Code
cat('\014') # clean terminalCode
rm(list = ls()) # clean workspace
library(tidyverse)
library(afex)
library(emmeans)
library(psych)
library(robustbase)
library(performance)Common Humanity & Minfulness 2023
Response times
Code
options(mc_doScale_quiet = TRUE)
theme_set(
theme_minimal()
)Code
first_day <- as.Date('2023/11/10')
xcluded <- c('d20', 'd21', 'd26', 'd33')
df_neurokit2 <- read_csv('../meditation_task/data/hrv_hrf_hra_rsa_rrv_neurokit2.csv', col_types = cols())
df_age <- read_csv('gsheets/Antecedentes generales.csv', col_types = cols()) |>
rename(Subject = `Código de participante:`,
name = `Nombre:`,
age = `Edad:`) |>
mutate(Subject = replace(Subject, Subject == 'd24_s46_t01_f' , 'd24_s47_t01_f')) |>
mutate(Subject = replace(Subject, Subject == 'd25_s49_test01_f', 'd25_s49_t01_f')) |>
mutate(Subject = replace(Subject, Subject == 'd28_s57_t01_m' , 'd29_s57_t01_m')) |>
mutate(Subject = replace(Subject, Subject == 'd28_s58_t01_m' , 'd29_s58_t01_m')) |>
mutate(Subject = replace(Subject, Subject == 'd32_64_t01_f' , 'd32_s64_t01_f')) |>
separate(Subject, c("duo", "id", "session", "sex"), sep = "_", remove = FALSE) |>
mutate(the_day = as.Date(word(`Marca temporal`, 1))) |>
filter(grepl('d[0-9]{2}_s.*', Subject) & the_day >= first_day) |>
filter(!(duo %in% xcluded)) |>
left_join(y = df_neurokit2[c('sbj', 'grp')], by = c('id' = 'sbj')) |>
rename(group = grp) |>
mutate_if(is.character, as.factor)
# write_csv(df_age, 'data/df_age_2023_data_clean.csv')
if (is_empty(list.files('data/rt_files'))) {
rt_file_list <- list.files('../code_apt/data', pattern = '.*_d[0-9]{2}_s.*', full.names = TRUE)
for (rt_filename in rt_file_list) {
rt_ind_data <- read_csv(rt_filename, col_types = cols()) |>
filter(rehersal == 'no')
rt_task_filename <- gsub('../code_apt/data/', 'data/rt_files/task_', rt_filename)
repeats <- which(rt_ind_data$word == 'Repugnante')[5:8]
rt_ind_data <- rt_ind_data[-repeats, ]
write_csv(rt_ind_data, rt_task_filename)
}
}
df_apt <- list.files('data/rt_files', pattern = '.*_d[0-9]{2}_s.*', full.names = TRUE) |>
lapply(read_csv, col_types = cols()) |>
bind_rows() |>
mutate(Subject = replace(Subject, Subject == 'd28_s57_t01_m', 'd29_s57_t01_m')) |>
mutate(Subject = replace(Subject, Subject == 'd28_s58_t01_m', 'd29_s58_t01_m')) |>
mutate(Subject = replace(Subject, Subject == 'd60_s60_t01_m', 'd30_s60_t01_m')) |>
separate(Subject, c('duo', 'id', 'session', 'sex'), sep = '_', remove = FALSE) |>
left_join(df_age[c('id', 'age')], "id") |>
mutate(sex = if_else(sex == 'f', 'female', 'male')) |>
left_join(y = df_neurokit2[c('sbj', 'grp')], by = c('id' = 'sbj')) |>
rename(group = grp) |>
mutate(primer = factor(primer, levels = c('it', 'other', 'you', 'me'))) |>
mutate_if(is.character, as.factor) |>
mutate(seed = factor(seed)) |>
