Code
cat('\014') # clean terminalCode
rm(list = ls()) # clean workspace
library(tidyverse)
library(afex)
library(emmeans)
library(GGally)
library(GGally)
library(easystats)ERPs Pain Empathy 2024, MoBI device, version 2
Recordings with EMOTIV EPOC Flex Gel, 32 channels
Álvaro Rivera-Rei 
Code
my_dodge <- .3
my_jitter <- .2
theme_set(theme_minimal())
a_posteriori_aov_ez <- function(aov_ez_obj, sig_level = .05) {
factors <- as.list(rownames(aov_ez_obj$anova_table))
for (j in 1:length(factors)) {
if (grepl(':', factors[[j]])) {
factors[[j]] <- unlist(strsplit(factors[[j]], ':'))
}
}
p_values <- aov_ez_obj$anova_table$`Pr(>F)`
for (i in 1:length(p_values)) {
if (p_values[i] <= sig_level) {
cat(rep('_', 60), '\n', sep = '')
print(emmeans(aov_ez_obj, factors[[i]], contr = 'pairwise'))
}
}
}
a_posteriori_lmer <- function(lmer_obj, sig_level = .05) {
anova_lmer <- anova(lmer_obj)
factors <- as.list(row.names(anova_lmer))
for (j in 1:length(factors)) {
if (grepl(':', factors[[j]])) {
factors[[j]] <- unlist(strsplit(factors[[j]], ':'))
}
}
p_values <- anova_lmer$`Pr(>F)`
for (i in 1:length(p_values)) {
if (p_values[i] <= sig_level) {
cat(rep('_', 60), '\n', sep = '')
print(emmeans(lmer_obj, factors[[i]], contr = 'pairwise'))
}
}
}
a_posteriori_glmer <- function(glmer_obj, sig_level = .05) {
anova_glmer <- car::Anova(glmer_obj)
factors <- as.list(row.names(anova_glmer))
for (j in 1:length(factors)) {
if (grepl(':', factors[[j]])) {
factors[[j]] <- unlist(strsplit(factors[[j]], ':'))
}
}
p_values <- anova_glmer$`Pr(>Chisq)`
print(anova_glmer)
for (i in 1:length(p_values)) {
if (p_values[i] <= sig_level) {
cat(rep('_', 60), '\n', sep = '')
print(emmeans(glmer_obj, factors[[i]], contr = 'pairwise'))
}
}
}
xclude <- c(15, 22, 24, 36, 44)Code
csv_answers <- list.files(path = '../csv', pattern = 'pev2_', full.names = TRUE)
answers_df <- vroom::vroom(csv_answers, col_types = c(Sex = 'c'), show_col_types = FALSE) |>
filter(Block > 0) |>
mutate(Block = factor(Block)) |>
filter(!is.na(Question)) |>
mutate(file_info = str_extract(Subject, '.+?(?=_202)')) |>
mutate(Sex = if_else(Sex == 'f', 'female', 'male')) |>
separate(file_info, c('Version', 'ID', 'Group', 'City'), sep = '_', remove = FALSE) |>
mutate(Version = tolower(Version),
Group = tolower(Group),
Group = str_replace(Group, 'suv', 'vul'),
Group = if_else(Group == 'vul', 'vulnerable', 'control'),
Group = fct_relevel(Group, 'vulnerable'),
num_id = parse_number(ID),
Question = recode(Question, 'unpleasantness' = 'displeasing', 'pain' = 'painful'),
Stimulus = case_match(Valence, 'EASE' ~ 'no_pain', 'PAIN' ~ 'pain'),
City = case_match(City, 's' ~ 'stgo', 'v' ~ 'viña')) |>
mutate_if(is.character, as.factor) |>
relocate(Subject, .after = last_col()) |>
filter(!(num_id %in% xclude))
write.csv(answers_df, 'data/painEmpathy_2023_answers_clean_version_2.csv', row.names = FALSE)
erp_averages <- list.files(path = './data/ave_volt_2_versions', pattern = 'pev2_', full.names = TRUE)
painEmpathy_data <- vroom::vroom(erp_averages, show_col_types = FALSE) |>
separate(ERPset, c('Version', 'ID', 'Group', 'City'), sep = '_', remove = FALSE) |>
rename(Component = mlabel,
Amplitude = value,
Stimulus = binlabel,
Electrode = chlabel) |>
mutate(Group = if_else(Group == 'vul', 'vulnerable', 'control'),
Group = fct_relevel(Group, 'vulnerable'),
City = case_match(City, 's' ~ 'stgo', 'v' ~ 'viña'),
worklat = gsub('.0', '', worklat, fixed = TRUE),
num_id = parse_number(ID),
chindex = factor(chindex),
bini = factor(bini)) |>
left_join(unique(answers_df[c('num_id', 'Sex')]), by = 'num_id') |>
mutate_if(is.character, as.factor) |>
mutate(Component = factor(Component, levels = c('N1', 'N2', 'P1', 'P3', 'LPP', 'P3a', 'P3b')),
Electrode = factor(Electrode, levels = c('FC1', 'Fz', 'FC2',
'CP1', 'Cz', 'CP2',
'O1' , 'Oz', 'O2')),
Lateral = case_match(Electrode,
c('FC1', 'CP1', 'O1') ~ 'left',
c('Fz' , 'Cz' , 'Oz') ~ 'center',
c('FC2', 'CP2', 'O2') ~ 'right')) |>
filter(!(num_id %in% xclude))
write.csv(painEmpathy_data, file.path('data/painEmpathy_2024_erp_clean_version_2.csv'), row.names = FALSE)
connectivity_file <- 'data/median_connectivity_2_versions.csv'
label_a <- 'occipital'
label_b <- 'parietal'
connectivity_data <- read_csv(connectivity_file, show_col_types = FALSE) |>
separate(ERPset, c('ID', 'Group', 'City'), sep = '_', remove = FALSE) |>
mutate(Group = if_else(Group == 'vul', 'vulnerable', 'control'),
Group = fct_relevel(Group, 'vulnerable'),
Version = str_sub(image, -2, -1),
Stimulus = str_sub(image, 1, -4),
Stimulus = fct_relevel(Stimulus, 'pain'),
City = case_match(City, 's' ~ 'stgo', 'v' ~ 'viña'),log_median_connectivity = log(median_connectivity),
num_id = parse_number(ID),
region_b = fct_relevel(region_b, paste0(label_b, '_ipsi'))
) |>
filter(Version == 'v1') |>
left_join(unique(answers_df[c('num_id', 'Sex')]), by = 'num_id') |>
mutate_if(is.character, as.factor) |>
filter(!(num_id %in% xclude))
write_csv(connectivity_data, 'data/painEmpathy_2024_connectivity_clean_version_2.csv')
n1_data <- subset(painEmpathy_data, Component == 'N1',)
n2_data <- subset(painEmpathy_data, Component == 'N2',)
p1_data <- subset(painEmpathy_data, Component == 'P1',)
p3_data <- subset(painEmpathy_data, Component == 'P3',)
lpp_data <- subset(painEmpathy_data, Component == 'LPP',)
p3a_data <- subset(painEmpathy_data, Component == 'P3a',)
p3b_data <- subset(painEmpathy_data, Component == 'P3b',)
component_data_wide_stimulus <- painEmpathy_data[c('num_id', 'Stimulus', 'Lateral', 'Component', 'Amplitude')] |>
mutate(Stimulus = fct_relevel(Stimulus, 'pain')) |>
pivot_wider(names_from = Component, values_from = Amplitude)Answers
Code
addmargins(xtabs(~ Sex + Group, data = unique(answers_df[c('num_id', 'Sex', 'Group')]))) Group
Sex vulnerable control Sum
female 25 25 50
male 25 25 50
Sum 50 50 100Code
cat(rep('_', 60), '\n', sep = '')____________________________________________________________Code
addmargins(xtabs(~ Sex + Group + City, data = unique(answers_df[c('num_id', 'Sex', 'Group', 'City')])), c(1, 2)), , City = stgo
