 2 0.06326055 0.01900778 0.1195016 7 2.958843 3.212165 FALSECode
ggpairs(a_df_pow_peaks[a_df_pow_peaks$power < 1 & a_df_pow_peaks$bandwidth < .15, ],
columns = some_params,
aes(colour = group, alpha = .25),
progress = FALSE,
lower = list(continuous = wrap('points')))
4.1 Frequency
Code
frequency_lmer <- lmer(frequency ~ group*sex + age + (1|duo), a_df_pow_peaks[a_df_pow_peaks$power < 1 & a_df_pow_peaks$bandwidth < .15, ])
afex_plot(
frequency_lmer,
x = 'sex',
trace = 'group',
error_arg = list(width = .15),
dodge = .3,
data_arg = list(
position =
position_jitterdodge(
jitter.width = .1,
dodge.width = 0.3 ## needs to be same as dodge
)),
mapping = c('color'),
point_arg = list(size = 4)
)
Code
anova(frequency_lmer)Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
group 0.0170664 0.0170664 1 29.360 3.1823 0.08478 .
sex 0.0114298 0.0114298 1 29.634 2.1313 0.15484
age 0.0009756 0.0009756 1 52.606 0.1819 0.67147
group:sex 0.0038904 0.0038904 1 29.235 0.7254 0.40129
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Code
a_posteriori(frequency_lmer)# R2 for Mixed Models
Conditional R2: 0.225
Marginal R2: 0.107
____________________________________________________________
# Intraclass Correlation Coefficient
Adjusted ICC: 0.132
Unadjusted ICC: 0.118
____________________________________________________________
simulated finite sample distribution of LRT. (p-value based on 10000
simulated values)
data:
humanity LRT = 0, p-value = 0.3979
____________________________________________________________
simulated finite sample distribution of LRT. (p-value based on 10000
simulated values)
data:
mindfulness LRT = 0.19194, p-value = 0.25754.2 Power
Code
power_lmer <- lmer(power ~ group*sex + age + (1|duo), a_df_pow_peaks[a_df_pow_peaks$power < 1 & a_df_pow_peaks$bandwidth < .15, ])
afex_plot(
power_lmer,
x = 'sex',
trace = 'group',
error_arg = list(width = .15),
dodge = .3,
data_arg = list(
position =
position_jitterdodge(
jitter.width = .1,
dodge.width = 0.3 ## needs to be same as dodge
)),
mapping = c('color'),
point_arg = list(size = 4)
)
Code
anova(power_lmer)Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
group 0.0091871 0.0091871 1 26.615 0.2899 0.5948
sex 0.0116055 0.0116055 1 26.885 0.3662 0.5502
age 0.0015072 0.0015072 1 52.417 0.0476 0.8282
group:sex 0.0130448 0.0130448 1 26.496 0.4116 0.5267Code
a_posteriori(power_lmer)# R2 for Mixed Models
Conditional R2: 0.128
Marginal R2: 0.021
____________________________________________________________
# Intraclass Correlation Coefficient
Adjusted ICC: 0.110
Unadjusted ICC: 0.108
____________________________________________________________boundary (singular) fit: see help('isSingular')Warning in cov2cor(Vr): diag(V) had non-positive or NA entries; the non-finite
result may be dubious____________________________________________________________
simulated finite sample distribution of LRT. (p-value based on 10000
simulated values)
data:
mindfulness LRT = 1.3493, p-value = 0.09924.3 bandwidth
Code
bandwidth_lmer <- lmer(bandwidth ~ group*sex + age + (1|duo), a_df_pow_peaks[a_df_pow_peaks$power < 1 & a_df_pow_peaks$bandwidth < .15, ])
afex_plot(
bandwidth_lmer,
x = 'sex',
trace = 'group',
error_arg = list(width = .15),
dodge = .3,
data_arg = list(
position =
position_jitterdodge(
jitter.width = .1,
dodge.width = 0.3 ## needs to be same as dodge
)),
mapping = c('color'),
point_arg = list(size = 4)
)
Code
anova(bandwidth_lmer)Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
group 1.7162e-05 1.7162e-05 1 28.702 0.0443 0.8348
sex 2.2186e-05 2.2186e-05 1 28.978 0.0573 0.8126
age 8.1796e-05 8.1796e-05 1 52.206 0.2111 0.6478
group:sex 3.1311e-04 3.1311e-04 1 28.575 0.8081 0.3762Code
a_posteriori(bandwidth_lmer)# R2 for Mixed Models
Conditional R2: 0.158
Marginal R2: 0.021
____________________________________________________________
# Intraclass Correlation Coefficient
Adjusted ICC: 0.140
Unadjusted ICC: 0.137
____________________________________________________________
simulated finite sample distribution of LRT. (p-value based on 10000
simulated values)
data:
humanity LRT = 0, p-value = 0.4022
____________________________________________________________
simulated finite sample distribution of LRT. (p-value based on 10000
simulated values)
data:
mindfulness LRT = 0.074103, p-value = 0.30535 HR Aperiodic Components (borrowed from EEG PSD analysis)
“In neural data, this aperiodic activity has a 1/f-like distribu- tion, with exponentially decreasing power across increasing fre- quencies. This component can be characterized by a 1/fχ function, whereby the χ parameter, hereafter referred to as the aperiodic exponent, reflects the pattern of aperiodic power across frequen- cies, and is equivalent to the negative slope of the power spectrum when measured in log-log space. The aperiodic component is additionally parameterized with an ‘offset’ parameter, which reflects the uniform shift of power across frequencies. This aperiodic com- ponent has traditionally been ignored, or is treated either as noise or as a nuisance variable to be corrected for, such as is done in spectral whitening, rather than a feature to be explicitly parameterized.” (Donoghue et al. 2020)
Code
some_params <- c('exponent', 'log_knee', 'offset')
ggpairs(a_df_aperiodic,
columns = some_params,
aes(colour = group, alpha = .25),
progress = FALSE,
lower = list(continuous = wrap('points')))
No outliers
Code
rosnerTest(a_df_aperiodic$exponent)
Results of Outlier Test
-------------------------
Test Method: Rosner's Test for Outliers
Hypothesized Distribution: Normal
Data: a_df_aperiodic$exponent
Sample Size: 64
Test Statistics: R.1 = 3.972216
R.2 = 2.396083
R.3 = 2.134735
Test Statistic Parameter: k = 3
Alternative Hypothesis: Up to 3 observations are not
from the same Distribution.
Type I Error: 5%
Number of Outliers Detected: 1
i Mean.i SD.i Value Obs.Num R.i+1 lambda.i+1 Outlier
1 0 4.023082 1.0207330 8.077654 58 3.972216 3.224177 TRUE
2 1 3.958724 0.8884468 6.087516 43 2.396083 3.218230 FALSE
3 2 3.924389 0.8525188 5.744291 41 2.134735 3.212165 FALSECode
rosnerTest(a_df_aperiodic$log_knee)
Results of Outlier Test
-------------------------
Test Method: Rosner's Test for Outliers
Hypothesized Distribution: Normal
Data: a_df_aperiodic$log_knee
Sample Size: 64
Test Statistics: R.1 = 4.044837
R.2 = 2.344964
R.3 = 2.263906
Test Statistic Parameter: k = 3
Alternative Hypothesis: Up to 3 observations are not
from the same Distribution.
Type I Error: 5%
Number of Outliers Detected: 1
i Mean.i SD.i Value Obs.Num R.i+1 lambda.i+1 Outlier
1 0 -8.657766 1.1211445 -4.122919 49 4.044837 3.224177 TRUE
2 1 -8.729748 0.9696819 -11.003617 23 2.344964 3.218230 FALSE
3 2 -8.693072 0.9325064 -6.581965 39 2.263906 3.212165 FALSECode
ggpairs(a_df_aperiodic[a_df_aperiodic$exponent < 8 & a_df_aperiodic$log_knee < -5, ],
columns = some_params,
aes(colour = group, alpha = .25),
progress = FALSE,
lower = list(continuous = wrap('points')))
5.1 Exponent
Code
exponent_lmer <- lmer(exponent ~ group*sex + age + (1|duo), a_df_aperiodic[a_df_aperiodic$exponent < 8 & a_df_aperiodic$log_knee < -5, ])
afex_plot(
exponent_lmer,
x = 'sex',
trace = 'group',
error_arg = list(width = .15),
dodge = .3,
data_arg = list(
position =
position_jitterdodge(
jitter.width = .1,
dodge.width = 0.3 ## needs to be same as dodge
)),
mapping = c('color'),
point_arg = list(size = 4)
)
Code
anova(exponent_lmer)Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
group 0.86381 0.86381 1 28.968 1.4908 0.2319
sex 0.27189 0.27189 1 30.286 0.4692 0.4986
age 0.98028 0.98028 1 49.620 1.6918 0.1994
group:sex 0.23294 0.23294 1 29.828 0.4020 0.5309Code
a_posteriori(exponent_lmer)# R2 for Mixed Models
Conditional R2: 0.295
Marginal R2: 0.065
____________________________________________________________
# Intraclass Correlation Coefficient
Adjusted ICC: 0.245
Unadjusted ICC: 0.229
____________________________________________________________
simulated finite sample distribution of LRT. (p-value based on 10000
simulated values)
data:
humanity LRT = 0.32882, p-value = 0.2113
____________________________________________________________
simulated finite sample distribution of LRT. (p-value based on 10000
simulated values)
data:
mindfulness LRT = 0.67729, p-value = 0.1655.2 log_knee
Code
if('log_knee' %in% colnames(a_df_aperiodic)) {
#| label: fig-log_knee
#| fig-cap: 'log_knee'
#| warning: false
log_knee_lmer <- lmer(log_knee ~ group*sex + age + (1|duo), a_df_aperiodic[a_df_aperiodic$exponent < 8 & a_df_aperiodic$log_knee < -5, ])
afex_plot(
log_knee_lmer,
x = 'sex',
trace = 'group',
error_arg = list(width = .15),
dodge = .3,
data_arg = list(
position =
position_jitterdodge(
jitter.width = .1,
dodge.width = 0.3 ## needs to be same as dodge
)),
mapping = c('color'),
point_arg = list(size = 4)
)
} else {
summary('Fitted with fixed mode, no knee parameter included.')
