Code
cat('\014') # clean terminalCode
rm(list = ls()) # clean workspace
library(tidyverse)
library(afex)
library(emmeans)
library(lmerTest)
# library(performance)Common Humanity & Mindfulness 2023
Explicit evaluation of primers
Code
options(mc_doScale_quiet = TRUE)
theme_set(
theme_minimal()
)Code
df_neurokit2 <- read_csv('../meditation_task/data/hrv_hrf_hra_rsa_rrv_neurokit2.csv', col_types = cols())
df_explicit <- list.files('../code_apt/data_eval', 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) |>
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)
write_csv(df_explicit, 'data/df_explicit_2023_data_clean.csv')General description
Code
summary(df_explicit) Subject duo id session sex
d01_s01_t01_m: 12 d01 : 48 s01 : 24 t01:768 female:768
d01_s01_t02_m: 12 d02 : 48 s02 : 24 t02:768 male :768
d01_s02_t01_m: 12 d03 : 48 s03 : 24
d01_s02_t02_m: 12 d04 : 48 s04 : 24
d02_s03_t01_m: 12 d05 : 48 s05 : 24
d02_s03_t02_m: 12 d06 : 48 s06 : 24
(Other) :1464 (Other):1248 (Other):1392
primer photo explicit value
it :384 20230515_161630_phone.jpg:384 connection:512 Min. :1.000
other:384 she02.jpeg :192 positivity:512 1st Qu.:3.000
you :384 IMG_8904.jpg :126 similarity:512 Median :4.000
me :384 he_02.jpeg : 60 Mean :4.205
unnamed.jpg : 36 3rd Qu.:6.000
IMG_2409.jpg : 30 Max. :7.000
(Other) :708
rt group
Min. : 230 humanity :768
1st Qu.: 2277 mindfulness:768
Median : 3398
Mean : 4377
3rd Qu.: 5192
Max. :48397
Connection
Code
explicit_connection_lmer <- lmer(value ~ group*sex*primer*session + (primer|id) + (1|duo),
subset(df_explicit, explicit == 'connection'))
afex_plot(
explicit_connection_lmer,
id = 'id',
x = 'primer',
trace = 'group',
panel = 'session',
error_arg = list(width = .4, lwd = .75),
dodge = .3,
data_arg = list(
position =
position_jitterdodge(
jitter.width = .3,
jitter.height = .1,
dodge.width = .3 ## needs to be same as dodge
)),
mapping = c('color'),
point_arg = list(size = 3)
)
Code
options(width = 120)
summary(explicit_connection_lmer)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: value ~ group * sex * primer * session + (primer | id) + (1 | duo)
Data: subset(df_explicit, explicit == "connection")
REML criterion at convergence: 1586.2
Scaled residuals:
Min 1Q Median 3Q Max
-3.14161 -0.42971 0.06754 0.44179 2.57972
Random effects:
Groups Name Variance Std.Dev. Corr
id (Intercept) 0.96075 0.9802
primerother 2.08748 1.4448 -0.52
primeryou 2.20962 1.4865 -0.54 0.73
primerme 1.71642 1.3101 -0.76 0.48 0.52
duo (Intercept) 0.05033 0.2243
Residual 0.63593 0.7975
Number of obs: 512, groups: id, 64; duo, 32
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 2.000e+00 3.257e-01 8.569e+01 6.141 2.49e-08 ***
groupmindfulness 1.444e-13 4.606e-01 8.569e+01 0.000 1.000000
sexmale 4.375e-01 4.606e-01 8.569e+01 0.950 0.344879
primerother 1.000e+00 4.582e-01 9.007e+01 2.182 0.031683 *
primeryou 1.813e+00 4.665e-01 8.871e+01 3.886 0.000196 ***
primerme 3.875e+00 4.322e-01 9.509e+01 8.966 2.65e-14 ***
sessiont02 -4.375e-01 2.819e-01 2.400e+02 -1.552 0.122044
groupmindfulness:sexmale -6.250e-02 6.514e-01 8.569e+01 -0.096 0.923788
groupmindfulness:primerother 3.750e-01 6.480e-01 9.007e+01 0.579 0.564240
groupmindfulness:primeryou -1.545e-13 6.597e-01 8.871e+01 0.000 1.000000
groupmindfulness:primerme 1.250e-01 6.112e-01 9.509e+01 0.205 0.838381