Group
Sex vulnerable control Sum
female 25 18 43
male 24 20 44
Sum 49 38 87
, , City = viña
Group
Sex vulnerable control Sum
female 0 7 7
male 1 5 6
Sum 1 12 13Code
summary(answers_df) Sex Block trialN Image Valence
female:2557 1:2639 Min. : 1.00 54.1.jpg: 48 EASE:2533
male :2556 2:2474 1st Qu.:16.00 9.1.jpg : 48 PAIN:2580
Median :33.00 2.2.jpg : 47
Mean :32.55 43.2.jpg: 47
3rd Qu.:49.00 1.2.jpg : 46
Max. :64.00 11.1.jpg: 46
(Other) :4831
Answer Question rT Session
Min. : 1.00 displeasing:2554 Min. : 2 Min. :1
1st Qu.:14.00 painful :2559 1st Qu.: 3157 1st Qu.:1
Median :49.00 Median : 4029 Median :1
Mean :44.56 Mean : 4468 Mean :1
3rd Qu.:72.00 3rd Qu.: 5145 3rd Qu.:1
Max. :97.00 Max. :68437 Max. :1
file_info Version ID Group
pev2_s006_vul_s: 61 pev2:5113 s006 : 61 vulnerable:2556
pev2_s040_vul_s: 61 s040 : 61 control :2557
pev2_s126_ctl_s: 61 s126 : 61
pev2_s134_ctl_s: 61 s134 : 61
pev2_s037_vul_s: 60 s037 : 60
pev2_s132_ctl_s: 60 s132 : 60
(Other) :4749 (Other):4749
City num_id Stimulus
stgo:4476 Min. : 1.00 no_pain:2533
viña: 637 1st Qu.: 29.00 pain :2580
Median :100.00
Mean : 76.72
3rd Qu.:126.00
Max. :150.00
Subject
pev2_s006_vul_s_2024-10-25_18-31-33: 61
pev2_s040_vul_s_2024-11-29_14-50-53: 61
pev2_s126_ctl_s_2025-01-17_21-06-11: 61
pev2_s134_ctl_s_2025-01-22_15-26-40: 61
pev2_s037_vul_s_2024-11-26_17-18-05: 60
pev2_s132_ctl_s_2025-01-22_14-03-54: 60
(Other) :4749 Pain rating:
Code
pain_rating_lmer <- lmer(Answer ~ Group*Sex*Stimulus + (Stimulus|num_id) + (1|Image), subset(answers_df, Question == 'painful'))
afex_plot(
pain_rating_lmer,
x = 'Stimulus',
trace = 'Group',
panel = 'Sex',
id = 'num_id',
error_arg = list(width = .15),
dodge = my_dodge,
data_arg = list(
position =
position_jitterdodge(
jitter.width = my_jitter,
jitter.height = 0,
dodge.width = my_dodge ## needs to be same as dodge
)),
mapping = c('color'),
point_arg = list(size = 4)
)
pain_rating_lmer@calllmer(formula = Answer ~ Group * Sex * Stimulus + (Stimulus |
num_id) + (1 | Image), data = subset(answers_df, Question ==
"painful"))
Code
anova(pain_rating_lmer)Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
Group 800 800 1 95.109 2.8567 0.094271 .
Sex 186 186 1 95.184 0.6645 0.417028
Stimulus 104025 104025 1 147.650 371.5779 < 2.2e-16 ***
Group:Sex 1 1 1 94.920 0.0033 0.954238
Group:Stimulus 3185 3185 1 95.237 11.3760 0.001077 **
Sex:Stimulus 55 55 1 95.289 0.1954 0.659433
Group:Sex:Stimulus 78 78 1 95.133 0.2777 0.599425
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Code
interpret(omega_squared(pain_rating_lmer, alternative = 'two.sided'), rules = 'field2013')# Effect Size for ANOVA (Type III)
Parameter | Omega2 (partial) | 95% CI | Interpretation
---------------------------------------------------------------------
Group | 0.02 | [0.00, 0.10] | small
Sex | 0.00 | [0.00, 0.00] | very small
Stimulus | 0.71 | [0.64, 0.77] | large
Group:Sex | 0.00 | [0.00, 0.00] | very small
Group:Stimulus | 0.10 | [0.01, 0.22] | medium
Sex:Stimulus | 0.00 | [0.00, 0.00] | very small
Group:Sex:Stimulus | 0.00 | [0.00, 0.00] | very small
- Interpretation rule: field2013Code
a_posteriori_lmer(pain_rating_lmer)____________________________________________________________NOTE: Results may be misleading due to involvement in interactions$emmeans
Stimulus emmean SE df lower.CL upper.CL
no_pain 23.3 1.76 149 19.8 26.7
pain 71.1 1.45 168 68.2 73.9
Results are averaged over the levels of: Group, Sex
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95
$contrasts
contrast estimate SE df t.ratio p.value
no_pain - pain -47.8 2.48 149 -19.276 <.0001
Results are averaged over the levels of: Group, Sex
Degrees-of-freedom method: kenward-roger
____________________________________________________________NOTE: Results may be misleading due to involvement in interactions$emmeans
Group Stimulus emmean SE df lower.CL upper.CL
vulnerable no_pain 28.3 2.33 125 23.7 32.9
control no_pain 18.2 2.33 126 13.6 22.9
vulnerable pain 68.8 1.86 144 65.2 72.5
control pain 73.3 1.86 142 69.6 77.0
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
vulnerable no_pain - control no_pain 10.06 3.06 95.8 3.291 0.0075
vulnerable no_pain - vulnerable pain -40.53 3.28 125.9 -12.347 <.0001
vulnerable no_pain - control pain -44.99 2.98 223.7 -15.096 <.0001
control no_pain - vulnerable pain -50.59 2.99 225.6 -16.943 <.0001
control no_pain - control pain -55.05 3.28 125.7 -16.772 <.0001
vulnerable pain - control pain -4.45 2.33 95.8 -1.913 0.2295
Results are averaged over the levels of: Sex
Degrees-of-freedom method: kenward-roger
P value adjustment: tukey method for comparing a family of 4 estimates Unpleasantness rating:
Code
unpleasantness_rating_lmer <- lmer(Answer ~ Group*Sex*Stimulus + (Stimulus|num_id) + (1|Image), subset(answers_df, Question == 'displeasing'))
afex_plot(
unpleasantness_rating_lmer,
x = 'Stimulus',
trace = 'Group',
panel = 'Sex',
id = 'num_id',
error_arg = list(width = .15),
dodge = my_dodge,
data_arg = list(
position =
position_jitterdodge(
jitter.width = my_jitter,
jitter.height = 0,
dodge.width = my_dodge ## needs to be same as dodge
)),
mapping = c('color'),
point_arg = list(size = 4)
)
unpleasantness_rating_lmer@calllmer(formula = Answer ~ Group * Sex * Stimulus + (Stimulus |
num_id) + (1 | Image), data = subset(answers_df, Question ==
"displeasing"))
Code
options(width = 120)
anova(unpleasantness_rating_lmer)Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
Group 13 13 1 96.032 0.0459 0.830723
Sex 292 292 1 95.926 1.0236 0.314210
Stimulus 81912 81912 1 141.645 286.6576 < 2.2e-16 ***
Group:Sex 7 7 1 95.958 0.0234 0.878846
Group:Stimulus 2700 2700 1 95.990 9.4498 0.002749 **
Sex:Stimulus 582 582 1 95.788 2.0382 0.156645
Group:Sex:Stimulus 320 320 1 95.855 1.1212 0.292332
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Code
interpret(omega_squared(unpleasantness_rating_lmer, alternative = 'two.sided'), rules = 'field2013')# Effect Size for ANOVA (Type III)
Parameter | Omega2 (partial) | 95% CI | Interpretation