mutate(trial = factor(trial)) |>
mutate(log10_rt = log10(rt+1))
lo_log10 <- adjbox(df_apt$log10_rt, plot = FALSE)$fence[1]
up_log10 <- adjbox(df_apt$log10_rt, plot = FALSE)$fence[2]
lo <- adjbox(df_apt$rt, plot = FALSE)$fence[1]
up <- adjbox(df_apt$rt, plot = FALSE)$fence[2]
df_apt$rt_no_out <- df_apt$rt
df_apt$rt_no_out[df_apt$rt < lo | df_apt$rt > up] <- NA
df_apt$rt_no_out_sec <- df_apt$rt_no_out / 1000
df_apt$log10_rt_no_out <- df_apt$log10_rt
df_apt$log10_rt_no_out[df_apt$log10_rt < lo_log10 | df_apt$log10_rt > up_log10] <- NA
# write_csv(df_apt, 'data/df_apt_2023_data_clean.csv')General description
Code
summary(df_apt) Subject duo id session sex
d01_s01_t01_m: 80 d01 : 320 s01 : 160 t01:5120 female:5120
d01_s01_t02_m: 80 d02 : 320 s02 : 160 t02:5120 male :5120
d01_s02_t01_m: 80 d03 : 320 s03 : 160
d01_s02_t02_m: 80 d04 : 320 s04 : 160
d02_s03_t01_m: 80 d05 : 320 s05 : 160
d02_s03_t02_m: 80 d06 : 320 s06 : 160
(Other) :9760 (Other):8320 (Other):9280
trial primer photo valence
1 : 254 it :2553 20230515_161630_phone.jpg:2553 negative:5120
7 : 253 other:2567 she02.jpeg :1286 positive:5120
19 : 253 you :2560 IMG_8904.jpg : 844
4 : 252 me :2560 he_02.jpeg : 397
9 : 252 unnamed.jpg : 237
10 : 251 IMG_2409.jpg : 201
(Other):8725 (Other) :4722
word arrow rt score
Admirable : 512 left :5339 Min. : 0.0 Min. :0.0000
Asfixiante : 512 right:4897 1st Qu.: 321.0 1st Qu.:1.0000
Aterrador : 512 NA's : 4 Median : 414.0 Median :1.0000
Delicioso : 512 Mean : 531.7 Mean :0.9545
Desesperante: 512 3rd Qu.: 579.0 3rd Qu.:1.0000
Desgraciado : 512 Max. :5006.0 Max. :1.0000
(Other) :7168
block rehersal seed age
positive_left :4864 no:10240 671471 : 240 Min. :18.00
positive_right:5376 43850 : 160 1st Qu.:21.00
100851 : 160 Median :22.00
141112 : 160 Mean :23.77
155478 : 160 3rd Qu.:25.00
195300 : 160 Max. :48.00
(Other):9200
group log10_rt rt_no_out rt_no_out_sec
humanity :5120 Min. :0.000 Min. : 221 Min. :0.221
mindfulness:5120 1st Qu.:2.508 1st Qu.: 329 1st Qu.:0.329
Median :2.618 Median : 417 Median :0.417
Mean :2.648 Mean : 503 Mean :0.503
3rd Qu.:2.763 3rd Qu.: 570 3rd Qu.:0.570
Max. :3.700 Max. :1642 Max. :1.642
NA's :658 NA's :658
log10_rt_no_out
Min. :2.310
1st Qu.:2.518
Median :2.622
Mean :2.664
3rd Qu.:2.765
Max. :3.389
NA's :411 Code
ggplot(
df_apt, aes(x = rt, fill = group, color = group)) +
geom_histogram(alpha = .4) +
facet_wrap(~group)
ggplot(
df_apt, aes(x = rt_no_out, fill = group, color = group)) +
geom_histogram(alpha = .4) +
facet_wrap(~group)
ggplot(
df_apt, aes(x = log10_rt, fill = group, color = group)) +
geom_histogram(alpha = .4) +
facet_wrap(~group)
ggplot(
df_apt, aes(x = log10_rt_no_out, fill = group, color = group)) +
geom_histogram(alpha = .4) +
facet_wrap(~group)
Full Model
Code
load('apt_lmer_full.Rdata')