sexmale:primerother -1.375e-13 6.480e-01 9.007e+01 0.000 1.000000
sexmale:primeryou 4.375e-01 6.597e-01 8.871e+01 0.663 0.508926
sexmale:primerme 1.250e-01 6.112e-01 9.509e+01 0.205 0.838381
groupmindfulness:sessiont02 3.125e-01 3.987e-01 2.400e+02 0.784 0.433963
sexmale:sessiont02 6.250e-02 3.987e-01 2.400e+02 0.157 0.875575
primerother:sessiont02 1.125e+00 3.987e-01 2.400e+02 2.821 0.005181 **
primeryou:sessiont02 1.625e+00 3.987e-01 2.400e+02 4.075 6.25e-05 ***
primerme:sessiont02 5.625e-01 3.987e-01 2.400e+02 1.411 0.159617
groupmindfulness:sexmale:primerother 1.875e-01 9.164e-01 9.007e+01 0.205 0.838347
groupmindfulness:sexmale:primeryou -5.000e-01 9.329e-01 8.871e+01 -0.536 0.593340
groupmindfulness:sexmale:primerme 5.077e-14 8.643e-01 9.509e+01 0.000 1.000000
groupmindfulness:sexmale:sessiont02 6.250e-02 5.639e-01 2.400e+02 0.111 0.911837
groupmindfulness:primerother:sessiont02 -6.250e-01 5.639e-01 2.400e+02 -1.108 0.268807
groupmindfulness:primeryou:sessiont02 -1.875e-01 5.639e-01 2.400e+02 -0.333 0.739791
groupmindfulness:primerme:sessiont02 6.250e-02 5.639e-01 2.400e+02 0.111 0.911837
sexmale:primerother:sessiont02 -1.875e-01 5.639e-01 2.400e+02 -0.333 0.739791
sexmale:primeryou:sessiont02 -3.750e-01 5.639e-01 2.400e+02 -0.665 0.506671
sexmale:primerme:sessiont02 -1.250e-01 5.639e-01 2.400e+02 -0.222 0.824755
groupmindfulness:sexmale:primerother:sessiont02 -2.148e-14 7.975e-01 2.400e+02 0.000 1.000000
groupmindfulness:sexmale:primeryou:sessiont02 -5.625e-01 7.975e-01 2.400e+02 -0.705 0.481265
groupmindfulness:sexmale:primerme:sessiont02 -4.375e-01 7.975e-01 2.400e+02 -0.549 0.583776
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation matrix not shown by default, as p = 32 > 12.
Use print(x, correlation=TRUE) or
vcov(x) if you need itCode
cat(rep('_', 100), '\n', sep = '')____________________________________________________________________________________________________Code
anova(explicit_connection_lmer)Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
group 0.07 0.072 1 28.785 0.1126 0.73966
sex 2.39 2.390 1 28.785 3.7583 0.06241 .
primer 319.68 106.561 3 59.999 167.5656 < 2.2e-16 ***
session 15.82 15.820 1 240.001 24.8773 1.173e-06 ***
group:sex 0.20 0.199 1 28.785 0.3127 0.58033
group:primer 2.56 0.854 3 59.999 1.3424 0.26914
sex:primer 0.13 0.043 3 59.999 0.0681 0.97667
group:session 0.03 0.031 1 240.001 0.0491 0.82475
sex:session 1.32 1.320 1 240.001 2.0762 0.15092
primer:session 24.60 8.201 3 240.001 12.8952 7.708e-08 ***
group:sex:primer 1.39 0.465 3 59.999 0.7312 0.53746
group:sex:session 0.28 0.281 1 240.001 0.4423 0.50667
group:primer:session 1.95 0.651 3 240.001 1.0238 0.38275
sex:primer:session 1.85 0.617 3 240.001 0.9705 0.40729
group:sex:primer:session 0.52 0.172 3 240.001 0.2703 0.84680
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Code
cat(rep('_', 100), '\n', sep = '')____________________________________________________________________________________________________Code
emmeans(explicit_connection_lmer, pairwise ~ primer)NOTE: Results may be misleading due to involvement in interactions$emmeans
primer emmean SE df lower.CL upper.CL
it 2.09 0.147 52.1 1.79 2.38
other 3.68 0.176 56.9 3.33 4.03
you 4.59 0.178 57.0 4.24 4.95
me 6.30 0.133 48.2 6.03 6.57
Results are averaged over the levels of: group, sex, session
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95