---------------------------------------------------------------------
Group | 0.00 | [0.00, 0.00] | very small
Sex | 2.41e-04 | [0.00, 0.03] | very small
Stimulus | 0.67 | [0.58, 0.73] | large
Group:Sex | 0.00 | [0.00, 0.00] | very small
Group:Stimulus | 0.08 | [0.01, 0.20] | medium
Sex:Stimulus | 0.01 | [0.00, 0.08] | small
Group:Sex:Stimulus | 1.24e-03 | [0.00, 0.05] | very small
- Interpretation rule: field2013Code
a_posteriori_lmer(unpleasantness_rating_lmer)____________________________________________________________NOTE: Results may be misleading due to involvement in interactions$emmeans
Stimulus emmean SE df lower.CL upper.CL
no_pain 22.6 1.65 137 19.3 25.8
pain 60.0 1.98 125 56.0 63.9
Results are averaged over the levels of: Group, Sex
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95
$contrasts
contrast estimate SE df t.ratio p.value
no_pain - pain -37.4 2.21 142 -16.930 <.0001
Results are averaged over the levels of: Group, Sex
Degrees-of-freedom method: kenward-roger
____________________________________________________________NOTE: Results may be misleading due to involvement in interactions$emmeans
Group Stimulus emmean SE df lower.CL upper.CL
vulnerable no_pain 25.9 2.21 118 21.5 30.2
control no_pain 19.3 2.22 119 14.9 23.7
vulnerable pain 57.3 2.70 111 51.9 62.6
control pain 62.7 2.71 111 57.3 68.0
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
vulnerable no_pain - control no_pain 6.56 2.95 96.1 2.221 0.1248
vulnerable no_pain - vulnerable pain -31.40 2.94 121.5 -10.687 <.0001
vulnerable no_pain - control pain -36.79 3.50 196.2 -10.526 <.0001
control no_pain - vulnerable pain -37.95 3.50 196.5 -10.855 <.0001
control no_pain - control pain -43.34 2.94 122.5 -14.721 <.0001
vulnerable pain - control pain -5.39 3.68 95.9 -1.466 0.4622
Results are averaged over the levels of: Sex
Degrees-of-freedom method: kenward-roger
P value adjustment: tukey method for comparing a family of 4 estimates ERP plots
EDF files were pre-processed in Matlab using the EEGLAB 2023.1 toolbox (Delorme and Makeig 2004), the ERPLAB 10.0 toolbox (Lopez-Calderon and Luck 2014), and automated with in-house scripts. EEG data was high-pass filtered at 0.1 Hz with a 12 dB/oct roll-off. A working time window was selected from 2 seconds before the first Stimulus to 2 seconds after the last one. Defective channels were identified by eye inspection and omitted from the preprocessing steps. Artifacts originating from eye blinks or movements, heart beats, muscle contraction, channel noise, or electrical interference were identified and removed by means of independent Component analysis (ICA) and using the ICLabel 1.4 classifier (Pion-Tonachini, Kreutz-Delgado, and Makeig 2019), Components with an score between 0.8 and 1 in the aforementioned artifactual categories were removed. At this point defective channels were spherically interpolated. Data was re-referenced to infinity with the REST 1.2 toolbox (Dong et al. 2017), low-pass filtered at 35 Hz with a 12 dB/oct roll-off, cut in [-200ms 1000ms] epochs around stimuli, and baseline-corrected relative to the mean voltage in the pre-Stimulus time.
Topographic layout:
ERPs General description
Code
options(width = 90)
ggplot(
painEmpathy_data, aes(x = Amplitude, fill = Component, color = Component)) +
geom_histogram(alpha = .4) +
facet_wrap(~Component, ncol = 1) +
theme(strip.text.x = element_blank())
addmargins(xtabs(~ Sex + Group, data = unique(painEmpathy_data[c('num_id', 'Sex', 'Group')]))) Group
Sex vulnerable control Sum
female 25 25 50
male 24 25 49
Sum 49 50 99Code
cat(rep('_', 60), '\n', sep = '')____________________________________________________________Code
addmargins(xtabs(~ Sex + Group + City, data = unique(painEmpathy_data[c('num_id', 'Sex', 'Group', 'City')])), c(1, 2)), , City = stgo
Group
Sex vulnerable control Sum
female 25 18 43
male 24 20 44
Sum 49 38 87
, , City = viña
Group
Sex vulnerable control Sum
female 0 7 7
male 0 5 5
Sum 0 12 12Code
summary(painEmpathy_data) worklat Component Amplitude chindex Electrode bini
[105 135]:594 N1 :594 Min. :-9.1139 16 :792 O1 :792 1:2079
[125 145]:594 N2 :594 1st Qu.:-0.6199 18 :792 Oz :792 2:2079
[240 300]:594 P1 :594 Median : 1.3245 19 :792 O2 :792
[260 300]:594 P3 :594 Mean : 1.3948 2 :396 FC1 :396
[260 360]:594 LPP:594 3rd Qu.: 3.3220 6 :396 Fz :396
[340 390]:594 P3a:594 Max. :12.3915 29 :396 FC2 :396
[550 750]:594 P3b:594 (Other):594 (Other):594
Stimulus ERPset Version ID Group
no_pain:2079 pev2_s001_vul_s: 42 pev2:4158 s001 : 42 vulnerable:2058
pain :2079 pev2_s002_vul_s: 42 s002 : 42 control :2100
pev2_s003_vul_s: 42 s003 : 42
pev2_s004_vul_s: 42 s004 : 42
pev2_s005_vul_s: 42 s005 : 42
pev2_s006_vul_s: 42 s006 : 42
(Other) :3906 (Other):3906
City num_id Sex Lateral
stgo:3654 Min. : 1.00 female:2100 Length:4158
viña: 504 1st Qu.: 28.00 male :2058 Class :character
Median :100.00 Mode :character
Mean : 76.87
3rd Qu.:126.00
Max. :150.00
Code
ggpairs(
component_data_wide_stimulus,
aes(colour = Stimulus, alpha = .5, linewidth = NA),
columns = c('N1', 'N2', 'P1', 'P3a', 'P3b', 'LPP'),
lower = list(continuous = wrap('points', alpha = .4)),
progress = FALSE)
In the front: N1 & N2:
N1, average from [125 145] ms interval
Electrodes FC1, Fz, FC2
Code
painEmpathy_n1_lmer <- lmer(Amplitude ~ Group*Sex*Stimulus + (Stimulus|num_id) + (1|num_id:Electrode), n1_data)
afex_plot(
painEmpathy_n1_lmer,
x = 'Stimulus',
trace = 'Group',
panel = 'Sex',
id = 'num_id',
error_arg = list(width = .25),
dodge = my_dodge,
data_arg = list(
position =
position_jitterdodge(
jitter.width = my_jitter,
jitter.height = 0,
dodge.width = my_dodge ## needs to be same as dodge
)),
mapping = c('color'),
point_arg = list(size = 3)
)
painEmpathy_n1_lmer@calllmer(formula = Amplitude ~ Group * Sex * Stimulus + (Stimulus |
num_id) + (1 | num_id:Electrode), data = n1_data)
Code
options(width = 110)
anova(painEmpathy_n1_lmer)Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
Group 0.05225 0.05225 1 95 0.1362 0.7129