# apt_lmer <- lmer(log10_rt_no_out ~ group*sex*primer*valence*session+age + (primer*valence*session|duo:id) + (1|word),
# df_apt)
# save(apt_lmer, file = 'apt_lmer_full.Rdata')
afex_plot(
apt_lmer,
id = 'id',
x = 'session',
trace = 'group',
panel = 'primer',
error_arg = list(width = .4, lwd = .75),
dodge = .3,
data_arg = list(
position =
position_jitterdodge(
jitter.width = .1,
dodge.width = .3 ## needs to be same as dodge
)),
mapping = c('color'),
point_arg = list(size = 3)
)
Code
options(width = 200)
summary(apt_lmer)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: log10_rt_no_out ~ group * sex * primer * valence * session + age + (primer * valence * session | duo:id) + (1 | word)
Data: df_apt
REML criterion at convergence: -6671.4
Scaled residuals:
Min 1Q Median 3Q Max
-2.8169 -0.6274 -0.1751 0.4395 4.5811
Random effects:
Groups Name Variance Std.Dev. Corr
duo:id (Intercept) 1.924e-02 0.138696
primerother 2.504e-03 0.050044 -0.49
primeryou 2.839e-03 0.053279 -0.54 0.85
primerme 1.810e-03 0.042541 -0.50 0.28 0.47
valencepositive 4.301e-03 0.065578 -0.14 0.39 0.35 0.64
sessiont02 7.964e-03 0.089241 -0.56 0.44 0.69 0.59 0.51
primerother:valencepositive 4.649e-03 0.068185 0.16 -0.53 -0.40 -0.41 -0.90 -0.32
primeryou:valencepositive 4.187e-03 0.064708 0.29 -0.57 -0.65 -0.50 -0.83 -0.59 0.84
primerme:valencepositive 3.367e-03 0.058022 0.04 0.12 0.12 -0.46 -0.79 -0.33 0.61 0.52
primerother:sessiont02 3.673e-03 0.060605 0.40 -0.86 -0.76 -0.53 -0.71 -0.54 0.76 0.76 0.18
primeryou:sessiont02 5.920e-03 0.076942 0.32 -0.61 -0.84 -0.51 -0.48 -0.58 0.55 0.72 -0.01 0.76
primerme:sessiont02 4.030e-03 0.063485 0.18 -0.34 -0.37 -0.68 -0.67 -0.37 0.61 0.54 0.25 0.73 0.65
valencepositive:sessiont02 5.799e-03 0.076150 0.17 -0.72 -0.71 -0.51 -0.81 -0.54 0.85 0.86 0.31 0.92 0.82 0.75
primerother:valencepositive:sessiont02 9.309e-03 0.096483 -0.16 0.71 0.56 0.51 0.86 0.37 -0.92 -0.79 -0.44 -0.91 -0.64 -0.68 -0.93
primeryou:valencepositive:sessiont02 9.291e-03 0.096390 -0.22 0.80 0.78 0.50 0.74 0.45 -0.82 -0.84 -0.26 -0.90 -0.81 -0.57 -0.93 0.93
primerme:valencepositive:sessiont02 3.903e-03 0.062470 0.01 0.53 0.21 0.32 0.76 0.18 -0.76 -0.54 -0.43 -0.77 -0.29 -0.65 -0.72 0.85 0.64
word (Intercept) 6.995e-05 0.008364
Residual 2.688e-02 0.163937
Number of obs: 9829, groups: duo:id, 64; word, 20
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 2.678e+00 7.034e-02 7.387e+01 38.072 < 2e-16 ***
groupmindfulness 6.923e-03 5.342e-02 6.199e+01 0.130 0.89730
sexmale -4.155e-02 5.245e-02 5.995e+01 -0.792 0.43145
primerother -2.568e-03 2.228e-02 8.651e+01 -0.115 0.90852
primeryou -5.226e-03 2.296e-02 7.321e+01 -0.228 0.82060
primerme -4.079e-03 2.157e-02 1.059e+02 -0.189 0.85035
valencepositive 1.005e-02 2.510e-02 6.942e+01 0.400 0.69027
sessiont02 -6.273e-02 2.923e-02 6.437e+01 -2.146 0.03562 *