}Aggregating data over: duo
Code
anova(log_knee_lmer)Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
group 0.2295 0.2295 1 29.068 0.3382 0.565372
sex 0.9431 0.9431 1 30.410 1.3895 0.247639
age 6.6012 6.6012 1 51.508 9.7256 0.002973 **
group:sex 0.5544 0.5544 1 29.956 0.8168 0.373315
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Code
a_posteriori(log_knee_lmer)# R2 for Mixed Models
Conditional R2: 0.317
Marginal R2: 0.167
____________________________________________________________
# Intraclass Correlation Coefficient
Adjusted ICC: 0.181
Unadjusted ICC: 0.150
____________________________________________________________boundary (singular) fit: see help('isSingular')Warning in cov2cor(Vr): diag(V) had non-positive or NA entries; the non-finite
result may be dubious____________________________________________________________
simulated finite sample distribution of LRT. (p-value based on 10000
simulated values)
data:
mindfulness LRT = 1.9652, p-value = 0.0685
____________________________________________________________
[1] "Slope for age = -0.0719043815721009"5.3 Offset
Code
offset_lmer <- lmer(offset ~ group*sex + age + (1|duo), a_df_aperiodic[a_df_aperiodic$exponent < 8, ])
afex_plot(
offset_lmer,
x = 'sex',
trace = 'group',
error_arg = list(width = .15),
dodge = .3,
data_arg = list(
position =
position_jitterdodge(
jitter.width = .1,
dodge.width = 0.3 ## needs to be same as dodge
)),
mapping = c('color'),
point_arg = list(size = 4)
)
Code
anova(offset_lmer)Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
group 0.49417 0.49417 1 28.631 1.6981 0.20292
sex 1.54650 1.54650 1 28.909 5.3142 0.02853 *
age 0.86811 0.86811 1 54.640 2.9831 0.08979 .
group:sex 0.30467 0.30467 1 28.559 1.0469 0.31481
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Code
a_posteriori(offset_lmer)# R2 for Mixed Models
Conditional R2: 0.244
Marginal R2: 0.183
____________________________________________________________
# Intraclass Correlation Coefficient
Adjusted ICC: 0.075
Unadjusted ICC: 0.061
____________________________________________________________boundary (singular) fit: see help('isSingular')Warning in cov2cor(Vr): diag(V) had non-positive or NA entries; the non-finite
result may be dubious____________________________________________________________
simulated finite sample distribution of LRT. (p-value based on 10000
simulated values)
data:
mindfulness LRT = 0.11848, p-value = 0.2979
____________________________________________________________NOTE: Results may be misleading due to involvement in interactions$emmeans
sex emmean SE df lower.CL upper.CL
female 0.315 0.106 28.4 0.0985 0.531
male 0.659 0.104 27.5 0.4463 0.873
Results are averaged over the levels of: group
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95
$contrasts
contrast estimate SE df t.ratio p.value
female - male -0.345 0.15 28.6 -2.301 0.0289
Results are averaged over the levels of: group
Degrees-of-freedom method: kenward-roger 6 Poincaré Plot HRV
“A Poincaré plot (return map) is graphed by plotting every R-R interval against the prior interval, creating a scatter plot. Poincaré plot analysis allows researchers to visually search for patterns buried within a time series (a sequence of values from successive measurements). Unlike frequency-domain measurements, Poincaré plot analysis is insensitive to changes in trends in the R-R intervals” (Shaffer and Ginsberg 2017)
Code
some_params <- c('HRV_SD2', 'HRV_SD1SD2')
ggpairs(a_df_hrv,
columns = some_params,
aes(colour = group, alpha = .25),
progress = FALSE,
lower = list(continuous = wrap('points')))
No outliers:
Code
rosnerTest(a_df_hrv$HRV_SD1SD2)
Results of Outlier Test
-------------------------
Test Method: Rosner's Test for Outliers
Hypothesized Distribution: Normal
Data: a_df_hrv$HRV_SD1SD2
Sample Size: 64
Test Statistics: R.1 = 5.256081
R.2 = 2.671331
R.3 = 2.432261
Test Statistic Parameter: k = 3
Alternative Hypothesis: Up to 3 observations are not
from the same Distribution.
Type I Error: 5%
Number of Outliers Detected: 1
i Mean.i SD.i Value Obs.Num R.i+1 lambda.i+1 Outlier
1 0 0.3209757 0.10489220 0.8722977 49 5.256081 3.224177 TRUE
2 1 0.3122246 0.07873690 0.5225569 41 2.671331 3.218230 FALSE
3 2 0.3088321 0.07459351 0.4902630 43 2.432261 3.212165 FALSECode
ggpairs(a_df_hrv[a_df_hrv$HRV_SD1SD2 < .8, ],
columns = some_params,
aes(colour = group, alpha = .25),
progress = FALSE,
lower = list(continuous = wrap('points')))
6.1 HRV_SD2
Code
sd2_lmer <- lmer(HRV_SD2 ~ group*sex + age + (1|duo), a_df_hrv[a_df_hrv$HRV_SD1SD2 < .8, ])
afex_plot(
sd2_lmer,
x = 'sex',
trace = 'group',
error_arg = list(width = .15),
dodge = .3,
data_arg = list(
position =
position_jitterdodge(
jitter.width = .1,
dodge.width = 0.3 ## needs to be same as dodge
)),
mapping = c('color'),
point_arg = list(size = 4)
)
Code
anova(sd2_lmer)Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
group 261.9 261.9 1 28.782 0.1956 0.661581
sex 16527.3 16527.3 1 30.045 12.3430 0.001424 **
age 2584.9 2584.9 1 54.857 1.9304 0.170324
group:sex 58.4 58.4 1 29.745 0.0436 0.836007
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Code
a_posteriori(sd2_lmer)# R2 for Mixed Models
Conditional R2: 0.295
Marginal R2: 0.236
____________________________________________________________
# Intraclass Correlation Coefficient
Adjusted ICC: 0.077
Unadjusted ICC: 0.059
____________________________________________________________
simulated finite sample distribution of LRT. (p-value based on 10000
simulated values)
data:
humanity LRT = 0.59208, p-value = 0.1675
____________________________________________________________boundary (singular) fit: see help('isSingular')Warning in cov2cor(Vr): diag(V) had non-positive or NA entries; the non-finite
result may be dubious____________________________________________________________NOTE: Results may be misleading due to involvement in interactions$emmeans
sex emmean SE df lower.CL upper.CL
female 91.1 7.10 27.8 76.6 106
male 127.2 7.22 28.8 112.4 142
Results are averaged over the levels of: group
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95
$contrasts
contrast estimate SE df t.ratio p.value
female - male -36.1 10.3 29.3 -3.505 0.0015
Results are averaged over the levels of: group
Degrees-of-freedom method: kenward-roger 6.2 HRV_SD1SD2
Code
sd1sd2_lmer <- lmer(HRV_SD1SD2 ~ group*sex + age + (1|duo), a_df_hrv[a_df_hrv$HRV_SD1SD2 < .8, ])
afex_plot(
sd1sd2_lmer,
x = 'sex',
trace = 'group',
error_arg = list(width = .15),
dodge = .3,
data_arg = list(
position =
position_jitterdodge(
jitter.width = .1,
dodge.width = 0.3 ## needs to be same as dodge
)),
mapping = c('color'),
point_arg = list(size = 4)
)
Code
anova(sd1sd2_lmer)Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
group 0.0064701 0.0064701 1 28.692 1.0921 0.3047
sex 0.0004992 0.0004992 1 29.969 0.0843 0.7736
age 0.0067814 0.0067814 1 56.219 1.1447 0.2892
group:sex 0.0139797 0.0139797 1 29.661 2.3597 0.1351Code
a_posteriori(sd1sd2_lmer)# R2 for Mixed Models
Conditional R2: 0.101
Marginal R2: 0.090
____________________________________________________________
# Intraclass Correlation Coefficient
Adjusted ICC: 0.012
Unadjusted ICC: 0.011
____________________________________________________________
simulated finite sample distribution of LRT. (p-value based on 10000
simulated values)
data:
humanity LRT = 0, p-value = 0.3994
____________________________________________________________boundary (singular) fit: see help('isSingular')Warning in cov2cor(Vr): diag(V) had non-positive or NA entries; the non-finite
result may be dubious7 Heart Rate Fragmentation (HRF)
“A key aspect of the fragmentation paradigm is that dysfunction or actual breakdown of one or more system components allows for the emergence of high frequency fluctuations that compete with or even exceed the shortest-termmodulatory responsiveness of the vagal system. Therefore, a marker of this fragmentation on the surface ECG should be abrupt changes in the sign of heart rate acceleration, which may be periodic (as with classic sinus node alternans) or more random appearing (as with what has been termed “erratic sinus rhythm”). Such markers of fragmentation may be useful as correlates of cardiovascular aging and/or underlying organic heart disease.” (Costa, Davis, and Goldberger 2017)
Code
some_params <- c('HRF_PIP', 'HRF_IALS', 'HRF_PSS', 'HRF_PAS')
ggpairs(a_df_hrv,
columns = some_params,
aes(colour = group, alpha = .25),
progress = FALSE,
lower = list(continuous = wrap('points')))
Code
rosnerTest(a_df_hrv$HRF_PAS)
Results of Outlier Test
-------------------------
Test Method: Rosner's Test for Outliers
Hypothesized Distribution: Normal
Data: a_df_hrv$HRF_PAS
Sample Size: 64
Test Statistics: R.1 = 4.266096
R.2 = 2.672123
R.3 = 2.601687
Test Statistic Parameter: k = 3
Alternative Hypothesis: Up to 3 observations are not
from the same Distribution.
Type I Error: 5%
Number of Outliers Detected: 1
i Mean.i SD.i Value Obs.Num R.i+1 lambda.i+1 Outlier
1 0 0.03683187 0.02806258 0.15654952 49 4.266096 3.224177 TRUE
2 1 0.03493159 0.02377760 0.09846827 61 2.672123 3.218230 FALSE
3 2 0.03390681 0.02252546 0.09251101 46 2.601687 3.212165 FALSECode
ggpairs(a_df_hrv[a_df_hrv$HRF_PAS < .15, ],
columns = some_params,
aes(colour = group, alpha = .25),
progress = FALSE,
lower = list(continuous = wrap('points')))
7.1 Percentage of inflection points of the RR intervals series (PIP)
Code
pip_lmer <- lmer(HRF_PIP ~ group*sex + age + (1|duo), a_df_hrv[a_df_hrv$HRF_PAS < .15, ])
afex_plot(
pip_lmer,
x = 'sex',
trace = 'group',
error_arg = list(width = .15),
dodge = .3,
data_arg = list(
position =
position_jitterdodge(
jitter.width = .1,
dodge.width = 0.3 ## needs to be same as dodge
)),
mapping = c('color'),
point_arg = list(size = 4)
)
Code
anova(pip_lmer)Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
group 0.0185276 0.0185276 1 27.816 3.4093 0.0755 .
sex 0.0017608 0.0017608 1 29.021 0.3240 0.5736
age 0.0052802 0.0052802 1 51.066 0.9716 0.3289
group:sex 0.0084101 0.0084101 1 28.741 1.5476 0.2235
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Code
a_posteriori(pip_lmer)# R2 for Mixed Models
Conditional R2: 0.302
Marginal R2: 0.122
____________________________________________________________
# Intraclass Correlation Coefficient
Adjusted ICC: 0.206
Unadjusted ICC: 0.180
____________________________________________________________
simulated finite sample distribution of LRT. (p-value based on 10000
simulated values)
data:
humanity LRT = 0, p-value = 0.4112
____________________________________________________________
simulated finite sample distribution of LRT. (p-value based on 10000
simulated values)
data:
mindfulness LRT = 1.5348, p-value = 0.08127.2 Inverse of the average length of the acceleration/deceleration segments (IALS)
Code
ials_lmer <- lmer(HRF_IALS ~ group*sex + age + (1|duo), a_df_hrv[a_df_hrv$HRF_PAS < .15, ])
afex_plot(
ials_lmer,
x = 'sex',
trace = 'group',
error_arg = list(width = .15),
dodge = .3,
data_arg = list(
position =
position_jitterdodge(
jitter.width = .1,
dodge.width = 0.3 ## needs to be same as dodge
)),
mapping = c('color'),
point_arg = list(size = 4)
)
Code
anova(ials_lmer)Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
group 0.0191394 0.0191394 1 27.930 3.4130 0.0753 .