Sex 0.00217 0.00217 1 95 0.0057 0.9402
Stimulus 0.01421 0.01421 1 95 0.0370 0.8478
Group:Sex 0.84661 0.84661 1 95 2.2070 0.1407
Group:Stimulus 0.36135 0.36135 1 95 0.9420 0.3342
Sex:Stimulus 0.96559 0.96559 1 95 2.5172 0.1159
Group:Sex:Stimulus 0.00719 0.00719 1 95 0.0187 0.8914Code
interpret(omega_squared(painEmpathy_n1_lmer, alternative = 'two.sided'), rules = 'field2013')# Effect Size for ANOVA (Type III)
Parameter | Omega2 (partial) | 95% CI | Interpretation
---------------------------------------------------------------------
Group | 0.00 | [0.00, 0.00] | very small
Sex | 0.00 | [0.00, 0.00] | very small
Stimulus | 0.00 | [0.00, 0.00] | very small
Group:Sex | 0.01 | [0.00, 0.09] | small
Group:Stimulus | 0.00 | [0.00, 0.00] | very small
Sex:Stimulus | 0.02 | [0.00, 0.10] | small
Group:Sex:Stimulus | 0.00 | [0.00, 0.00] | very small
- Interpretation rule: field2013Code
a_posteriori_lmer(painEmpathy_n1_lmer)N2, average from [260 300] ms interval
Electrodes FC1, Fz, FC2
Code
painEmpathy_n2_lmer <- lmer(Amplitude ~ Group*Sex*Stimulus + (Stimulus|num_id) + (1|num_id:Electrode), n2_data)
afex_plot(
painEmpathy_n2_lmer,
x = 'Stimulus',
trace = 'Group',
panel = 'Sex',
id = 'num_id',
error_arg = list(width = .25),
dodge = my_dodge,
data_arg = list(
position =
position_jitterdodge(
jitter.width = my_jitter,
jitter.height = 0,
dodge.width = my_dodge ## needs to be same as dodge
)),
mapping = c('color'),
point_arg = list(size = 3)
)
painEmpathy_n2_lmer@calllmer(formula = Amplitude ~ Group * Sex * Stimulus + (Stimulus |
num_id) + (1 | num_id:Electrode), data = n2_data)
Code
options(width = 110)
anova(painEmpathy_n2_lmer)Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
Group 1.98234 1.98234 1 95.002 5.1371 0.02569 *
Sex 0.05990 0.05990 1 95.002 0.1552 0.69448
Stimulus 0.37304 0.37304 1 95.000 0.9667 0.32800
Group:Sex 0.38635 0.38635 1 95.002 1.0012 0.31957
Group:Stimulus 0.20166 0.20166 1 95.000 0.5226 0.47152
Sex:Stimulus 0.26166 0.26166 1 95.000 0.6781 0.41232
Group:Sex:Stimulus 0.50816 0.50816 1 95.000 1.3168 0.25404
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Code
interpret(omega_squared(painEmpathy_n2_lmer, alternative = 'two.sided'), rules = 'field2013')# Effect Size for ANOVA (Type III)
Parameter | Omega2 (partial) | 95% CI | Interpretation
---------------------------------------------------------------------
Group | 0.04 | [0.00, 0.14] | small
Sex | 0.00 | [0.00, 0.00] | very small
Stimulus | 0.00 | [0.00, 0.00] | very small
Group:Sex | 1.22e-05 | [0.00, 0.00] | very small
Group:Stimulus | 0.00 | [0.00, 0.00] | very small
Sex:Stimulus | 0.00 | [0.00, 0.00] | very small
Group:Sex:Stimulus | 3.26e-03 | [0.00, 0.06] | very small
- Interpretation rule: field2013Code
a_posteriori_lmer(painEmpathy_n2_lmer)____________________________________________________________NOTE: Results may be misleading due to involvement in interactions$emmeans
Group emmean SE df lower.CL upper.CL
vulnerable -1.86 0.273 95 -2.40 -1.32
control -2.73 0.271 95 -3.27 -2.19
Results are averaged over the levels of: Sex, Stimulus
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95
$contrasts
contrast estimate SE df t.ratio p.value
vulnerable - control 0.872 0.385 95 2.267 0.0257
Results are averaged over the levels of: Sex, Stimulus
Degrees-of-freedom method: kenward-roger In the back: P1 & P3:
P1, average [105 135] ms interval
Electrodes O1, Oz, O2
Code
painEmpathy_p1_lmer <- lmer(Amplitude ~ Group*Sex*Stimulus + (Stimulus|num_id) + (1|num_id:Electrode), p1_data)
afex_plot(
painEmpathy_p1_lmer,
x = 'Stimulus',
trace = 'Group',
panel = 'Sex',
id = 'num_id',
error_arg = list(width = .25),
dodge = my_dodge,
data_arg = list(
position =
position_jitterdodge(
jitter.width = my_jitter,
jitter.height = 0,
dodge.width = my_dodge ## needs to be same as dodge
)),
mapping = c('color'),
point_arg = list(size = 3)
)
painEmpathy_p1_lmer@calllmer(formula = Amplitude ~ Group * Sex * Stimulus + (Stimulus |
num_id) + (1 | num_id:Electrode), data = p1_data)
Code
options(width = 90)
anova(painEmpathy_p1_lmer)Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
Group 0.00280 0.00280 1 95 0.0138 0.9067
Sex 0.16454 0.16454 1 95 0.8111 0.3701
Stimulus 0.54018 0.54018 1 95 2.6629 0.1060
Group:Sex 0.27908 0.27908 1 95 1.3757 0.2438
Group:Stimulus 0.32676 0.32676 1 95 1.6108 0.2075
Sex:Stimulus 0.27976 0.27976 1 95 1.3791 0.2432
Group:Sex:Stimulus 0.12808 0.12808 1 95 0.6314 0.4288Code
interpret(omega_squared(painEmpathy_p1_lmer, alternative = 'two.sided'), rules = 'field2013')# Effect Size for ANOVA (Type III)
Parameter | Omega2 (partial) | 95% CI | Interpretation
---------------------------------------------------------------------
Group | 0.00 | [0.00, 0.00] | very small
Sex | 0.00 | [0.00, 0.00] | very small
Stimulus | 0.02 | [0.00, 0.10] | small
Group:Sex | 3.86e-03 | [0.00, 0.06] | very small
Group:Stimulus | 6.26e-03 | [0.00, 0.07] | very small
Sex:Stimulus | 3.89e-03 | [0.00, 0.06] | very small
Group:Sex:Stimulus | 0.00 | [0.00, 0.00] | very small
- Interpretation rule: field2013Code
a_posteriori_lmer(painEmpathy_p1_lmer)P3, average from [260 360] ms interval
Electrodes O1, Oz, O2
Code
painEmpathy_p3_lmer <- lmer(Amplitude ~ Group*Sex*Stimulus + (Stimulus|num_id) + (1|num_id:Electrode), p3_data)
afex_plot(
painEmpathy_p3_lmer,
x = 'Stimulus',
trace = 'Group',
panel = 'Sex',
id = 'num_id',
error_arg = list(width = .25),
dodge = my_dodge,
data_arg = list(
position =
position_jitterdodge(
jitter.width = my_jitter,
jitter.height = 0,
dodge.width = my_dodge ## needs to be same as dodge
)),
mapping = c('color'),
point_arg = list(size = 3)
)
painEmpathy_p3_lmer@calllmer(formula = Amplitude ~ Group * Sex * Stimulus + (Stimulus |
num_id) + (1 | num_id:Electrode), data = p3_data)
Code
options(width = 110)
anova(painEmpathy_p3_lmer)Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
Group 0.4366 0.4366 1 95.001 1.7746 0.18600
Sex 0.3242 0.3242 1 95.001 1.3178 0.25387
Stimulus 4.1910 4.1910 1 95.002 17.0361 7.873e-05 ***