age 1.141e-03 2.610e-03 5.903e+01 0.437 0.66355
groupmindfulness:sexmale 4.440e-02 7.480e-02 6.082e+01 0.594 0.55501
groupmindfulness:primerother 5.611e-03 3.161e-02 8.757e+01 0.178 0.85951
groupmindfulness:primeryou -7.808e-04 3.239e-02 7.246e+01 -0.024 0.98083
groupmindfulness:primerme 2.149e-02 3.032e-02 1.035e+02 0.709 0.47997
sexmale:primerother 1.027e-02 3.163e-02 8.778e+01 0.325 0.74627
sexmale:primeryou -2.226e-02 3.239e-02 7.238e+01 -0.687 0.49404
sexmale:primerme -2.407e-02 3.032e-02 1.034e+02 -0.794 0.42912
groupmindfulness:valencepositive 1.340e-02 3.504e-02 6.630e+01 0.382 0.70336
sexmale:valencepositive 3.892e-02 3.504e-02 6.629e+01 1.111 0.27075
primerother:valencepositive -1.288e-02 3.125e-02 7.776e+01 -0.412 0.68125
primeryou:valencepositive -1.877e-02 3.096e-02 6.997e+01 -0.606 0.54630
primerme:valencepositive -1.401e-02 3.010e-02 1.122e+02 -0.465 0.64255
groupmindfulness:sessiont02 2.796e-02 4.119e-02 6.352e+01 0.679 0.49967
sexmale:sessiont02 2.313e-02 4.124e-02 6.384e+01 0.561 0.57692
primerother:sessiont02 -9.642e-03 3.040e-02 9.532e+01 -0.317 0.75186
primeryou:sessiont02 -4.159e-02 3.292e-02 6.643e+01 -1.263 0.21084
primerme:sessiont02 -9.780e-03 3.104e-02 9.072e+01 -0.315 0.75342
valencepositive:sessiont02 1.224e-02 3.276e-02 7.705e+01 0.374 0.70971
groupmindfulness:sexmale:primerother -6.229e-02 4.473e-02 8.774e+01 -1.393 0.16723
groupmindfulness:sexmale:primeryou -8.628e-03 4.567e-02 7.159e+01 -0.189 0.85068
groupmindfulness:sexmale:primerme -2.658e-02 4.273e-02 1.021e+02 -0.622 0.53529
groupmindfulness:sexmale:valencepositive -1.073e-01 4.945e-02 6.577e+01 -2.170 0.03366 *
groupmindfulness:primerother:valencepositive -2.042e-02 4.421e-02 7.790e+01 -0.462 0.64545
groupmindfulness:primeryou:valencepositive 1.155e-02 4.367e-02 6.923e+01 0.264 0.79226
groupmindfulness:primerme:valencepositive -2.229e-02 4.241e-02 1.106e+02 -0.525 0.60032
sexmale:primerother:valencepositive -4.924e-02 4.424e-02 7.809e+01 -1.113 0.26912
sexmale:primeryou:valencepositive 1.795e-03 4.365e-02 6.902e+01 0.041 0.96732
sexmale:primerme:valencepositive -1.060e-02 4.243e-02 1.107e+02 -0.250 0.80313
groupmindfulness:sexmale:sessiont02 -6.874e-02 5.834e-02 6.386e+01 -1.178 0.24303
groupmindfulness:primerother:sessiont02 -1.021e-02 4.300e-02 9.538e+01 -0.238 0.81276
groupmindfulness:primeryou:sessiont02 9.522e-03 4.629e-02 6.505e+01 0.206 0.83767
groupmindfulness:primerme:sessiont02 -2.628e-02 4.358e-02 8.825e+01 -0.603 0.54803
sexmale:primerother:sessiont02 -1.693e-02 4.298e-02 9.511e+01 -0.394 0.69445
sexmale:primeryou:sessiont02 7.218e-02 4.639e-02 6.552e+01 1.556 0.12455
sexmale:primerme:sessiont02 3.707e-02 4.365e-02 8.872e+01 0.849 0.39804
groupmindfulness:valencepositive:sessiont02 -8.013e-02 4.610e-02 7.558e+01 -1.738 0.08625 .