sex 0.0013829 0.0013829 1 29.139 0.2466 0.6232
age 0.0056012 0.0056012 1 51.323 0.9988 0.3223
group:sex 0.0086806 0.0086806 1 28.857 1.5479 0.2235
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Code
a_posteriori(ials_lmer)# R2 for Mixed Models
Conditional R2: 0.296
Marginal R2: 0.121
____________________________________________________________
# Intraclass Correlation Coefficient
Adjusted ICC: 0.199
Unadjusted ICC: 0.174
____________________________________________________________
simulated finite sample distribution of LRT. (p-value based on 10000
simulated values)
data:
humanity LRT = 0, p-value = 0.402
____________________________________________________________
simulated finite sample distribution of LRT. (p-value based on 10000
simulated values)
data:
mindfulness LRT = 1.3161, p-value = 0.10027.3 Percentage of short segments (PSS)
Code
pss_lmer <- lmer(HRF_PSS ~ group*sex + age + (1|duo), a_df_hrv[a_df_hrv$HRF_PAS < .15, ])
afex_plot(
pss_lmer,
x = 'sex',
trace = 'group',
error_arg = list(width = .15),
dodge = .3,
data_arg = list(
position =
position_jitterdodge(
jitter.width = .1,
dodge.width = 0.3 ## needs to be same as dodge
)),
mapping = c('color'),
point_arg = list(size = 4)
)
Code
anova(pss_lmer)Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
group 0.067467 0.067467 1 28.322 3.4476 0.07377 .
sex 0.000376 0.000376 1 29.450 0.0192 0.89066
age 0.016426 0.016426 1 48.142 0.8394 0.36414
group:sex 0.053010 0.053010 1 29.192 2.7088 0.11052
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Code
a_posteriori(pss_lmer)# R2 for Mixed Models
Conditional R2: 0.409
Marginal R2: 0.145
____________________________________________________________
# Intraclass Correlation Coefficient
Adjusted ICC: 0.309
Unadjusted ICC: 0.264
____________________________________________________________
simulated finite sample distribution of LRT. (p-value based on 10000
simulated values)
data:
humanity LRT = 0, p-value = 0.4011
____________________________________________________________
simulated finite sample distribution of LRT. (p-value based on 10000
simulated values)
data:
mindfulness LRT = 5.4025, p-value = 0.0087.4 Percentage of NN intervals in alternation segments (PAS)
Code
pas_lmer <- lmer(log10_HRF_PAS ~ group*sex + age + (1|duo), a_df_hrv[a_df_hrv$HRF_PAS < .15, ])
afex_plot(
pas_lmer,
x = 'sex',
trace = 'group',
error_arg = list(width = .15),
dodge = .3,
data_arg = list(
position =
position_jitterdodge(
jitter.width = .1,
dodge.width = 0.3 ## needs to be same as dodge
)),
mapping = c('color'),
point_arg = list(size = 4)
)
Code
anova(pas_lmer)Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
group 0.182901 0.182901 1 28.187 2.3208 0.1388
sex 0.003111 0.003111 1 29.166 0.0395 0.8439
age 0.008832 0.008832 1 43.688 0.1121 0.7394
group:sex 0.135700 0.135700 1 28.947 1.7219 0.1998Code
a_posteriori(pas_lmer)# R2 for Mixed Models
Conditional R2: 0.503
Marginal R2: 0.096
____________________________________________________________
# Intraclass Correlation Coefficient
Adjusted ICC: 0.450
Unadjusted ICC: 0.407
____________________________________________________________boundary (singular) fit: see help('isSingular')Warning in cov2cor(Vr): diag(V) had non-positive or NA entries; the non-finite
result may be dubious____________________________________________________________
simulated finite sample distribution of LRT. (p-value based on 10000
simulated values)
data:
mindfulness LRT = 9.1283, p-value = 0.00178 Heart Rate Asymmetry (HRA)
“Previous studies have indicated that the Poincaré plot is physiologically asymmetric with respect to line of identity (the line on which the current heartbeat interval is identical to the preceding one) [14, 15] and this asymmetry changes in diseases, e.g., arrhythmia [19], heart failure [16], obstructive sleep apnea [20], myocardial infarction [21, 22], postoperative myocardial ischemia [23], and type 1 diabetes [24], etc., suggesting possibly an imbalance in autonomic control under those pathological conditions.” (Yan et al. 2017)
Code
some_params <- c('HRA_GI', 'HRA_SI', 'HRA_AI', 'HRA_PI', 'HRA_SDNNd', 'HRA_SDNNa')
ggpairs(a_df_hrv,
columns = some_params,
aes(colour = group, alpha = .25),
progress = FALSE,
lower = list(continuous = wrap('points')))
Code
rosnerTest(a_df_hrv$HRA_GI)
Results of Outlier Test
-------------------------
Test Method: Rosner's Test for Outliers
Hypothesized Distribution: Normal
Data: a_df_hrv$HRA_GI
Sample Size: 64
Test Statistics: R.1 = 3.728615
R.2 = 3.245314
R.3 = 2.930969
Test Statistic Parameter: k = 3
Alternative Hypothesis: Up to 3 observations are not
from the same Distribution.
Type I Error: 5%
Number of Outliers Detected: 2
i Mean.i SD.i Value Obs.Num R.i+1 lambda.i+1 Outlier
1 0 49.96597 0.1503305 49.40545 10 3.728615 3.224177 TRUE
2 1 49.97487 0.1334757 50.40804 64 3.245314 3.218230 TRUE
3 2 49.96788 0.1224018 50.32664 24 2.930969 3.212165 FALSECode
ggpairs(a_df_hrv[a_df_hrv$HRA_GI > 49.5 & a_df_hrv$HRA_GI < 50.4, ],
columns = some_params,
aes(colour = group, alpha = .25),
progress = FALSE,
lower = list(continuous = wrap('points')))
8.1 Guzik’s Index (GI)
Code
gi_lmer <- lmer(HRA_GI ~ group*sex + age + (1|duo), a_df_hrv[a_df_hrv$HRA_GI > 49.5 & a_df_hrv$HRA_GI < 50.4, ])
afex_plot(
gi_lmer,
x = 'sex',
trace = 'group',
error_arg = list(width = .15),
dodge = .3,
data_arg = list(
position =
position_jitterdodge(
jitter.width = .1,
dodge.width = 0.3 ## needs to be same as dodge
)),
mapping = c('color'),
point_arg = list(size = 4)
)
Code
anova(gi_lmer)Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
group 0.040934 0.040934 1 57 2.6907 0.1064
sex 0.002482 0.002482 1 57 0.1631 0.6878
age 0.000012 0.000012 1 57 0.0008 0.9782
group:sex 0.002058 0.002058 1 57 0.1353 0.7144Code
a_posteriori(gi_lmer)Random effect variances not available. Returned R2 does not account for random effects.
# R2 for Mixed Models
Conditional R2: NA
Marginal R2: 0.0488.2 Slope Index (SI)
Code
si_lmer <- lmer(HRA_SI ~ group*sex + age + (1|duo), a_df_hrv[a_df_hrv$HRA_GI > 49.5 & a_df_hrv$HRA_GI < 50.4, ])
afex_plot(
si_lmer,
x = 'sex',
trace = 'group',
error_arg = list(width = .15),
dodge = .3,
data_arg = list(
position =
position_jitterdodge(
jitter.width = .1,
dodge.width = 0.3 ## needs to be same as dodge
)),
mapping = c('color'),
point_arg = list(size = 4)
)
Code
anova(si_lmer)Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
group 0.096285 0.096285 1 57 4.3516 0.04146 *
sex 0.009907 0.009907 1 57 0.4478 0.50610
age 0.005111 0.005111 1 57 0.2310 0.63264
group:sex 0.028091 0.028091 1 57 1.2696 0.26456
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Code
a_posteriori(si_lmer)Random effect variances not available. Returned R2 does not account for random effects.
# R2 for Mixed Models
Conditional R2: NA
Marginal R2: 0.097
____________________________________________________________NOTE: Results may be misleading due to involvement in interactions$emmeans
group emmean SE df lower.CL upper.CL
humanity 49.96 0.0275 28.46 49.90 50.02
mindfulness 49.88 0.0265 26.52 49.83 49.93
Results are averaged over the levels of: sex
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95
$contrasts
contrast estimate SE df t.ratio p.value
humanity - mindfulness 0.0803 0.0386 28.1 2.081 0.0466
Results are averaged over the levels of: sex
Degrees-of-freedom method: kenward-roger 8.3 Area Index (AI)
Code
ai_lmer <- lmer(HRA_AI ~ group*sex + age + (1|duo), a_df_hrv[a_df_hrv$HRA_GI > 49.5 & a_df_hrv$HRA_GI < 50.4, ])
afex_plot(
ai_lmer,
x = 'sex',
trace = 'group',
error_arg = list(width = .15),
dodge = .3,
data_arg = list(
position =
position_jitterdodge(
jitter.width = .1,
dodge.width = 0.3 ## needs to be same as dodge
)),
mapping = c('color'),
point_arg = list(size = 4)
)
Code
anova(ai_lmer)Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
group 0.008994 0.008994 1 57 0.4397 0.5099
sex 0.045222 0.045222 1 57 2.2110 0.1425
age 0.003755 0.003755 1 57 0.1836 0.6699
group:sex 0.006084 0.006084 1 57 0.2975 0.5876Code
a_posteriori(ai_lmer)Random effect variances not available. Returned R2 does not account for random effects.