Group:Sex 0.0401 0.0401 1 95.001 0.1630 0.68729
Group:Stimulus 0.0161 0.0161 1 95.002 0.0655 0.79851
Sex:Stimulus 1.0651 1.0651 1 95.002 4.3293 0.04015 *
Group:Sex:Stimulus 1.5170 1.5170 1 95.002 6.1664 0.01477 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Code
interpret(omega_squared(painEmpathy_p3_lmer, alternative = 'two.sided'), rules = 'field2013')# Effect Size for ANOVA (Type III)
Parameter | Omega2 (partial) | 95% CI | Interpretation
---------------------------------------------------------------------
Group | 7.92e-03 | [0.00, 0.08] | very small
Sex | 3.27e-03 | [0.00, 0.06] | very small
Stimulus | 0.14 | [0.04, 0.27] | large
Group:Sex | 0.00 | [0.00, 0.00] | very small
Group:Stimulus | 0.00 | [0.00, 0.00] | very small
Sex:Stimulus | 0.03 | [0.00, 0.13] | small
Group:Sex:Stimulus | 0.05 | [0.00, 0.16] | small
- Interpretation rule: field2013Code
a_posteriori_lmer(painEmpathy_p3_lmer)____________________________________________________________NOTE: Results may be misleading due to involvement in interactions$emmeans
Stimulus emmean SE df lower.CL upper.CL
no_pain 3.93 0.235 95 3.46 4.40
pain 3.47 0.226 95 3.02 3.91
Results are averaged over the levels of: Group, Sex
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95
$contrasts
contrast estimate SE df t.ratio p.value
no_pain - pain 0.465 0.113 95 4.127 0.0001
Results are averaged over the levels of: Group, Sex
Degrees-of-freedom method: kenward-roger
____________________________________________________________NOTE: Results may be misleading due to involvement in interactions$emmeans
Sex Stimulus emmean SE df lower.CL upper.CL
female no_pain 3.79 0.331 95 3.13 4.45
male no_pain 4.07 0.334 95 3.41 4.73
female pain 3.09 0.318 95 2.46 3.72
male pain 3.84 0.321 95 3.20 4.48
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 no_pain - male no_pain -0.2790 0.470 95 -0.594 0.9338
female no_pain - female pain 0.6994 0.159 95 4.413 0.0002
female no_pain - male pain -0.0484 0.461 107 -0.105 0.9996
male no_pain - female pain 0.9784 0.461 107 2.121 0.1531
male no_pain - male pain 0.2306 0.160 95 1.440 0.4779
female pain - male pain -0.7478 0.452 95 -1.653 0.3541
Results are averaged over the levels of: Group
Degrees-of-freedom method: kenward-roger
P value adjustment: tukey method for comparing a family of 4 estimates
____________________________________________________________
$emmeans
Group Sex Stimulus emmean SE df lower.CL upper.CL
vulnerable female no_pain 3.28 0.467 95 2.35 4.21
control female no_pain 4.30 0.467 95 3.38 5.23
vulnerable male no_pain 4.02 0.477 95 3.07 4.96
control male no_pain 4.12 0.467 95 3.19 5.05
vulnerable female pain 2.83 0.450 95 1.94 3.72
control female pain 3.35 0.450 95 2.46 4.25
vulnerable male pain 3.48 0.459 95 2.57 4.39
control male pain 4.20 0.450 95 3.31 5.09
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95
$contrasts
contrast estimate SE df t.ratio p.value
vulnerable female no_pain - control female no_pain -1.0272 0.661 95 -1.554 0.7760
vulnerable female no_pain - vulnerable male no_pain -0.7393 0.668 95 -1.107 0.9539
vulnerable female no_pain - control male no_pain -0.8459 0.661 95 -1.279 0.9042
vulnerable female no_pain - vulnerable female pain 0.4485 0.224 95 2.001 0.4869
vulnerable female no_pain - control female pain -0.0769 0.649 107 -0.119 1.0000
vulnerable female no_pain - vulnerable male pain -0.2001 0.655 107 -0.305 1.0000
vulnerable female no_pain - control male pain -0.9239 0.649 107 -1.424 0.8443
control female no_pain - vulnerable male no_pain 0.2879 0.668 95 0.431 0.9999
control female no_pain - control male no_pain 0.1814 0.661 95 0.274 1.0000
control female no_pain - vulnerable female pain 1.4757 0.649 107 2.274 0.3174
control female no_pain - control female pain 0.9503 0.224 95 4.240 0.0013
control female no_pain - vulnerable male pain 0.8271 0.655 107 1.262 0.9106
control female no_pain - control male pain 0.1034 0.649 107 0.159 1.0000
vulnerable male no_pain - control male no_pain -0.1066 0.668 95 -0.160 1.0000
vulnerable male no_pain - vulnerable female pain 1.1878 0.656 107 1.811 0.6140
vulnerable male no_pain - control female pain 0.6624 0.656 107 1.010 0.9720
vulnerable male no_pain - vulnerable male pain 0.5392 0.229 95 2.357 0.2748
vulnerable male no_pain - control male pain -0.1846 0.656 107 -0.281 1.0000
control male no_pain - vulnerable female pain 1.2944 0.649 107 1.995 0.4902
control male no_pain - control female pain 0.7690 0.649 107 1.185 0.9345
control male no_pain - vulnerable male pain 0.6458 0.655 107 0.985 0.9756
control male no_pain - control male pain -0.0780 0.224 95 -0.348 1.0000
vulnerable female pain - control female pain -0.5254 0.636 95 -0.826 0.9912
vulnerable female pain - vulnerable male pain -0.6486 0.643 95 -1.009 0.9721
vulnerable female pain - control male pain -1.3724 0.636 95 -2.157 0.3872
control female pain - vulnerable male pain -0.1232 0.643 95 -0.192 1.0000
control female pain - control male pain -0.8470 0.636 95 -1.331 0.8847
vulnerable male pain - control male pain -0.7238 0.643 95 -1.126 0.9495
Degrees-of-freedom method: kenward-roger
P value adjustment: tukey method for comparing a family of 8 estimates Late Centro-parietal: LPP
LPP, average from [550 750] ms interval
Electrodes CP1, Cz, CP2
Code
painEmpathy_lpp_lmer <- lmer(Amplitude ~ Group*Sex*Stimulus + (Stimulus|num_id) + (1|num_id:Electrode), lpp_data)
afex_plot(
painEmpathy_lpp_lmer,
x = 'Stimulus',
trace = 'Group',
panel = 'Sex',
id = 'num_id',
error_arg = list(width = .25),
dodge = my_dodge,
data_arg = list(
position =
position_jitterdodge(
jitter.width = my_jitter,
jitter.height = 0,
dodge.width = my_dodge ## needs to be same as dodge
)),
mapping = c('color'),
point_arg = list(size = 3)
)
painEmpathy_lpp_lmer@calllmer(formula = Amplitude ~ Group * Sex * Stimulus + (Stimulus |
num_id) + (1 | num_id:Electrode), data = lpp_data)
Code
options(width = 90)
anova(painEmpathy_lpp_lmer)Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
Group 0.6467 0.6467 1 95 3.5266 0.06346 .