$contrasts
contrast estimate SE df t.ratio p.value
it - other -1.594 0.206 60 -7.726 <.0001
it - you -2.508 0.211 60 -11.893 <.0001
it - me -4.211 0.192 60 -21.964 <.0001
other - you -0.914 0.168 60 -5.434 <.0001
other - me -2.617 0.203 60 -12.903 <.0001
you - me -1.703 0.199 60 -8.562 <.0001
Results are averaged over the levels of: group, sex, session
Degrees-of-freedom method: kenward-roger
P value adjustment: tukey method for comparing a family of 4 estimates Code
cat(rep('_', 100), '\n', sep = '')____________________________________________________________________________________________________Code
emmeans(explicit_connection_lmer, pairwise ~ session)NOTE: Results may be misleading due to involvement in interactions$emmeans
session emmean SE df lower.CL upper.CL
t01 3.99 0.111 42.5 3.77 4.21
t02 4.34 0.111 42.5 4.12 4.56
Results are averaged over the levels of: group, sex, primer
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95
$contrasts
contrast estimate SE df t.ratio p.value
t01 - t02 -0.352 0.0705 240 -4.988 <.0001
Results are averaged over the levels of: group, sex, primer
Degrees-of-freedom method: kenward-roger Code
cat(rep('_', 100), '\n', sep = '')____________________________________________________________________________________________________Code
emmeans(explicit_connection_lmer, pairwise ~ session|primer)NOTE: Results may be misleading due to involvement in interactions$emmeans
primer = it:
session emmean SE df lower.CL upper.CL
t01 2.20 0.163 77.9 1.88 2.53
t02 1.97 0.163 77.9 1.64 2.29
primer = other:
session emmean SE df lower.CL upper.CL
t01 3.44 0.190 76.0 3.06 3.82
t02 3.92 0.190 76.0 3.54 4.30
primer = you:
session emmean SE df lower.CL upper.CL
t01 4.11 0.191 75.9 3.73 4.49
t02 5.08 0.191 75.9 4.70 5.46
primer = me:
session emmean SE df lower.CL upper.CL
t01 6.20 0.151 77.6 5.90 6.50
t02 6.39 0.151 77.6 6.09 6.69
Results are averaged over the levels of: group, sex
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95
$contrasts
primer = it:
contrast estimate SE df t.ratio p.value
t01 - t02 0.234 0.141 240 1.663 0.0977
primer = other:
contrast estimate SE df t.ratio p.value
t01 - t02 -0.484 0.141 240 -3.436 0.0007
primer = you:
contrast estimate SE df t.ratio p.value
t01 - t02 -0.969 0.141 240 -6.872 <.0001
primer = me:
contrast estimate SE df t.ratio p.value
t01 - t02 -0.188 0.141 240 -1.330 0.1848
Results are averaged over the levels of: group, sex
Degrees-of-freedom method: kenward-roger Code
cat(rep('_', 100), '\n', sep = '')____________________________________________________________________________________________________Code
ranova(explicit_connection_lmer)ANOVA-like table for random-effects: Single term deletions
Model:
value ~ group + sex + primer + session + (primer | id) + (1 | duo) + group:sex + group:primer + sex:primer + group:session + sex:session + primer:session + group:sex:primer + group:sex:session + group:primer:session + sex:primer:session + group:sex:primer:session
npar logLik AIC LRT Df Pr(>Chisq)
<none> 44 -793.09 1674.2
primer in (primer | id) 35 -849.24 1768.5 112.292 9 <2e-16 ***
(1 | duo) 43 -793.17 1672.3 0.162 1 0.6876
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Similarity
Code
explicit_similarity_lmer <- lmer(value ~ group*sex*primer*session + (primer|id) + (1|duo),