# R2 for Mixed Models
Conditional R2: NA
Marginal R2: 0.0598.4 Porta’s Index (PI)
Code
pi_lmer <- lmer(HRA_PI ~ group*sex + age + (1|duo), a_df_hrv[a_df_hrv$HRA_GI > 49.5 & a_df_hrv$HRA_GI < 50.4, ])
afex_plot(
pi_lmer,
x = 'sex',
trace = 'group',
error_arg = list(width = .15),
dodge = .3,
data_arg = list(
position =
position_jitterdodge(
jitter.width = .1,
dodge.width = 0.3 ## needs to be same as dodge
)),
mapping = c('color'),
point_arg = list(size = 4)
)
Code
anova(pi_lmer)Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
group 31.238 31.238 1 27.732 1.2535 0.27250
sex 124.379 124.379 1 27.796 4.9909 0.03371 *
age 1.776 1.776 1 53.397 0.0713 0.79053
group:sex 0.478 0.478 1 27.611 0.0192 0.89088
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Code
a_posteriori(pi_lmer)# R2 for Mixed Models
Conditional R2: 0.186
Marginal R2: 0.107
____________________________________________________________
# Intraclass Correlation Coefficient
Adjusted ICC: 0.089
Unadjusted ICC: 0.079
____________________________________________________________
simulated finite sample distribution of LRT. (p-value based on 10000
simulated values)
data:
humanity LRT = 0.78685, p-value = 0.1477
____________________________________________________________boundary (singular) fit: see help('isSingular')Warning in cov2cor(Vr): diag(V) had non-positive or NA entries; the non-finite
result may be dubious____________________________________________________________NOTE: Results may be misleading due to involvement in interactions$emmeans
sex emmean SE df lower.CL upper.CL
female 51.0 0.991 27.6 48.9 53.0
male 54.1 0.992 27.7 52.1 56.2
Results are averaged over the levels of: group
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95
$contrasts
contrast estimate SE df t.ratio p.value
female - male -3.16 1.42 28.3 -2.228 0.0340
Results are averaged over the levels of: group
Degrees-of-freedom method: kenward-roger 8.5 Total variance of contributions of decelerations (SDNNd)
Code
sdnnd_lmer <- lmer(HRA_SDNNd ~ group*sex + age + (1|duo), a_df_hrv[a_df_hrv$HRA_GI > 49.5 & a_df_hrv$HRA_GI < 50.4, ])
afex_plot(
sdnnd_lmer,
x = 'sex',
trace = 'group',
error_arg = list(width = .15),
dodge = .3,
data_arg = list(
position =
position_jitterdodge(
jitter.width = .1,
dodge.width = 0.3 ## needs to be same as dodge
)),
mapping = c('color'),
point_arg = list(size = 4)
)
Code
anova(sdnnd_lmer)Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
group 44.9 44.9 1 29.602 0.1465 0.704655
sex 3247.4 3247.4 1 29.664 10.5883 0.002841 **
age 305.2 305.2 1 53.903 0.9953 0.322912
group:sex 339.9 339.9 1 29.482 1.1083 0.301004
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Code
a_posteriori(sdnnd_lmer)# R2 for Mixed Models
Conditional R2: 0.279
Marginal R2: 0.212
____________________________________________________________
# Intraclass Correlation Coefficient
Adjusted ICC: 0.085
Unadjusted ICC: 0.067
____________________________________________________________
simulated finite sample distribution of LRT. (p-value based on 10000
simulated values)
data:
humanity LRT = 0.27205, p-value = 0.2269
____________________________________________________________boundary (singular) fit: see help('isSingular')Warning in cov2cor(Vr): diag(V) had non-positive or NA entries; the non-finite
result may be dubious____________________________________________________________NOTE: Results may be misleading due to involvement in interactions$emmeans
sex emmean SE df lower.CL upper.CL
female 46.9 3.46 27.6 39.8 54.0
male 63.0 3.47 27.7 55.9 70.1
Results are averaged over the levels of: group
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95
$contrasts
contrast estimate SE df t.ratio p.value
female - male -16.1 4.95 28.3 -3.246 0.0030
Results are averaged over the levels of: group
Degrees-of-freedom method: kenward-roger 8.6 Total variance of contributions of accelerations (SDNNa)
Code
sdnna_lmer <- lmer(HRA_SDNNa ~ group*sex + age + (1|duo), a_df_hrv[a_df_hrv$HRA_GI > 49.5 & a_df_hrv$HRA_GI < 50.4, ])
afex_plot(
sdnnd_lmer,
x = 'sex',
trace = 'group',
error_arg = list(width = .15),
dodge = .3,
data_arg = list(
position =
position_jitterdodge(
jitter.width = .1,
dodge.width = 0.3 ## needs to be same as dodge
)),
mapping = c('color'),
point_arg = list(size = 4)
)
Code
anova(sdnna_lmer)Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
group 184.6 184.6 1 29.585 0.3873 0.538495
sex 5067.9 5067.9 1 29.658 10.6315 0.002793 **
age 621.0 621.0 1 53.463 1.3027 0.258806
group:sex 253.4 253.4 1 29.460 0.5316 0.471697
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Code
a_posteriori(sdnna_lmer)# R2 for Mixed Models
Conditional R2: 0.296
Marginal R2: 0.214
____________________________________________________________
# Intraclass Correlation Coefficient
Adjusted ICC: 0.104
Unadjusted ICC: 0.082
____________________________________________________________
simulated finite sample distribution of LRT. (p-value based on 10000
simulated values)
data:
humanity LRT = 0.46514, p-value = 0.1915
____________________________________________________________boundary (singular) fit: see help('isSingular')Warning in cov2cor(Vr): diag(V) had non-positive or NA entries; the non-finite
result may be dubious____________________________________________________________NOTE: Results may be misleading due to involvement in interactions$emmeans
sex emmean SE df lower.CL upper.CL
female 50.1 4.4 27.6 41.0 59.1
male 70.5 4.4 27.7 61.5 79.5
Results are averaged over the levels of: group
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95
$contrasts
contrast estimate SE df t.ratio p.value
female - male -20.5 6.29 28.3 -3.253 0.0030
Results are averaged over the levels of: group
Degrees-of-freedom method: kenward-roger 9 Indices of HR Complexity
Code
some_params <- c('HRV_SampEn', 'HRV_ShanEn')
ggpairs(a_df_hrv,
columns = some_params,
aes(colour = group, alpha = .25),
progress = FALSE,
lower = list(continuous = wrap('points')))
9.1 HRV_SampEn
“Sample entropy was designed to provide a less biased and more reliable measure of signal regularity and complexity (92). SampEn values are interpreted and used like ApEn and may be calculated from a much shorter time series of fewer than 200 values” (Shaffer and Ginsberg 2017)
Code
sampen_lmer <- lmer(HRV_SampEn ~ group*sex + age + (1|duo), a_df_hrv)
afex_plot(
sampen_lmer,
x = 'sex',
trace = 'group',
error_arg = list(width = .15),
dodge = .3,
data_arg = list(
position =
position_jitterdodge(
jitter.width = .1,
dodge.width = 0.3 ## needs to be same as dodge
)),
mapping = c('color'),
point_arg = list(size = 4)
)
Code
anova(sampen_lmer)Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
group 0.150995 0.150995 1 28.843 2.6294 0.1158
sex 0.010695 0.010695 1 29.003 0.1862 0.6693
age 0.000962 0.000962 1 55.888 0.0168 0.8975
group:sex 0.046398 0.046398 1 28.766 0.8080 0.3762Code
a_posteriori(sampen_lmer)# R2 for Mixed Models
Conditional R2: 0.115
Marginal R2: 0.061
____________________________________________________________
# Intraclass Correlation Coefficient
Adjusted ICC: 0.058
Unadjusted ICC: 0.054
____________________________________________________________
simulated finite sample distribution of LRT. (p-value based on 10000
simulated values)
data:
humanity LRT = 0.0053143, p-value = 0.379
____________________________________________________________
simulated finite sample distribution of LRT. (p-value based on 10000
simulated values)
data:
mindfulness LRT = 0, p-value = 0.40599.2 HRV_ShanEn
Code
shanen_lmer <- lmer(HRV_ShanEn ~ group*sex + age + (1|duo), a_df_hrv)
afex_plot(
shanen_lmer,
x = 'sex',
trace = 'group',
error_arg = list(width = .15),
dodge = .3,
data_arg = list(
position =
position_jitterdodge(
jitter.width = .1,
dodge.width = 0.3 ## needs to be same as dodge
)),
mapping = c('color'),
point_arg = list(size = 4)
)
Code
anova(shanen_lmer)Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
group 0.11334 0.11334 1 59 0.5261 0.4711138
sex 2.85157 2.85157 1 59 13.2366 0.0005787 ***
age 0.29870 0.29870 1 59 1.3865 0.2437196
group:sex 0.00032 0.00032 1 59 0.0015 0.9694922
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Code
a_posteriori(shanen_lmer)Random effect variances not available. Returned R2 does not account for random effects.
# R2 for Mixed Models
Conditional R2: NA
Marginal R2: 0.216
____________________________________________________________NOTE: Results may be misleading due to involvement in interactions$emmeans
sex emmean SE df lower.CL upper.CL
female 7.77 0.083 28.1 7.60 7.94
male 8.20 0.083 28.1 8.03 8.37
Results are averaged over the levels of: group
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95
$contrasts
contrast estimate SE df t.ratio p.value
female - male -0.431 0.119 28.8 -3.633 0.0011
Results are averaged over the levels of: group
Degrees-of-freedom method: kenward-roger 10 Respiratory Rate Variability (RRV)
“As compared to HRV, BRV can provide short‑term effect on anatomic nervous system meditation, while HRV shows long‑term effects. Improved autonomic function is one of the long‑term effects of meditation in which an increase in parasympathetic activity and decrease in sympathetic dominance are observed. In future works, BRV could also be used for measuring stress.” (Soni and Muniyandi 2019)
Code
some_params <- c('rsp_rate', 'log10_RRV_RMSSD', 'RRV_SD2', 'RRV_SD2SD1', 'RRV_ApEn', 'RRV_SampEn')
ggpairs(a_df_hrv,
columns = some_params,
aes(colour = group, alpha = .25),
progress = FALSE,
lower = list(continuous = wrap('points')))
Code
rosnerTest(a_df_hrv$RRV_SD2)
Results of Outlier Test
-------------------------
Test Method: Rosner's Test for Outliers
Hypothesized Distribution: Normal
Data: a_df_hrv$RRV_SD2
Sample Size: 64
Test Statistics: R.1 = 3.592618
R.2 = 2.895863
R.3 = 2.639238
Test Statistic Parameter: k = 3
Alternative Hypothesis: Up to 3 observations are not
from the same Distribution.
Type I Error: 5%
Number of Outliers Detected: 1
i Mean.i SD.i Value Obs.Num R.i+1 lambda.i+1 Outlier
1 0 2831.039 1146.0884 6948.496 54 3.592618 3.224177 TRUE
2 1 2765.682 1028.0667 5742.823 21 2.895863 3.218230 FALSE
3 2 2717.664 962.6023 5258.201 40 2.639238 3.212165 FALSECode
rosnerTest(a_df_hrv$RRV_SD2SD1)
Results of Outlier Test
-------------------------
Test Method: Rosner's Test for Outliers
Hypothesized Distribution: Normal
Data: a_df_hrv$RRV_SD2SD1
Sample Size: 64
Test Statistics: R.1 = 4.123644
R.2 = 3.075219
R.3 = 3.041633
Test Statistic Parameter: k = 3
Alternative Hypothesis: Up to 3 observations are not
from the same Distribution.
Type I Error: 5%
Number of Outliers Detected: 1
i Mean.i SD.i Value Obs.Num R.i+1 lambda.i+1 Outlier
1 0 1.364447 0.3454570 2.788989 54 4.123644 3.224177 TRUE
2 1 1.341836 0.2966731 2.254170 19 3.075219 3.218230 FALSE
3 2 1.327120 0.2749410 2.163390 23 3.041633 3.212165 FALSECode
ggpairs(a_df_hrv[a_df_hrv$RRV_SD2 < 6948, ],
columns = some_params,
aes(colour = group, alpha = .25),
progress = FALSE,
lower = list(continuous = wrap('points')))
10.1 Respiratory Rate
Code
rsp_rate_lmer <- lmer(rsp_rate ~ group*sex + age + (1|duo), a_df_hrv[a_df_hrv$RRV_SD2 < 6948, ])
afex_plot(
rsp_rate_lmer,
x = 'sex',
trace = 'group',
error_arg = list(width = .15),
dodge = .3,
data_arg = list(
position =
position_jitterdodge(
jitter.width = .1,
dodge.width = 0.3 ## needs to be same as dodge
)),
mapping = c('color'),
point_arg = list(size = 4)
)
Code
anova(rsp_rate_lmer)Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
group 18.3754 18.3754 1 27.285 2.2927 0.1415
sex 0.4891 0.4891 1 27.410 0.0610 0.8067
age 16.0596 16.0596 1 46.555 2.0038 0.1636
group:sex 7.5244 7.5244 1 27.299 0.9388 0.3411Code
a_posteriori(rsp_rate_lmer)# R2 for Mixed Models
Conditional R2: 0.394
Marginal R2: 0.110
____________________________________________________________
# Intraclass Correlation Coefficient
Adjusted ICC: 0.319
Unadjusted ICC: 0.284
____________________________________________________________
simulated finite sample distribution of LRT. (p-value based on 10000
simulated values)
data:
humanity LRT = 1.2335, p-value = 0.1095
____________________________________________________________
simulated finite sample distribution of LRT. (p-value based on 10000
simulated values)
data:
mindfulness LRT = 0.74683, p-value = 0.147310.2 log10_RRV_RMSSD
Code
log10_rrmssd_lmer <- lmer(log10_RRV_RMSSD ~ group*sex + age + (1|duo), a_df_hrv[a_df_hrv$RRV_SD2 < 6948, ])
afex_plot(
log10_rrmssd_lmer,
x = 'sex',
trace = 'group',
error_arg = list(width = .15),
dodge = .3,
data_arg = list(
position =
position_jitterdodge(
jitter.width = .1,
dodge.width = 0.3 ## needs to be same as dodge
)),
mapping = c('color'),
point_arg = list(size = 4)
)
Code
anova(log10_rrmssd_lmer)Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
group 0.031879 0.031879 1 58 0.7406 0.3930
sex 0.004161 0.004161 1 58 0.0967 0.7570
age 0.040890 0.040890 1 58 0.9499 0.3338
group:sex 0.001026 0.001026 1 58 0.0238 0.8779Code
a_posteriori(log10_rrmssd_lmer)Random effect variances not available. Returned R2 does not account for random effects.