sexmale:valencepositive:sessiont02 -8.338e-02 4.612e-02 7.568e+01 -1.808 0.07461 .
primerother:valencepositive:sessiont02 1.498e-03 4.450e-02 7.930e+01 0.034 0.97323
primeryou:valencepositive:sessiont02 1.318e-02 4.480e-02 8.193e+01 0.294 0.76935
primerme:valencepositive:sessiont02 -6.410e-03 4.070e-02 1.439e+02 -0.158 0.87507
groupmindfulness:sexmale:primerother:valencepositive 1.439e-01 6.252e-02 7.786e+01 2.301 0.02407 *
groupmindfulness:sexmale:primeryou:valencepositive 6.812e-02 6.160e-02 6.848e+01 1.106 0.27264
groupmindfulness:sexmale:primerme:valencepositive 6.599e-02 5.985e-02 1.096e+02 1.103 0.27263
groupmindfulness:sexmale:primerother:sessiont02 8.858e-02 6.101e-02 9.647e+01 1.452 0.14980
groupmindfulness:sexmale:primeryou:sessiont02 1.292e-02 6.560e-02 6.542e+01 0.197 0.84447
groupmindfulness:sexmale:primerme:sessiont02 4.480e-02 6.183e-02 8.911e+01 0.725 0.47058
groupmindfulness:sexmale:valencepositive:sessiont02 2.028e-01 6.530e-02 7.595e+01 3.105 0.00267 **
groupmindfulness:primerother:valencepositive:sessiont02 6.077e-02 6.283e-02 7.880e+01 0.967 0.33642
groupmindfulness:primeryou:valencepositive:sessiont02 5.295e-02 6.300e-02 8.015e+01 0.841 0.40312
groupmindfulness:primerme:valencepositive:sessiont02 8.431e-02 5.719e-02 1.405e+02 1.474 0.14271
sexmale:primerother:valencepositive:sessiont02 9.070e-02 6.282e-02 7.868e+01 1.444 0.15279
sexmale:primeryou:valencepositive:sessiont02 6.931e-03 6.305e-02 8.032e+01 0.110 0.91274
sexmale:primerme:valencepositive:sessiont02 2.157e-02 5.726e-02 1.411e+02 0.377 0.70696
groupmindfulness:sexmale:primerother:valencepositive:sessiont02 -2.296e-01 8.911e-02 7.953e+01 -2.577 0.01182 *
groupmindfulness:sexmale:primeryou:valencepositive:sessiont02 -1.552e-01 8.925e-02 8.051e+01 -1.738 0.08597 .
groupmindfulness:sexmale:primerme:valencepositive:sessiont02 -1.587e-01 8.117e-02 1.419e+02 -1.956 0.05246 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation matrix not shown by default, as p = 65 > 12.