Sex 0.3750 0.3750 1 95 2.0453 0.15596
Stimulus 19.3264 19.3264 1 95 105.3939 < 2e-16 ***
Group:Sex 0.0249 0.0249 1 95 0.1359 0.71322
Group:Stimulus 0.0274 0.0274 1 95 0.1495 0.69990
Sex:Stimulus 0.0012 0.0012 1 95 0.0066 0.93554
Group:Sex:Stimulus 0.0226 0.0226 1 95 0.1235 0.72609
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Code
interpret(omega_squared(painEmpathy_lpp_lmer, alternative = 'two.sided'), rules = 'field2013')# Effect Size for ANOVA (Type III)
Parameter | Omega2 (partial) | 95% CI | Interpretation
---------------------------------------------------------------------
Group | 0.03 | [0.00, 0.12] | small
Sex | 0.01 | [0.00, 0.09] | small
Stimulus | 0.52 | [0.38, 0.62] | large
Group:Sex | 0.00 | [0.00, 0.00] | very small
Group:Stimulus | 0.00 | [0.00, 0.00] | very small
Sex:Stimulus | 0.00 | [0.00, 0.00] | very small
Group:Sex:Stimulus | 0.00 | [0.00, 0.00] | very small
- Interpretation rule: field2013Code
a_posteriori_lmer(painEmpathy_lpp_lmer)____________________________________________________________NOTE: Results may be misleading due to involvement in interactions$emmeans
Stimulus emmean SE df lower.CL upper.CL
no_pain 1.03 0.0937 95 0.845 1.22
pain 2.01 0.1060 95 1.800 2.22
Results are averaged over the levels of: Group, Sex
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95
$contrasts
contrast estimate SE df t.ratio p.value
no_pain - pain -0.979 0.0954 95 -10.266 <.0001
Results are averaged over the levels of: Group, Sex
Degrees-of-freedom method: kenward-roger A deeper look in the back: P3a & P3b
P3a, average from [240 300] ms interval
Electrodes O1, Oz, O2
Code
painEmpathy_p3a_lmer <- lmer(Amplitude ~ Group*Sex*Stimulus + (Stimulus|num_id) + (1|num_id:Electrode), p3a_data)
afex_plot(
painEmpathy_p3a_lmer,
x = 'Stimulus',
trace = 'Group',
panel = 'Sex',
id = 'num_id',
error_arg = list(width = .25),
dodge = my_dodge,
data_arg = list(
position =
position_jitterdodge(
jitter.width = my_jitter,
jitter.height = 0,
dodge.width = my_dodge ## needs to be same as dodge
)),
mapping = c('color'),
point_arg = list(size = 3)
)
painEmpathy_p3a_lmer@calllmer(formula = Amplitude ~ Group * Sex * Stimulus + (Stimulus |
num_id) + (1 | num_id:Electrode), data = p3a_data)
Code
options(width = 110)
anova(painEmpathy_p3a_lmer)Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
Group 1.2233 1.2233 1 95.001 4.8018 0.030876 *
Sex 0.1229 0.1229 1 95.001 0.4825 0.488997
Stimulus 4.9139 4.9139 1 95.001 19.2887 2.919e-05 ***
Group:Sex 0.1954 0.1954 1 95.001 0.7669 0.383398
Group:Stimulus 0.0294 0.0294 1 95.001 0.1156 0.734645
Sex:Stimulus 0.7831 0.7831 1 95.001 3.0741 0.082775 .
Group:Sex:Stimulus 2.0638 2.0638 1 95.001 8.1009 0.005421 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Code
interpret(omega_squared(painEmpathy_p3a_lmer, alternative = 'two.sided'), rules = 'field2013')# Effect Size for ANOVA (Type III)
Parameter | Omega2 (partial) | 95% CI | Interpretation
---------------------------------------------------------------------
Group | 0.04 | [0.00, 0.14] | small
Sex | 0.00 | [0.00, 0.00] | very small
Stimulus | 0.16 | [0.05, 0.29] | large
Group:Sex | 0.00 | [0.00, 0.00] | very small
Group:Stimulus | 0.00 | [0.00, 0.00] | very small
Sex:Stimulus | 0.02 | [0.00, 0.11] | small
Group:Sex:Stimulus | 0.07 | [0.00, 0.18] | medium
- Interpretation rule: field2013Code
a_posteriori_lmer(painEmpathy_p3a_lmer)____________________________________________________________NOTE: Results may be misleading due to involvement in interactions$emmeans
Group emmean SE df lower.CL upper.CL
vulnerable 3.22 0.381 95 2.46 3.97
control 4.39 0.377 95 3.64 5.14
Results are averaged over the levels of: Sex, Stimulus
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95
$contrasts
contrast estimate SE df t.ratio p.value
vulnerable - control -1.17 0.536 95 -2.191 0.0309
Results are averaged over the levels of: Sex, Stimulus
Degrees-of-freedom method: kenward-roger
____________________________________________________________NOTE: Results may be misleading due to involvement in interactions$emmeans
Stimulus emmean SE df lower.CL upper.CL
no_pain 4.05 0.279 95 3.49 4.6
pain 3.56 0.268 95 3.03 4.1
Results are averaged over the levels of: Group, Sex
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95
$contrasts
contrast estimate SE df t.ratio p.value
no_pain - pain 0.484 0.11 95 4.392 <.0001
Results are averaged over the levels of: Group, Sex
Degrees-of-freedom method: kenward-roger
____________________________________________________________
$emmeans
Group Sex Stimulus emmean SE df lower.CL upper.CL
vulnerable female no_pain 3.00 0.555 95 1.90 4.10
control female no_pain 4.92 0.555 95 3.82 6.02
vulnerable male no_pain 3.96 0.566 95 2.84 5.08
control male no_pain 4.31 0.555 95 3.21 5.41
vulnerable female pain 2.60 0.533 95 1.54 3.66
control female pain 3.96 0.533 95 2.91 5.02
vulnerable male pain 3.32 0.544 95 2.24 4.40
control male pain 4.37 0.533 95 3.32 5.43
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95
$contrasts
contrast estimate SE df t.ratio p.value
vulnerable female no_pain - control female no_pain -1.91892 0.784 95 -2.447 0.2316
vulnerable female no_pain - vulnerable male no_pain -0.96150 0.792 95 -1.213 0.9260
vulnerable female no_pain - control male no_pain -1.31455 0.784 95 -1.676 0.7024
vulnerable female no_pain - vulnerable female pain 0.40142 0.219 95 1.829 0.6023
vulnerable female no_pain - control female pain -0.96454 0.769 103 -1.254 0.9133
vulnerable female no_pain - vulnerable male pain -0.31895 0.777 103 -0.411 0.9999
vulnerable female no_pain - control male pain -1.37495 0.769 103 -1.787 0.6299
control female no_pain - vulnerable male no_pain 0.95742 0.792 95 1.208 0.9276