# R2 for Mixed Models
Conditional R2: NA
Marginal R2: 0.03610.3 RRV_SD2
Code
rrv_sd2_lmer <- lmer(RRV_SD2 ~ group*sex + age + (1|duo), a_df_hrv[a_df_hrv$RRV_SD2 < 6948, ])
afex_plot(
rrv_sd2_lmer,
x = 'sex',
trace = 'group',
error_arg = list(width = .15),
dodge = .3,
data_arg = list(
position =
position_jitterdodge(
jitter.width = .1,
dodge.width = 0.3 ## needs to be same as dodge
)),
mapping = c('color'),
point_arg = list(size = 4)
)
Code
anova(rrv_sd2_lmer)Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
group 800159 800159 1 58 0.7297 0.3965
sex 2180 2180 1 58 0.0020 0.9646
age 500590 500590 1 58 0.4565 0.5019
group:sex 153268 153268 1 58 0.1398 0.7099Code
a_posteriori(rrv_sd2_lmer)Random effect variances not available. Returned R2 does not account for random effects.
# R2 for Mixed Models
Conditional R2: NA
Marginal R2: 0.02810.4 RRV_SD2SD1
Code
rrv_sd2sd1_lmer <- lmer(RRV_SD2SD1 ~ group*sex + age + (1|duo), a_df_hrv[a_df_hrv$RRV_SD2 < 6948, ])
afex_plot(
rrv_sd2sd1_lmer,
x = 'sex',
trace = 'group',
error_arg = list(width = .15),
dodge = .3,
data_arg = list(
position =
position_jitterdodge(
jitter.width = .1,
dodge.width = 0.3 ## needs to be same as dodge
)),
mapping = c('color'),
point_arg = list(size = 4)
)
Code
anova(rrv_sd2sd1_lmer)Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
group 0.006065 0.006065 1 29.211 0.0867 0.7705
sex 0.000992 0.000992 1 29.346 0.0142 0.9060
age 0.045805 0.045805 1 49.946 0.6551 0.4221
group:sex 0.079769 0.079769 1 29.243 1.1408 0.2942Code
a_posteriori(rrv_sd2sd1_lmer)# R2 for Mixed Models
Conditional R2: 0.266
Marginal R2: 0.029
____________________________________________________________
# Intraclass Correlation Coefficient
Adjusted ICC: 0.245
Unadjusted ICC: 0.238
____________________________________________________________
simulated finite sample distribution of LRT. (p-value based on 10000
simulated values)
data:
humanity LRT = 0.18237, p-value = 0.2622
____________________________________________________________
simulated finite sample distribution of LRT. (p-value based on 10000
simulated values)
data:
mindfulness LRT = 0.77105, p-value = 0.14810.5 RRV_ApEn
Code
RRV_ApEn_lmer <- lmer(RRV_ApEn ~ group*sex + age + (1|duo), a_df_hrv[a_df_hrv$RRV_SD2 < 6948, ])
afex_plot(
RRV_ApEn_lmer,
x = 'sex',
trace = 'group',
error_arg = list(width = .15),
dodge = .3,
data_arg = list(
position =
position_jitterdodge(
jitter.width = .1,
dodge.width = 0.3 ## needs to be same as dodge
)),
mapping = c('color'),
point_arg = list(size = 4)
)
Code
anova(RRV_ApEn_lmer)Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
group 0.018367 0.018367 1 28.916 1.3429 0.2560
sex 0.018127 0.018127 1 29.038 1.3253 0.2590
age 0.016966 0.016966 1 46.757 1.2405 0.2711
group:sex 0.031894 0.031894 1 28.925 2.3319 0.1376Code
a_posteriori(RRV_ApEn_lmer)# R2 for Mixed Models
Conditional R2: 0.426
Marginal R2: 0.123
____________________________________________________________
# Intraclass Correlation Coefficient
Adjusted ICC: 0.346
Unadjusted ICC: 0.303
____________________________________________________________
simulated finite sample distribution of LRT. (p-value based on 10000
simulated values)
data:
humanity LRT = 0.88592, p-value = 0.1331
____________________________________________________________
simulated finite sample distribution of LRT. (p-value based on 10000
simulated values)
data:
mindfulness LRT = 1.8321, p-value = 0.070710.6 RRV_SampEn
Code
RRV_SampEn_lmer <- lmer(RRV_SampEn ~ group*sex + age + (1|duo), a_df_hrv[a_df_hrv$RRV_SD2 < 6948, ])
afex_plot(
RRV_SampEn_lmer,
x = 'sex',
trace = 'group',
error_arg = list(width = .15),
dodge = .3,
data_arg = list(
position =
position_jitterdodge(
jitter.width = .1,
dodge.width = 0.3 ## needs to be same as dodge
)),
mapping = c('color'),
point_arg = list(size = 4)
)
Code
anova(RRV_SampEn_lmer)Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
group 0.05312 0.05312 1 28.192 0.6547 0.4252
sex 0.00276 0.00276 1 28.329 0.0340 0.8551
age 0.33274 0.33274 1 50.157 4.1010 0.0482 *
group:sex 0.03956 0.03956 1 28.229 0.4876 0.4907
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Code
a_posteriori(RRV_SampEn_lmer)# R2 for Mixed Models
Conditional R2: 0.297
Marginal R2: 0.098
____________________________________________________________
# Intraclass Correlation Coefficient
Adjusted ICC: 0.222
Unadjusted ICC: 0.200
____________________________________________________________
simulated finite sample distribution of LRT. (p-value based on 10000
simulated values)
data:
humanity LRT = 2.9644, p-value = 0.0327
____________________________________________________________boundary (singular) fit: see help('isSingular')Warning in cov2cor(Vr): diag(V) had non-positive or NA entries; the non-finite
result may be dubious____________________________________________________________
[1] "Slope for age = 0.0153099700755632"11 Respiratory Sinus Arrhythmia (RSA)
Code
some_params <- c('RSA_P2T_Mean', 'RSA_PorgesBohrer', 'RSA_Gates_Mean')
ggpairs(a_df_hrv,
columns = some_params,
aes(colour = group, alpha = .25),
progress = FALSE,
lower = list(continuous = wrap('points')))
11.1 RSA_P2T_Mean
“The peak to trough method measures the statistical range in ms of the heart period oscillation associated with synchronous respiration. Operationally, subtracting the shortest heart period during inspiration from the longest heart period during expiration produces an estimate of RSA during each breath. The peak-to-trough method makes no statistical assumption or correction (e.g., adaptive filtering) regarding other sources of variance in the heart period time series that may confound, distort, or interact with the metric such as slower periodicities and baseline trend.” (Lewis et al. 2012)
Code
rsa_p2t_lmer <- lmer(RSA_P2T_Mean_log ~ group*sex + age + (1|duo), a_df_hrv)
afex_plot(
rsa_p2t_lmer,
x = 'sex',
trace = 'group',
error_arg = list(width = .15),
dodge = .3,
data_arg = list(
position =
position_jitterdodge(
jitter.width = .1,
dodge.width = 0.3 ## needs to be same as dodge
)),
mapping = c('color'),
point_arg = list(size = 4)
)
Code
anova(rsa_p2t_lmer)Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
group 0.16694 0.16694 1 29.079 0.4832 0.49251
sex 1.30035 1.30035 1 29.231 3.7635 0.06209 .
age 0.00869 0.00869 1 53.816 0.0252 0.87455
group:sex 0.06550 0.06550 1 29.006 0.1896 0.66650
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Code
a_posteriori(rsa_p2t_lmer)# R2 for Mixed Models
Conditional R2: 0.209
Marginal R2: 0.078
____________________________________________________________
# Intraclass Correlation Coefficient
Adjusted ICC: 0.142
Unadjusted ICC: 0.131
____________________________________________________________
simulated finite sample distribution of LRT. (p-value based on 10000
simulated values)
data:
humanity LRT = 0, p-value = 0.4013
____________________________________________________________
simulated finite sample distribution of LRT. (p-value based on 10000
simulated values)
data:
mindfulness LRT = 0.23033, p-value = 0.250911.2 RSA_PorgesBohrer
Some bullshit (Lewis et al. 2012): “A Chebychev type I bandpass filter was applied to the residual series to remove any variance outside the bandwidth of spontaneous respiration (0.12-0.40 Hz).”
Code
rsa_pogboh_lmer <- lmer(RSA_PorgesBohrer ~ group*sex + age + (1|duo), a_df_hrv)
afex_plot(
rsa_pogboh_lmer,
x = 'sex',
trace = 'group',
error_arg = list(width = .15),
dodge = .3,
data_arg = list(
position =
position_jitterdodge(
jitter.width = .1,
dodge.width = 0.3 ## needs to be same as dodge
)),
mapping = c('color'),
point_arg = list(size = 4)
)
Code
anova(rsa_pogboh_lmer)Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
group 0.47421 0.47421 1 59 0.5200 0.4737
sex 0.00155 0.00155 1 59 0.0017 0.9673
age 2.22185 2.22185 1 59 2.4363 0.1239
group:sex 0.13956 0.13956 1 59 0.1530 0.6971Code
a_posteriori(rsa_pogboh_lmer)Random effect variances not available. Returned R2 does not account for random effects.
# R2 for Mixed Models
Conditional R2: NA
Marginal R2: 0.04811.3 RSA_Gates_Mean
Same bullshit (Gates et al. 2015) as Section 11.2: “As with the traditional approach, the sum of the values (i.e., the squared sum of STFT estimates) in the frequency band of .12 to .40 Hz provides the power in the IBI series associated with respiration frequency for each epoch m.”