Use print(x, correlation=TRUE) or
vcov(x) if you need itoptimizer (nloptwrap) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')Code
cat(rep('_', 100), '\n', sep = '')____________________________________________________________________________________________________Code
anova(apt_lmer)Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
group 0.01301 0.01301 1 59.610 0.4842 0.48924
sex 0.02463 0.02463 1 59.680 0.9165 0.34227
primer 0.17406 0.05802 3 77.754 2.1589 0.09960 .
valence 0.00134 0.00134 1 31.192 0.0499 0.82466
session 1.00749 1.00749 1 60.287 37.4878 7.54e-08 ***
age 0.00514 0.00514 1 59.032 0.1912 0.66355
group:sex 0.00002 0.00002 1 59.590 0.0009 0.97583
group:primer 0.10144 0.03381 3 77.758 1.2581 0.29463
sex:primer 0.14394 0.04798 3 77.756 1.7853 0.15689
group:valence 0.04225 0.04225 1 63.103 1.5722 0.21451
sex:valence 0.00623 0.00623 1 63.105 0.2319 0.63179
primer:valence 0.07182 0.02394 3 90.996 0.8908 0.44911
group:session 0.00323 0.00323 1 60.287 0.1201 0.73012
sex:session 0.03028 0.03028 1 60.287 1.1266 0.29274
primer:session 0.03227 0.01076 3 94.603 0.4002 0.75317
valence:session 0.05748 0.05748 1 95.231 2.1387 0.14692
group:sex:primer 0.00689 0.00230 3 77.756 0.0855 0.96779
group:sex:valence 0.00142 0.00142 1 63.105 0.0529 0.81882
group:primer:valence 0.07938 0.02646 3 91.002 0.9846 0.40371
sex:primer:valence 0.01803 0.00601 3 90.998 0.2236 0.87975
group:sex:session 0.00003 0.00003 1 60.288 0.0011 0.97308
group:primer:session 0.00552 0.00184 3 94.596 0.0685 0.97655
sex:primer:session 0.10752 0.03584 3 94.600 1.3336 0.26812
group:valence:session 0.00094 0.00094 1 95.231 0.0349 0.85229
sex:valence:session 0.04754 0.04754 1 95.234 1.7688 0.18671
primer:valence:session 0.02729 0.00910 3 128.064 0.3384 0.79758
group:sex:primer:valence 0.03485 0.01162 3 90.998 0.4322 0.73045
group:sex:primer:session 0.05697 0.01899 3 94.600 0.7065 0.55045
group:sex:valence:session 0.13122 0.13122 1 95.237 4.8827 0.02952 *
group:primer:valence:session 0.06535 0.02178 3 128.055 0.8105 0.49028
sex:primer:valence:session 0.10188 0.03396 3 128.060 1.2636 0.28971
group:sex:primer:valence:session 0.18826 0.06275 3 128.059 2.3349 0.07696 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Code
cat(rep('_', 100), '\n', sep = '')____________________________________________________________________________________________________Code
emmeans(apt_lmer, pairwise ~ session)Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
To enable adjustments, add the argument 'pbkrtest.limit = 9829' (or larger)
[or, globally, 'set emm_options(pbkrtest.limit = 9829)' or larger];
but be warned that this may result in large computation time and memory use.Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
To enable adjustments, add the argument 'lmerTest.limit = 9829' (or larger)
[or, globally, 'set emm_options(lmerTest.limit = 9829)' or larger];
but be warned that this may result in large computation time and memory use.NOTE: Results may be misleading due to involvement in interactions$emmeans
session emmean SE df asymp.LCL asymp.UCL
t01 2.69 0.0158 Inf 2.66 2.72
t02 2.63 0.0144 Inf 2.60 2.66
Results are averaged over the levels of: group, sex, primer, valence
Degrees-of-freedom method: asymptotic
Confidence level used: 0.95
$contrasts
contrast estimate SE df z.ratio p.value
t01 - t02 0.0578 0.00945 Inf 6.123 <.0001
Results are averaged over the levels of: group, sex, primer, valence
Degrees-of-freedom method: asymptotic Code
cat(rep('_', 100), '\n', sep = '')____________________________________________________________________________________________________Code