control female no_pain - control male no_pain 0.60437 0.784 95 0.771 0.9942
control female no_pain - vulnerable female pain 2.32034 0.769 103 3.016 0.0616
control female no_pain - control female pain 0.95437 0.219 95 4.348 0.0009
control female no_pain - vulnerable male pain 1.59997 0.777 103 2.059 0.4480
control female no_pain - control male pain 0.54397 0.769 103 0.707 0.9966
vulnerable male no_pain - control male no_pain -0.35305 0.792 95 -0.446 0.9998
vulnerable male no_pain - vulnerable female pain 1.36292 0.778 103 1.753 0.6528
vulnerable male no_pain - control female pain -0.00305 0.778 103 -0.004 1.0000
vulnerable male no_pain - vulnerable male pain 0.64255 0.224 95 2.868 0.0909
vulnerable male no_pain - control male pain -0.41345 0.778 103 -0.532 0.9995
control male no_pain - vulnerable female pain 1.71597 0.769 103 2.231 0.3425
control male no_pain - control female pain 0.35000 0.769 103 0.455 0.9998
control male no_pain - vulnerable male pain 0.99560 0.777 103 1.281 0.9037
control male no_pain - control male pain -0.06040 0.219 95 -0.275 1.0000
vulnerable female pain - control female pain -1.36597 0.754 95 -1.812 0.6137
vulnerable female pain - vulnerable male pain -0.72037 0.762 95 -0.946 0.9806
vulnerable female pain - control male pain -1.77638 0.754 95 -2.356 0.2752
control female pain - vulnerable male pain 0.64559 0.762 95 0.848 0.9897
control female pain - control male pain -0.41041 0.754 95 -0.544 0.9994
vulnerable male pain - control male pain -1.05600 0.762 95 -1.386 0.8613
Degrees-of-freedom method: kenward-roger
P value adjustment: tukey method for comparing a family of 8 estimates P3b, average from [340 390] ms interval
Electrodes O1, Oz, O2
Code
painEmpathy_p3b_lmer <- lmer(Amplitude ~ Group*Sex*Stimulus + (Stimulus|num_id) + (1|num_id:Electrode), p3b_data)
afex_plot(
painEmpathy_p3b_lmer,
x = 'Stimulus',
trace = 'Group',
panel = 'Sex',
id = 'num_id',
error_arg = list(width = .25),
dodge = my_dodge,
data_arg = list(
position =
position_jitterdodge(
jitter.width = my_jitter,
jitter.height = 0,
dodge.width = my_dodge ## needs to be same as dodge
)),
mapping = c('color'),
point_arg = list(size = 3)
)
painEmpathy_p3b_lmer@calllmer(formula = Amplitude ~ Group * Sex * Stimulus + (Stimulus |
num_id) + (1 | num_id:Electrode), data = p3b_data)
Code
options(width = 110)
anova(painEmpathy_p3b_lmer)Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
Group 0.00010 0.00010 1 95.000 0.0004 0.9845
Sex 0.10768 0.10768 1 95.000 0.4266 0.5152
Stimulus 0.64962 0.64962 1 94.999 2.5737 0.1120
Group:Sex 0.05051 0.05051 1 95.000 0.2001 0.6556
Group:Stimulus 0.30469 0.30469 1 94.999 1.2071 0.2747
Sex:Stimulus 0.67173 0.67173 1 94.999 2.6614 0.1061
Group:Sex:Stimulus 0.20362 0.20362 1 94.999 0.8067 0.3714Code
interpret(omega_squared(painEmpathy_p3b_lmer, alternative = 'two.sided'), rules = 'field2013')# Effect Size for ANOVA (Type III)
Parameter | Omega2 (partial) | 95% CI | Interpretation
---------------------------------------------------------------------
Group | 0.00 | [0.00, 0.00] | very small
Sex | 0.00 | [0.00, 0.00] | very small
Stimulus | 0.02 | [0.00, 0.10] | small
Group:Sex | 0.00 | [0.00, 0.00] | very small
Group:Stimulus | 2.13e-03 | [0.00, 0.06] | very small
Sex:Stimulus | 0.02 | [0.00, 0.10] | small
Group:Sex:Stimulus | 0.00 | [0.00, 0.00] | very small
- Interpretation rule: field2013Code
a_posteriori_lmer(painEmpathy_p3b_lmer)Factorial Mass Univariate analysis (FMUT, cluster mass)
Code
n_ctl <- sum(unique(painEmpathy_data$num_id) >= 100)
n_vul <- sum(unique(painEmpathy_data$num_id) < 100)
n_fmut <- min(n_ctl, n_vul)Using 49 cases by group as FMUT only works with groups of equal size.
Fields and Kuperberg (2020)
max_dist value of 50 corresponds to an approximate distance of 5.24 cm (assuming a 56 cm great circle circumference head and that your electrode coordinates are based on an idealized spherical head with radius of 85.000000). Min/Max distances between all pairs of channels (in chanlocs units): 26.560450/169.891840 Median (semi-IQR) distance between all pairs of channels (in chanlocs units): 119.172654 (30.583399) Mean (SD) # of neighbors per channel: 2.6 (1.2) Median (semi-IQR) # of neighbors per channel: 2.0 (1.0) Min/max # of neighbors per channel: 0 to 4
Version 2
pev2_painEmpathy_fmut_version_2: 2 significant Stimulus cluster(s) out of 33 cluster 1 F-masss: 626 cluster 1 p-value: 0.0442 cluster 2 F-masss: 17166 cluster 2 p-value: 0.0001
Mass Univariate analysis (MASS, cluster mass), full time window
Using all cases
Groppe, Urbach, and Kutas (2011)
max_dist value of 50 corresponds to an approximate distance of 5.24 cm (assuming a 56 cm great circle circumference head and that your electrode coordinates are based on an idealized spherical head with radius of 85.000000). Min/Max distances between all pairs of channels (in chanlocs units): 26.560450/169.891840 Median (semi-IQR) distance between all pairs of channels (in chanlocs units): 119.172654 (30.583399) Mean (SD) # of neighbors per channel: 2.6 (1.2) Median (semi-IQR) # of neighbors per channel: 2.0 (1.0) Min/max # of neighbors per channel: 0 to 4
Version 2
pev2_painEmpathy_ctl: 1 significant positive cluster(s) out of 17 cluster 1 t-masss: 2096 cluster 1 p-value: 0.0000 1 significant negative cluster(s) out of 28 cluster 1 t-masss: -217 cluster 1 p-value: 0.0352 pev2_painEmpathy_vul: 1 significant positive cluster(s) out of 9 cluster 1 t-masss: 2369 cluster 1 p-value: 0.0000 0 significant negative cluster(s) out of 14
Functional Connectivity: occipital ↔︎ parietal
Multivariate Interaction Measure, MIM (ROIconnect, Pellegrini et al. 2023), beta band (13-30 Hz) 1 sec post-stimulus.