Code
rsa_gates_lmer <- lmer(RSA_Gates_Mean_log ~ group*sex + age + (1|duo), a_df_hrv)
afex_plot(
rsa_gates_lmer,
x = 'sex',
trace = 'group',
error_arg = list(width = .15),
dodge = .3,
data_arg = list(
position =
position_jitterdodge(
jitter.width = .1,
dodge.width = 0.3 ## needs to be same as dodge
)),
mapping = c('color'),
point_arg = list(size = 4)
)
Code
anova(rsa_gates_lmer)Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
group 0.0000043 0.0000043 1 29.118 0.0023 0.9621458
sex 0.0252777 0.0252777 1 29.281 13.3713 0.0009969 ***
age 0.0000023 0.0000023 1 56.786 0.0012 0.9724516
group:sex 0.0009141 0.0009141 1 29.039 0.4835 0.4923551
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Code
a_posteriori(rsa_gates_lmer)# R2 for Mixed Models
Conditional R2: 0.205
Marginal R2: 0.190
____________________________________________________________
# Intraclass Correlation Coefficient
Adjusted ICC: 0.019
Unadjusted ICC: 0.015
____________________________________________________________
simulated finite sample distribution of LRT. (p-value based on 10000
simulated values)
data:
humanity LRT = 0.13472, p-value = 0.2774
____________________________________________________________boundary (singular) fit: see help('isSingular')Warning in cov2cor(Vr): diag(V) had non-positive or NA entries; the non-finite
result may be dubious____________________________________________________________NOTE: Results may be misleading due to involvement in interactions$emmeans
sex emmean SE df lower.CL upper.CL
female 2.08 0.00792 28.1 2.06 2.09
male 2.12 0.00792 28.1 2.10 2.13
Results are averaged over the levels of: group
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95
$contrasts
contrast estimate SE df t.ratio p.value
female - male -0.0413 0.0113 28.8 -3.652 0.0010
Results are averaged over the levels of: group
Degrees-of-freedom method: kenward-roger 12 Cyclic RSA
Code
some_params <- c('rate_diff', 'rate_diff_t', 'rising_slope', 'decay_slope')
ggpairs(a_df_rsa_cyclic,
columns = some_params,
aes(colour = group, alpha = .25),
progress = FALSE,
lower = list(continuous = wrap('points')))
No outliers:
Code
rosnerTest(a_df_rsa_cyclic$decay_slope)
Results of Outlier Test
-------------------------
Test Method: Rosner's Test for Outliers
Hypothesized Distribution: Normal
Data: a_df_rsa_cyclic$decay_slope
Sample Size: 64
Test Statistics: R.1 = 6.085288
R.2 = 2.570064
R.3 = 2.473754
Test Statistic Parameter: k = 3
Alternative Hypothesis: Up to 3 observations are not
from the same Distribution.
Type I Error: 5%
Number of Outliers Detected: 1
i Mean.i SD.i Value Obs.Num R.i+1 lambda.i+1 Outlier
1 0 5.363964 3.934402 29.30594 49 6.085288 3.224177 TRUE
2 1 4.983933 2.517337 11.45365 55 2.570064 3.218230 FALSE
3 2 4.879583 2.396585 10.80815 59 2.473754 3.212165 FALSECode
ggpairs(a_df_rsa_cyclic[a_df_rsa_cyclic$decay_slope < 29, ],
columns = some_params,
aes(colour = group, alpha = .25),
progress = FALSE,
lower = list(continuous = wrap('points')))
12.1 rate_diff
Code
rate_diff_lmer <- lmer(rate_diff ~ group*sex + age + (1|duo), a_df_rsa_cyclic[a_df_rsa_cyclic$decay_slope < 29, ])
afex_plot(
rate_diff_lmer,
x = 'sex',
trace = 'group',
error_arg = list(width = .15),
dodge = .3,
data_arg = list(
position =
position_jitterdodge(
jitter.width = .1,
dodge.width = 0.3 ## needs to be same as dodge
)),
mapping = c('color'),
point_arg = list(size = 4)
)
Code
anova(rate_diff_lmer)Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
group 61.229 61.229 1 58 1.9806 0.1647
sex 25.001 25.001 1 58 0.8087 0.3722
age 79.405 79.405 1 58 2.5686 0.1144
group:sex 19.428 19.428 1 58 0.6285 0.4312Code
a_posteriori(rate_diff_lmer)Random effect variances not available. Returned R2 does not account for random effects.
# R2 for Mixed Models
Conditional R2: NA
Marginal R2: 0.08712.2 rate_diff_t
Code
rate_diff_t_lmer <- lmer(rate_diff_t ~ group*sex + age + (1|duo), a_df_rsa_cyclic[a_df_rsa_cyclic$decay_slope < 29, ])
afex_plot(
rate_diff_t_lmer,
x = 'sex',
trace = 'group',
error_arg = list(width = .15),
dodge = .3,
data_arg = list(
position =
position_jitterdodge(
jitter.width = .1,
dodge.width = 0.3 ## needs to be same as dodge
)),
mapping = c('color'),
point_arg = list(size = 4)
)
Code
anova(rate_diff_t_lmer)Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
group 3.253 3.253 1 58 0.0902 0.7650
sex 248.245 248.245 1 58 6.8830 0.0111 *
age 62.438 62.438 1 58 1.7312 0.1934
group:sex 100.196 100.196 1 58 2.7781 0.1010
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Code
a_posteriori(rate_diff_t_lmer)Random effect variances not available. Returned R2 does not account for random effects.
# R2 for Mixed Models
Conditional R2: NA
Marginal R2: 0.195
____________________________________________________________NOTE: Results may be misleading due to involvement in interactions$emmeans
sex emmean SE df lower.CL upper.CL
female 25.7 1.08 27.8 23.5 27.9
male 21.6 1.10 28.7 19.4 23.9
Results are averaged over the levels of: group
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95
$contrasts
contrast estimate SE df t.ratio p.value
female - male 4.11 1.57 29.3 2.616 0.0139
Results are averaged over the levels of: group
Degrees-of-freedom method: kenward-roger 12.3 rising_slope
Code
rising_slope_lmer <- lmer(rising_slope ~ group*sex + age + (1|duo), a_df_rsa_cyclic[a_df_rsa_cyclic$decay_slope < 29, ])
afex_plot(
rising_slope_lmer,
x = 'sex',
trace = 'group',
error_arg = list(width = .15),
dodge = .3,
data_arg = list(
position =
position_jitterdodge(
jitter.width = .1,
dodge.width = 0.3 ## needs to be same as dodge
)),
mapping = c('color'),
point_arg = list(size = 4)
)
Code
anova(rising_slope_lmer)Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
group 0.0317 0.0317 1 58 0.0230 0.87990
sex 1.3272 1.3272 1 58 0.9641 0.33025
age 9.1393 9.1393 1 58 6.6387 0.01255 *
group:sex 0.0004 0.0004 1 58 0.0003 0.98627
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Code
a_posteriori(rising_slope_lmer)Random effect variances not available. Returned R2 does not account for random effects.
# R2 for Mixed Models
Conditional R2: NA
Marginal R2: 0.108
____________________________________________________________
[1] "Slope for age = -0.0791753272376088"12.4 decay_slope
Code
decay_slope_lmer <- lmer(decay_slope ~ group*sex + age + (1|duo), a_df_rsa_cyclic[a_df_rsa_cyclic$decay_slope < 29, ])
afex_plot(
decay_slope_lmer,
x = 'sex',
trace = 'group',
error_arg = list(width = .15),
dodge = .3,
data_arg = list(
position =
position_jitterdodge(
jitter.width = .1,
dodge.width = 0.3 ## needs to be same as dodge
)),
mapping = c('color'),
point_arg = list(size = 4)
)
Code
anova(decay_slope_lmer)Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
group 5.2813 5.2813 1 58 0.8729 0.35404
sex 0.9363 0.9363 1 58 0.1547 0.69548
age 29.5941 29.5941 1 58 4.8911 0.03095 *
group:sex 0.6606 0.6606 1 58 0.1092 0.74226
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Code
a_posteriori(decay_slope_lmer)Random effect variances not available. Returned R2 does not account for random effects.
# R2 for Mixed Models
Conditional R2: NA
Marginal R2: 0.101
____________________________________________________________
[1] "Slope for age = -0.142473895327648"13 Clumpiness RSA
Clumpiness (Zhang, Bradlow, and Small 2014) is just Shannon entropy with some makeup.
Code
some_params <- c('inspi_hp_clumpi', 'expi_hp_clumpi', 'inspi_entropy', 'expi_entropy',
'davies_bouldin_insp', 'davies_bouldin_exp', 'wasserstein_insp', 'wasserstein_exp')
ggpairs(a_df_clumpiness,
columns = some_params,
aes(colour = group, alpha = .25),
progress = FALSE,
lower = list(continuous = wrap('points')))
No outliers:
Code
rosnerTest(a_df_clumpiness$inspi_hp_clumpi , k = 5)
Results of Outlier Test
-------------------------
Test Method: Rosner's Test for Outliers
Hypothesized Distribution: Normal
Data: a_df_clumpiness$inspi_hp_clumpi
Sample Size: 64
Test Statistics: R.1 = 3.403150
R.2 = 3.506338
R.3 = 3.574851
R.4 = 3.563527
R.5 = 2.436983
Test Statistic Parameter: k = 5
Alternative Hypothesis: Up to 5 observations are not
from the same Distribution.
Type I Error: 5%
Number of Outliers Detected: 4
i Mean.i SD.i Value Obs.Num R.i+1 lambda.i+1 Outlier
1 0 0.06339484 0.02352192 0.14344344 32 3.403150 3.224177 TRUE
2 1 0.06212422 0.02138253 0.13709860 33 3.506338 3.218230 TRUE
3 2 0.06091496 0.01926322 0.12977809 23 3.574851 3.212165 TRUE
4 3 0.05978606 0.01723151 0.12119100 24 3.563527 3.205977 TRUE
5 4 0.05876264 0.01539430 0.09627828 31 2.436983 3.199662 FALSECode
rosnerTest(a_df_clumpiness$wasserstein_insp, k = 5)
Results of Outlier Test
-------------------------
Test Method: Rosner's Test for Outliers
Hypothesized Distribution: Normal
Data: a_df_clumpiness$wasserstein_insp
Sample Size: 64
Test Statistics: R.1 = 3.299862
R.2 = 3.242995
R.3 = 3.522818
R.4 = 2.073377
R.5 = 2.079927
Test Statistic Parameter: k = 5
Alternative Hypothesis: Up to 5 observations are not
from the same Distribution.
Type I Error: 5%
Number of Outliers Detected: 3
i Mean.i SD.i Value Obs.Num R.i+1 lambda.i+1 Outlier
1 0 7.515140 3.206706 18.09683 33 3.299862 3.224177 TRUE
2 1 7.347177 2.934987 16.86532 24 3.242995 3.218230 TRUE
3 2 7.193659 2.691885 16.67668 58 3.522818 3.212165 TRUE
4 3 7.038199 2.417364 12.05031 63 2.073377 3.205977 FALSE
5 4 6.954664 2.347299 11.83687 17 2.079927 3.199662 FALSECode
ggpairs(a_df_clumpiness[a_df_clumpiness$inspi_hp_clumpi < .12 & a_df_clumpiness$wasserstein_insp < 16, ],
columns = some_params,
aes(colour = group, alpha = .25),
progress = FALSE,
lower = list(continuous = wrap('points')))
13.1 inspi_hp_clumpi
Code
inspi_hp_clumpi_lmer <- lmer(inspi_hp_clumpi ~ group*sex + age + (1|duo), a_df_clumpiness[a_df_clumpiness$inspi_hp_clumpi < .12 & a_df_clumpiness$wasserstein_insp < 16, ])
afex_plot(
inspi_hp_clumpi_lmer,
x = 'sex',
trace = 'group',
error_arg = list(width = .15),
dodge = .3,
data_arg = list(
position =
position_jitterdodge(
jitter.width = .1,
dodge.width = 0.3 ## needs to be same as dodge
)),
mapping = c('color'),
point_arg = list(size = 4)
)
Code
anova(inspi_hp_clumpi_lmer)Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
group 0.00007366 0.00007366 1 54 0.3275 0.56949
sex 0.00081638 0.00081638 1 54 3.6298 0.06208 .
age 0.00011749 0.00011749 1 54 0.5224 0.47295
group:sex 0.00000778 0.00000778 1 54 0.0346 0.85311
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Code
a_posteriori(inspi_hp_clumpi_lmer)Random effect variances not available. Returned R2 does not account for random effects.