emmeans(apt_lmer, pairwise ~ session|sex*group*valence)Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
To enable adjustments, add the argument 'pbkrtest.limit = 9829' (or larger)
[or, globally, 'set emm_options(pbkrtest.limit = 9829)' or larger];
but be warned that this may result in large computation time and memory use.Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
To enable adjustments, add the argument 'lmerTest.limit = 9829' (or larger)
[or, globally, 'set emm_options(lmerTest.limit = 9829)' or larger];
but be warned that this may result in large computation time and memory use.NOTE: Results may be misleading due to involvement in interactions$emmeans
sex = female, group = humanity, valence = negative:
session emmean SE df asymp.LCL asymp.UCL
t01 2.70 0.0315 Inf 2.64 2.76
t02 2.62 0.0289 Inf 2.57 2.68
sex = male, group = humanity, valence = negative:
session emmean SE df asymp.LCL asymp.UCL
t01 2.65 0.0316 Inf 2.59 2.71
t02 2.62 0.0289 Inf 2.56 2.68
sex = female, group = mindfulness, valence = negative:
session emmean SE df asymp.LCL asymp.UCL
t01 2.72 0.0323 Inf 2.65 2.78
t02 2.66 0.0298 Inf 2.60 2.72
sex = male, group = mindfulness, valence = negative:
session emmean SE df asymp.LCL asymp.UCL
t01 2.69 0.0315 Inf 2.62 2.75
t02 2.64 0.0290 Inf 2.59 2.70
sex = female, group = humanity, valence = positive:
session emmean SE df asymp.LCL asymp.UCL
t01 2.70 0.0327 Inf 2.64 2.77
t02 2.64 0.0300 Inf 2.58 2.70
sex = male, group = humanity, valence = positive:
session emmean SE df asymp.LCL asymp.UCL
t01 2.67 0.0328 Inf 2.61 2.74
t02 2.60 0.0301 Inf 2.54 2.66
sex = female, group = mindfulness, valence = positive:
session emmean SE df asymp.LCL asymp.UCL
t01 2.72 0.0335 Inf 2.65 2.79
t02 2.65 0.0309 Inf 2.59 2.71
sex = male, group = mindfulness, valence = positive:
session emmean SE df asymp.LCL asymp.UCL
t01 2.68 0.0328 Inf 2.61 2.74
t02 2.63 0.0301 Inf 2.57 2.69
Results are averaged over the levels of: primer
Degrees-of-freedom method: asymptotic
Confidence level used: 0.95
$contrasts
sex = female, group = humanity, valence = negative:
contrast estimate SE df z.ratio p.value
t01 - t02 0.0780 0.0208 Inf 3.747 0.0002
sex = male, group = humanity, valence = negative:
contrast estimate SE df z.ratio p.value
t01 - t02 0.0318 0.0208 Inf 1.530 0.1261
sex = female, group = mindfulness, valence = negative:
contrast estimate SE df z.ratio p.value
t01 - t02 0.0568 0.0208 Inf 2.733 0.0063
sex = male, group = mindfulness, valence = negative:
contrast estimate SE df z.ratio p.value
t01 - t02 0.0427 0.0209 Inf 2.044 0.0410
sex = female, group = humanity, valence = positive:
contrast estimate SE df z.ratio p.value
t01 - t02 0.0637 0.0199 Inf 3.203 0.0014
sex = male, group = humanity, valence = positive:
contrast estimate SE df z.ratio p.value
t01 - t02 0.0711 0.0198 Inf 3.580 0.0003
sex = female, group = mindfulness, valence = positive:
contrast estimate SE df z.ratio p.value
t01 - t02 0.0731 0.0198 Inf 3.684 0.0002
sex = male, group = mindfulness, valence = positive:
contrast estimate SE df z.ratio p.value
t01 - t02 0.0457 0.0200 Inf 2.289 0.0221
Results are averaged over the levels of: primer
Degrees-of-freedom method: asymptotic Code
cat(rep('_', 100), '\n', sep = '')____________________________________________________________________________________________________Code
print(paste('Slope for age =', coef(summary(apt_lmer))[['age', 'Estimate']]))[1] "Slope for age = 0.00114127481896648"Code
check_model(apt_lmer)