Code
options(width = 80)
summary(connectivity_data) ERPset ID Group City image
s001_vul_s: 8 s001 : 8 vulnerable:392 stgo:696 no_pain_v1:396
s002_vul_s: 8 s002 : 8 control :400 viña: 96 pain_v1 :396
s003_vul_s: 8 s003 : 8
s004_vul_s: 8 s004 : 8
s005_vul_s: 8 s005 : 8
s006_vul_s: 8 s006 : 8
(Other) :744 (Other):744
region_a region_b median_connectivity Version
left_occipital :396 parietal_ipsi :396 Min. :0.04779 v1:792
right_occipital:396 parietal_contra:396 1st Qu.:0.07131
Median :0.08096
Mean :0.08267
3rd Qu.:0.09109
Max. :0.15159
Stimulus log_median_connectivity num_id Sex
pain :396 Min. :-3.041 Min. : 1.00 female:400
no_pain:396 1st Qu.:-2.641 1st Qu.: 28.00 male :392
Median :-2.514 Median :100.00
Mean :-2.512 Mean : 76.87
3rd Qu.:-2.396 3rd Qu.:126.00
Max. :-1.887 Max. :150.00
Code
connectivity_data_wide_region_a <- connectivity_data[c('ERPset', 'region_a', 'region_b', 'Stimulus', 'log_median_connectivity')] |>
pivot_wider(names_from = region_a, values_from = log_median_connectivity)
connectivity_data_wide_region_b <- connectivity_data[c('ERPset', 'region_a', 'region_b', 'Stimulus', 'log_median_connectivity')] |>
pivot_wider(names_from = region_b, values_from = log_median_connectivity)
connectivity_data_wide_stimulus <- connectivity_data[c('ERPset', 'region_a', 'region_b', 'Stimulus', 'log_median_connectivity')] |>
pivot_wider(names_from = Stimulus, values_from = log_median_connectivity)
stims <- c('pain', 'no_pain')
region_a_parts <- c(paste0('left_', label_a), paste0('right_', label_a))
region_b_parts <- c(paste0(label_b, '_ipsi'), paste0(label_b, '_contra'))
region_a_pairs <- ggpairs(connectivity_data_wide_region_a,
aes(colour = Stimulus, alpha = .5, linewidth = NA),
columns = region_a_parts,
lower = list(continuous = wrap('points', alpha = .4)),
progress = FALSE
)
region_b_pairs <- ggpairs(connectivity_data_wide_region_b,
aes(colour = Stimulus, alpha = .5, linewidth = NA),
columns = region_b_parts,
lower = list(continuous = wrap('points', alpha = .4)),
progress = FALSE
)
stims_pairs <- ggpairs(connectivity_data_wide_stimulus,
aes(colour = region_b, alpha = .5, linewidth = NA),
columns = stims,
lower = list(continuous = wrap('points', alpha = .4)),
progress = FALSE
)
suppressWarnings(print(region_a_pairs))
suppressWarnings(print(region_b_pairs))
suppressWarnings(print(stims_pairs))
Connectivity ANOVA (glmer):
Code
connectivity_glmer <- glmer(median_connectivity ~ Group*Sex*Stimulus*region_b + (Stimulus*region_b|num_id), family = Gamma(log), connectivity_data)
afex_plot(connectivity_glmer,
x = 'region_b',
trace = 'Stimulus',
panel = 'Group',
id = 'num_id',
error_arg = list(width = .3, lwd = .75),
dodge = my_dodge,
data_arg = list(
position =
position_jitterdodge(
jitter.width = my_jitter,
dodge.width = my_dodge ## needs to be same as dodge
)),
mapping = c('color'),, data_alpha = .3,
point_arg = list(size = 3)
)
connectivity_glmer@callglmer(formula = median_connectivity ~ Group * Sex * Stimulus *
region_b + (Stimulus * region_b | num_id), data = connectivity_data,
family = Gamma(log))
Code
options(width = 150)
anova(connectivity_glmer)Analysis of Variance Table
npar Sum Sq Mean Sq F value
Group 1 0.13958 0.13958 11.6667
Sex 1 0.05705 0.05705 4.7686
Stimulus 1 0.00232 0.00232 0.1938
region_b 1 0.98862 0.98862 82.6353
Group:Sex 1 0.01612 0.01612 1.3471
Group:Stimulus 1 0.00001 0.00001 0.0007
Sex:Stimulus 1 0.02252 0.02252 1.8824
Group:region_b 1 0.00265 0.00265 0.2216
Sex:region_b 1 0.00023 0.00023 0.0194
Stimulus:region_b 1 0.00276 0.00276 0.2305
Group:Sex:Stimulus 1 0.00002 0.00002 0.0015
Group:Sex:region_b 1 0.00245 0.00245 0.2048
Group:Stimulus:region_b 1 0.00030 0.00030 0.0255
Sex:Stimulus:region_b 1 0.00025 0.00025 0.0210
Group:Sex:Stimulus:region_b 1 0.00034 0.00034 0.0282Code
a_posteriori_glmer(connectivity_glmer)Analysis of Deviance Table (Type II Wald chisquare tests)
Response: median_connectivity
Chisq Df Pr(>Chisq)
Group 2.8191 1 0.09315 .
Sex 1.2193 1 0.26950
Stimulus 0.0885 1 0.76605
region_b 117.1079 1 < 2e-16 ***
Group:Sex 0.3376 1 0.56121
Group:Stimulus 0.0018 1 0.96651
Sex:Stimulus 0.8227 1 0.36438
Group:region_b 0.3072 1 0.57939
Sex:region_b 0.0294 1 0.86390
Stimulus:region_b 0.3409 1 0.55931
Group:Sex:Stimulus 0.0000 1 0.99646
Group:Sex:region_b 0.2983 1 0.58494
Group:Stimulus:region_b 0.0384 1 0.84459
Sex:Stimulus:region_b 0.0310 1 0.86019
Group:Sex:Stimulus:region_b 0.0418 1 0.83802
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
____________________________________________________________NOTE: Results may be misleading due to involvement in interactions$emmeans
region_b emmean SE df asymp.LCL asymp.UCL
parietal_ipsi -2.55 0.0199 Inf -2.59 -2.51
parietal_contra -2.48 0.0200 Inf -2.52 -2.44
Results are averaged over the levels of: Group, Sex, Stimulus
Results are given on the log (not the response) scale.
Confidence level used: 0.95
$contrasts
contrast estimate SE df z.ratio p.value
parietal_ipsi - parietal_contra -0.0732 0.00675 Inf -10.845 <.0001
Results are averaged over the levels of: Group, Sex, Stimulus
Results are given on the log (not the response) scale. Connectivity Assumptions:
Code
check_model(connectivity_glmer)
References
Delorme, Arnaud, and Scott Makeig. 2004. “EEGLAB: An Open Source Toolbox for Analysis of Single-Trial EEG Dynamics Including Independent Component Analysis.” Journal of Neuroscience Methods 134 (March): 9–21. https://doi.org/10.1016/J.JNEUMETH.2003.10.009.
Dong, Li, Fali Li, Qiang Liu, Xin Wen, Yongxiu Lai, Peng Xu, and Dezhong Yao. 2017. “MATLAB Toolboxes for Reference Electrode Standardization Technique (REST) of Scalp EEG.” Frontiers in Neuroscience 11 (October): 601. https://doi.org/10.3389/fnins.2017.00601.
Fields, Eric C., and Gina R. Kuperberg. 2020. “Having Your Cake and Eating It Too: Flexibility and Power with Mass Univariate Statistics for ERP Data.” Psychophysiology 57 (February): e13468. https://doi.org/10.1111/PSYP.13468.
Groppe, David M., Thomas P. Urbach, and Marta Kutas. 2011. “Mass Univariate Analysis of Event-Related Brain Potentials/Fields i: A Critical Tutorial Review.” Psychophysiology 48 (December): 1711–25. https://doi.org/10.1111/J.1469-8986.2011.01273.X.
Lopez-Calderon, Javier, and Steven J. Luck. 2014. “ERPLAB: An Open-Source Toolbox for the Analysis of Event-Related Potentials.” Frontiers in Human Neuroscience 8 (April): 75729. https://doi.org/10.3389/FNHUM.2014.00213/BIBTEX.
Pellegrini, Franziska, Arnaud Delorme, Vadim Nikulin, and Stefan Haufe. 2023. “Identifying Good Practices for Detecting Inter-Regional Linear Functional Connectivity from EEG.” NeuroImage 277 (August): 120218. https://doi.org/10.1016/J.NEUROIMAGE.2023.120218.
Pion-Tonachini, Luca, Ken Kreutz-Delgado, and Scott Makeig. 2019. “ICLabel: An Automated Electroencephalographic Independent Component Classifier, Dataset, and Website.” NeuroImage 198: 181–97. https://doi.org/10.1016/j.neuroimage.2019.05.026.