# R2 for Mixed Models
Conditional R2: NA
Marginal R2: 0.06913.2 expi_hp_clumpi
Code
expi_hp_clumpi_lmer <- lmer(expi_hp_clumpi ~ group*sex + age + (1|duo), a_df_clumpiness[a_df_clumpiness$inspi_hp_clumpi < .12 & a_df_clumpiness$wasserstein_insp < 16, ])
afex_plot(
expi_hp_clumpi_lmer,
x = 'sex',
trace = 'group',
error_arg = list(width = .15),
dodge = .3,
data_arg = list(
position =
position_jitterdodge(
jitter.width = .1,
dodge.width = 0.3 ## needs to be same as dodge
)),
mapping = c('color'),
point_arg = list(size = 4)
)
Code
anova(expi_hp_clumpi_lmer)Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
group 4.5969e-05 4.5969e-05 1 27.901 1.1685 0.28898
sex 1.1920e-04 1.1920e-04 1 28.422 3.0298 0.09257 .
age 6.3000e-08 6.3000e-08 1 48.779 0.0016 0.96814
group:sex 2.0784e-05 2.0784e-05 1 27.520 0.5283 0.47347
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Code
a_posteriori(expi_hp_clumpi_lmer)# R2 for Mixed Models
Conditional R2: 0.231
Marginal R2: 0.086
____________________________________________________________
# Intraclass Correlation Coefficient
Adjusted ICC: 0.159
Unadjusted ICC: 0.145
____________________________________________________________boundary (singular) fit: see help('isSingular')Warning in cov2cor(Vr): diag(V) had non-positive or NA entries; the non-finite
result may be dubious____________________________________________________________
simulated finite sample distribution of LRT. (p-value based on 10000
simulated values)
data:
mindfulness LRT = 1.6908, p-value = 0.075913.3 inspi_entropy
Code
inspi_entropy_lmer <- lmer(inspi_entropy ~ group*sex + age + (1|duo), a_df_clumpiness[a_df_clumpiness$inspi_hp_clumpi < .12 & a_df_clumpiness$wasserstein_insp < 16, ])
afex_plot(
inspi_entropy_lmer,
x = 'sex',
trace = 'group',
error_arg = list(width = .15),
dodge = .3,
data_arg = list(
position =
position_jitterdodge(
jitter.width = .1,
dodge.width = 0.3 ## needs to be same as dodge
)),
mapping = c('color'),
point_arg = list(size = 4)
)
Code
anova(inspi_entropy_lmer)Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
group 0.0000185 0.0000185 1 54 0.0031 0.9557
sex 0.0179085 0.0179085 1 54 3.0072 0.0886 .
age 0.0035668 0.0035668 1 54 0.5990 0.4424
group:sex 0.0002726 0.0002726 1 54 0.0458 0.8314
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Code
a_posteriori(inspi_entropy_lmer)Random effect variances not available. Returned R2 does not account for random effects.
# R2 for Mixed Models
Conditional R2: NA
Marginal R2: 0.05313.4 expi_entropy
Code
expi_entropy_lmer <- lmer(expi_entropy ~ group*sex + age + (1|duo), a_df_clumpiness[a_df_clumpiness$inspi_hp_clumpi < .12 & a_df_clumpiness$wasserstein_insp < 16, ])
afex_plot(
expi_entropy_lmer,
x = 'sex',
trace = 'group',
error_arg = list(width = .15),
dodge = .3,
data_arg = list(
position =
position_jitterdodge(
jitter.width = .1,
dodge.width = 0.3 ## needs to be same as dodge
)),
mapping = c('color'),
point_arg = list(size = 4)
)
Code
anova(expi_entropy_lmer)Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
group 0.00054207 0.00054207 1 26.711 1.6011 0.21668
sex 0.00104752 0.00104752 1 27.286 3.0940 0.08979 .
age 0.00000436 0.00000436 1 52.184 0.0129 0.91014
group:sex 0.00041042 0.00041042 1 26.297 1.2122 0.28087
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Code
a_posteriori(expi_entropy_lmer)# R2 for Mixed Models
Conditional R2: 0.097
Marginal R2: 0.093
____________________________________________________________
# Intraclass Correlation Coefficient
Adjusted ICC: 0.004
Unadjusted ICC: 0.003
____________________________________________________________boundary (singular) fit: see help('isSingular')Warning in cov2cor(Vr): diag(V) had non-positive or NA entries; the non-finite
result may be dubious____________________________________________________________
simulated finite sample distribution of LRT. (p-value based on 10000
simulated values)
data:
mindfulness LRT = 0.66217, p-value = 0.157613.5 davies_bouldin_insp
One-dimensional clustering with Ckmeans (Wang and Song 2011). Davies-Bouldin Index of cluster similarity (Davies and Bouldin 1979).
Code
davies_bouldin_insp_lmer <- lmer(davies_bouldin_insp ~ group*sex + age + (1|duo), a_df_clumpiness[a_df_clumpiness$inspi_hp_clumpi < .12 & a_df_clumpiness$wasserstein_insp < 16, ])
afex_plot(
davies_bouldin_insp_lmer,
x = 'sex',
trace = 'group',
error_arg = list(width = .15),
dodge = .3,
data_arg = list(
position =
position_jitterdodge(
jitter.width = .1,
dodge.width = 0.3 ## needs to be same as dodge
)),
mapping = c('color'),
point_arg = list(size = 4)
)
Code
anova(davies_bouldin_insp_lmer)Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
group 0.0000051 0.0000051 1 54 0.0030 0.9567
sex 0.0041827 0.0041827 1 54 2.4357 0.1244
age 0.0021193 0.0021193 1 54 1.2341 0.2715
group:sex 0.0002185 0.0002185 1 54 0.1273 0.7227Code
a_posteriori(davies_bouldin_insp_lmer)Random effect variances not available. Returned R2 does not account for random effects.
# R2 for Mixed Models
Conditional R2: NA
Marginal R2: 0.05413.6 davies_bouldin_exp
Code
davies_bouldin_exp_lmer <- lmer(davies_bouldin_exp ~ group*sex + age + (1|duo), a_df_clumpiness[a_df_clumpiness$inspi_hp_clumpi < .12 & a_df_clumpiness$wasserstein_insp < 16, ])
afex_plot(
davies_bouldin_exp_lmer,
x = 'sex',
trace = 'group',
error_arg = list(width = .15),
dodge = .3,
data_arg = list(
position =
position_jitterdodge(
jitter.width = .1,
dodge.width = 0.3 ## needs to be same as dodge
)),
mapping = c('color'),
point_arg = list(size = 4)
)
Code
anova(davies_bouldin_exp_lmer)Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
group 0.00042797 0.00042797 1 54 0.2156 0.6442
sex 0.00295427 0.00295427 1 54 1.4886 0.2277
age 0.00277835 0.00277835 1 54 1.4000 0.2419
group:sex 0.00001181 0.00001181 1 54 0.0060 0.9388Code
a_posteriori(davies_bouldin_exp_lmer)Random effect variances not available. Returned R2 does not account for random effects.
# R2 for Mixed Models
Conditional R2: NA
Marginal R2: 0.04113.7 wasserstein_insp
Wasserstein transport distance (Panaretos and Zemel 2019).
Code
wasserstein_insp_lmer <- lmer(wasserstein_insp ~ group*sex + age + (1|duo), a_df_clumpiness[a_df_clumpiness$inspi_hp_clumpi < .12 & a_df_clumpiness$wasserstein_insp < 16, ])
afex_plot(
wasserstein_insp_lmer,
x = 'sex',
trace = 'group',
error_arg = list(width = .15),
dodge = .3,
data_arg = list(
position =
position_jitterdodge(
jitter.width = .1,
dodge.width = 0.3 ## needs to be same as dodge
)),
mapping = c('color'),
point_arg = list(size = 4)
)
Code
anova(wasserstein_insp_lmer)Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
group 1.39884 1.39884 1 27.465 0.2415 0.6270
sex 0.12586 0.12586 1 28.008 0.0217 0.8839
age 0.14733 0.14733 1 50.019 0.0254 0.8739
group:sex 0.62920 0.62920 1 27.071 0.1086 0.7442Code
a_posteriori(wasserstein_insp_lmer)# R2 for Mixed Models
Conditional R2: 0.112
Marginal R2: 0.007
____________________________________________________________
# Intraclass Correlation Coefficient
Adjusted ICC: 0.106
Unadjusted ICC: 0.105
____________________________________________________________boundary (singular) fit: see help('isSingular')Warning in cov2cor(Vr): diag(V) had non-positive or NA entries; the non-finite
result may be dubious____________________________________________________________
simulated finite sample distribution of LRT. (p-value based on 10000
simulated values)
data:
mindfulness LRT = 0.4811, p-value = 0.187613.8 wasserstein_exp
Code
wasserstein_exp_lmer <- lmer(wasserstein_exp ~ group*sex + age + (1|duo), a_df_clumpiness[a_df_clumpiness$inspi_hp_clumpi < .12 & a_df_clumpiness$wasserstein_insp < 16, ])
afex_plot(
wasserstein_exp_lmer,
x = 'sex',
trace = 'group',
error_arg = list(width = .15),
dodge = .3,
data_arg = list(
position =
position_jitterdodge(
jitter.width = .1,
dodge.width = 0.3 ## needs to be same as dodge
)),
mapping = c('color'),
point_arg = list(size = 4)
)
Code
anova(wasserstein_exp_lmer)Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
group 0.00359 0.00359 1 27.951 0.0045 0.9471
sex 1.03624 1.03624 1 28.498 1.2940 0.2648
age 1.47339 1.47339 1 50.494 1.8399 0.1810
group:sex 0.10686 0.10686 1 27.554 0.1334 0.7177Code
a_posteriori(wasserstein_exp_lmer)# R2 for Mixed Models
Conditional R2: 0.134
Marginal R2: 0.046
____________________________________________________________
# Intraclass Correlation Coefficient
Adjusted ICC: 0.092
Unadjusted ICC: 0.087
____________________________________________________________
simulated finite sample distribution of LRT. (p-value based on 10000
simulated values)
data:
humanity LRT = 0.837, p-value = 0.1498
____________________________________________________________boundary (singular) fit: see help('isSingular')Warning in cov2cor(Vr): diag(V) had non-positive or NA entries; the non-finite
result may be dubious14 Angular RSA
Code
some_params <- c('watson_u2', 'log_watson_f', 'log_wallraff_chi', 'rho_peak', 'rho_trough')
ggpairs(a_df_rsa_angular,
columns = some_params,
aes(colour = group, alpha = .25),
progress = FALSE,
lower = list(continuous = wrap('points')))
No outliers:
Code
rosnerTest(a_df_rsa_angular$rho_trough)
Results of Outlier Test
-------------------------
Test Method: Rosner's Test for Outliers
Hypothesized Distribution: Normal
Data: a_df_rsa_angular$rho_trough
Sample Size: 64
Test Statistics: R.1 = 5.023125
R.2 = 2.912373
R.3 = 2.560962
Test Statistic Parameter: k = 3
Alternative Hypothesis: Up to 3 observations are not
from the same Distribution.
Type I Error: 5%
Number of Outliers Detected: 1
i Mean.i SD.i Value Obs.Num R.i+1 lambda.i+1 Outlier
1 0 0.7864551 0.08558162 0.3565679 49 5.023125 3.224177 TRUE
2 1 0.7932787 0.06644050 0.5997792 30 2.912373 3.218230 FALSE
3 2 0.7963997 0.06215305 0.6372281 61 2.560962 3.212165 FALSECode
ggpairs(a_df_rsa_angular[a_df_rsa_angular$rho_trough > .36, ],
columns = some_params,
aes(colour = group, alpha = .25),
progress = FALSE,
lower = list(continuous = wrap('points')))
14.1 watson_u2
Code
watson_u2_lmer <- lmer(watson_u2 ~ group*sex + age + (1|duo), a_df_rsa_angular[a_df_rsa_angular$rho_trough > .36, ])
afex_plot(
watson_u2_lmer,
x = 'sex',
trace = 'group',
error_arg = list(width = .15),
dodge = .3,
data_arg = list(
position =
position_jitterdodge(
jitter.width = .1,
dodge.width = 0.3 ## needs to be same as dodge
)),
mapping = c('color'),
point_arg = list(size = 4)
)
Code
anova(watson_u2_lmer)Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
group 10.1549 10.1549 1 58 2.5573 0.1152
sex 9.9310 9.9310 1 58 2.5009 0.1192
age 1.7724 1.7724 1 58 0.4463 0.5067
group:sex 0.1411 0.1411 1 58 0.0355 0.8512Code
a_posteriori(watson_u2_lmer)Random effect variances not available. Returned R2 does not account for random effects.
# R2 for Mixed Models
Conditional R2: NA
Marginal R2: 0.08314.2 log_watson_f
Code
log_watson_f_lmer <- lmer(log_watson_f ~ group*sex + age + (1|duo), a_df_rsa_angular[a_df_rsa_angular$rho_trough > .36, ])
afex_plot(
log_watson_f_lmer,
x = 'sex',
trace = 'group',
error_arg = list(width = .15),
dodge = .3,
data_arg = list(
position =
position_jitterdodge(
jitter.width = .1,
dodge.width = 0.3 ## needs to be same as dodge
)),
mapping = c('color'),
point_arg = list(size = 4)
)
Code
anova(log_watson_f_lmer)Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
group 0.03092 0.03092 1 58 0.0345 0.8533
sex 1.15269 1.15269 1 58 1.2863 0.2614
age 0.23956 0.23956 1 58 0.2673 0.6071
group:sex 0.00011 0.00011 1 58 0.0001 0.9911Code
a_posteriori(log_watson_f_lmer)Random effect variances not available. Returned R2 does not account for random effects.
# R2 for Mixed Models
Conditional R2: NA
Marginal R2: 0.03114.3 log_wallraff_chi
Code
log_wallraff_chi_lmer <- lmer(log_wallraff_chi ~ group*sex + age + (1|duo), a_df_rsa_angular[a_df_rsa_angular$rho_trough > .36, ])
afex_plot(
log_wallraff_chi_lmer,
x = 'sex',
trace = 'group',
error_arg = list(width = .15),
dodge = .3,
data_arg = list(
position =
position_jitterdodge(
jitter.width = .1,
dodge.width = 0.3 ## needs to be same as dodge
)),
mapping = c('color'),
point_arg = list(size = 4)
)
Code
anova(log_wallraff_chi_lmer)Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
group 3.0577 3.0577 1 58 1.0074 0.31969
sex 8.6897 8.6897 1 58 2.8630 0.09601 .
age 0.2553 0.2553 1 58 0.0841 0.77285
group:sex 0.2504 0.2504 1 58 0.0825 0.77495
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Code
a_posteriori(log_wallraff_chi_lmer)Random effect variances not available. Returned R2 does not account for random effects.
# R2 for Mixed Models
Conditional R2: NA
Marginal R2: 0.07114.4 rho_peak
Code
rho_peak_lmer <- lmer(rho_peak ~ group*sex + age + (1|duo), a_df_rsa_angular[a_df_rsa_angular$rho_trough > .36, ])
afex_plot(
rho_peak_lmer,
x = 'sex',
trace = 'group',
error_arg = list(width = .15),
dodge = .3,
data_arg = list(
position =
position_jitterdodge(
jitter.width = .1,
dodge.width = 0.3 ## needs to be same as dodge
)),
mapping = c('color'),
point_arg = list(size = 4)
)
Code
anova(rho_peak_lmer)Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
group 0.016191 0.016191 1 28.494 1.1922 0.2840
sex 0.004031 0.004031 1 29.735 0.2968 0.5899
age 0.036082 0.036082 1 53.256 2.6568 0.1090
group:sex 0.004926 0.004926 1 29.443 0.3627 0.5516Code
a_posteriori(rho_peak_lmer)# R2 for Mixed Models
Conditional R2: 0.207
Marginal R2: 0.080
____________________________________________________________
# Intraclass Correlation Coefficient
Adjusted ICC: 0.138
Unadjusted ICC: 0.127
____________________________________________________________
simulated finite sample distribution of LRT. (p-value based on 10000
simulated values)
data:
humanity LRT = 0.39345, p-value = 0.203
____________________________________________________________
simulated finite sample distribution of LRT. (p-value based on 10000
simulated values)
data:
mindfulness LRT = 0, p-value = 0.410814.5 rho_trough
Code
rho_trough_lmer <- lmer(rho_trough ~ group*sex + age + (1|duo), a_df_rsa_angular[a_df_rsa_angular$rho_trough > .36, ])
afex_plot(
rho_trough_lmer,
x = 'sex',
trace = 'group',
error_arg = list(width = .15),
dodge = .3,
data_arg = list(
position =
position_jitterdodge(
jitter.width = .1,
dodge.width = 0.3 ## needs to be same as dodge
)),
mapping = c('color'),
point_arg = list(size = 4)
)
Code
anova(rho_trough_lmer)Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
group 0.0007723 0.0007723 1 27.350 0.1701 0.6833
sex 0.0008334 0.0008334 1 28.626 0.1835 0.6716
age 0.0019812 0.0019812 1 56.179 0.4363 0.5116
group:sex 0.0032072 0.0032072 1 28.319 0.7063 0.4077Code
a_posteriori(rho_trough_lmer)# R2 for Mixed Models
Conditional R2: 0.036
Marginal R2: 0.030
____________________________________________________________
# Intraclass Correlation Coefficient
Adjusted ICC: 0.006
Unadjusted ICC: 0.006
____________________________________________________________boundary (singular) fit: see help('isSingular')Warning in cov2cor(Vr): diag(V) had non-positive or NA entries; the non-finite
result may be dubious____________________________________________________________
simulated finite sample distribution of LRT. (p-value based on 10000
simulated values)
data:
mindfulness LRT = 0.024295, p-value = 0.344515 Continuous Coherence RSA
Code
some_params <- c('ccoh_ave_lfhf', 'ccoh_ave_rsp')
ggpairs(a_df_ccoh,
columns = some_params,
aes(colour = group, alpha = .25),
progress = FALSE,
lower = list(continuous = wrap('points')))
15.1 ccoh_ave_lfhf
Code
ccoh_ave_lfhf_lmer <- lmer(ccoh_ave_lfhf ~ group*sex + age + (1|duo), a_df_ccoh)
afex_plot(
ccoh_ave_lfhf_lmer,
x = 'sex',
trace = 'group',
error_arg = list(width = .15),
dodge = .3,
data_arg = list(
position =
position_jitterdodge(
jitter.width = .1,
dodge.width = 0.3 ## needs to be same as dodge
)),
mapping = c('color'),
point_arg = list(size = 4)
)
Code
anova(ccoh_ave_lfhf_lmer)Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
group 0.032436 0.032436 1 59 4.9566 0.02982 *
sex 0.035098 0.035098 1 59 5.3634 0.02406 *
age 0.000497 0.000497 1 59 0.0760 0.78379
group:sex 0.005137 0.005137 1 59 0.7850 0.37921
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Code
a_posteriori(ccoh_ave_lfhf_lmer)Random effect variances not available. Returned R2 does not account for random effects.
# R2 for Mixed Models
Conditional R2: NA
Marginal R2: 0.155
____________________________________________________________NOTE: Results may be misleading due to involvement in interactions$emmeans
group emmean SE df lower.CL upper.CL
humanity 0.570 0.0144 28 0.540 0.599
mindfulness 0.524 0.0144 28 0.494 0.553
Results are averaged over the levels of: sex
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95
$contrasts
contrast estimate SE df t.ratio p.value
humanity - mindfulness 0.0459 0.0206 28.7 2.224 0.0342
Results are averaged over the levels of: sex
Degrees-of-freedom method: kenward-roger
____________________________________________________________NOTE: Results may be misleading due to involvement in interactions$emmeans
sex emmean SE df lower.CL upper.CL
female 0.571 0.0145 28.1 0.541 0.600
male 0.523 0.0145 28.1 0.493 0.552
Results are averaged over the levels of: group
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95
$contrasts
contrast estimate SE df t.ratio p.value
female - male 0.0478 0.0207 28.8 2.313 0.0281
Results are averaged over the levels of: group
Degrees-of-freedom method: kenward-roger 15.2 ccoh_ave_rsp
Code
ccoh_ave_rsp_lmer <- lmer(ccoh_ave_rsp ~ group*sex + age + (1|duo), a_df_ccoh)
afex_plot(
ccoh_ave_rsp_lmer,
x = 'sex',
trace = 'group',
error_arg = list(width = .15),
dodge = .3,
data_arg = list(
position =
position_jitterdodge(
jitter.width = .1,
dodge.width = 0.3 ## needs to be same as dodge
)),
mapping = c('color'),
point_arg = list(size = 4)
)
Code
anova(ccoh_ave_rsp_lmer)Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
group 0.0269503 0.0269503 1 28.272 3.7989 0.06127 .
sex 0.0246976 0.0246976 1 28.431 3.4814 0.07241 .
age 0.0000511 0.0000511 1 55.468 0.0072 0.93266
group:sex 0.0000192 0.0000192 1 28.196 0.0027 0.95892
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Code
a_posteriori(ccoh_ave_rsp_lmer)# R2 for Mixed Models
Conditional R2: 0.176
Marginal R2: 0.114
____________________________________________________________
# Intraclass Correlation Coefficient
Adjusted ICC: 0.070
Unadjusted ICC: 0.062
____________________________________________________________
simulated finite sample distribution of LRT. (p-value based on 10000
simulated values)
data:
humanity LRT = 0.39419, p-value = 0.2077
____________________________________________________________
simulated finite sample distribution of LRT. (p-value based on 10000
simulated values)
data:
mindfulness LRT = 0, p-value = 0.413216 References
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