subset(df_explicit, explicit == 'similarity'))
afex_plot(
explicit_similarity_lmer,
id = 'id',
x = 'primer',
trace = 'group',
panel = 'session',
error_arg = list(width = .4, lwd = .75),
dodge = .3,
data_arg = list(
position =
position_jitterdodge(
jitter.width = .3,
jitter.height = .1,
dodge.width = .3 ## needs to be same as dodge
)),
mapping = c('color'),
point_arg = list(size = 3)
)
Code
options(width = 140)
summary(explicit_similarity_lmer)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: value ~ group * sex * primer * session + (primer | id) + (1 | duo)
Data: subset(df_explicit, explicit == "similarity")
REML criterion at convergence: 1513
Scaled residuals:
Min 1Q Median 3Q Max
-2.96126 -0.36293 0.01547 0.41536 2.34900
Random effects:
Groups Name Variance Std.Dev. Corr
id (Intercept) 0.51312 0.7163
primerother 2.23593 1.4953 -0.56
primeryou 2.29581 1.5152 -0.57 0.84
primerme 1.24323 1.1150 -0.81 0.58 0.54
duo (Intercept) 0.07621 0.2761
Residual 0.59531 0.7716
Number of obs: 512, groups: id, 64; duo, 32
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 1.312e+00 2.807e-01 1.027e+02 4.676 8.94e-06 ***
groupmindfulness 1.250e-01 3.970e-01 1.027e+02 0.315 0.75350
sexmale 2.500e-01 3.970e-01 1.027e+02 0.630 0.53027
primerother 1.875e+00 4.628e-01 8.692e+01 4.052 0.00011 ***
primeryou 2.875e+00 4.668e-01 8.634e+01 6.159 2.25e-08 ***
primerme 4.938e+00 3.900e-01 1.025e+02 12.660 < 2e-16 ***
sessiont02 -6.250e-02 2.728e-01 2.400e+02 -0.229 0.81898
groupmindfulness:sexmale -1.250e-01 5.614e-01 1.027e+02 -0.223 0.82426
groupmindfulness:primerother 5.000e-01 6.545e-01 8.692e+01 0.764 0.44694
groupmindfulness:primeryou -1.250e-01 6.602e-01 8.634e+01 -0.189 0.85026
groupmindfulness:primerme 1.250e-01 5.516e-01 1.025e+02 0.227 0.82117
sexmale:primerother 3.750e-01 6.545e-01 8.692e+01 0.573 0.56813
sexmale:primeryou 6.250e-02 6.602e-01 8.634e+01 0.095 0.92479
sexmale:primerme -1.250e-01 5.516e-01 1.025e+02 -0.227 0.82117
groupmindfulness:sessiont02 3.125e-01 3.858e-01 2.400e+02 0.810 0.41872
sexmale:sessiont02 6.250e-02 3.858e-01 2.400e+02 0.162 0.87144
primerother:sessiont02 7.500e-01 3.858e-01 2.400e+02 1.944 0.05305 .
primeryou:sessiont02 7.500e-01 3.858e-01 2.400e+02 1.944 0.05305 .
primerme:sessiont02 -2.333e-15 3.858e-01 2.400e+02 0.000 1.00000
groupmindfulness:sexmale:primerother -5.000e-01 9.255e-01 8.692e+01 -0.540 0.59043
groupmindfulness:sexmale:primeryou -4.375e-01 9.336e-01 8.634e+01 -0.469 0.64053
groupmindfulness:sexmale:primerme 1.875e-01 7.800e-01 1.025e+02 0.240 0.81052
groupmindfulness:sexmale:sessiont02 1.250e-01 5.456e-01 2.400e+02 0.229 0.81898
groupmindfulness:primerother:sessiont02 -6.250e-01 5.456e-01 2.400e+02 -1.146 0.25311
groupmindfulness:primeryou:sessiont02 4.231e-15 5.456e-01 2.400e+02 0.000 1.00000
groupmindfulness:primerme:sessiont02 -1.250e-01 5.456e-01 2.400e+02 -0.229 0.81898
sexmale:primerother:sessiont02 -3.750e-01 5.456e-01 2.400e+02 -0.687 0.49253
sexmale:primeryou:sessiont02 3.125e-01 5.456e-01 2.400e+02 0.573 0.56733
sexmale:primerme:sessiont02 2.500e-01 5.456e-01 2.400e+02 0.458 0.64720
groupmindfulness:sexmale:primerother:sessiont02 -2.500e-01 7.716e-01 2.400e+02 -0.324 0.74621
groupmindfulness:sexmale:primeryou:sessiont02 -1.250e+00 7.716e-01 2.400e+02 -1.620 0.10653
groupmindfulness:sexmale:primerme:sessiont02 -5.000e-01 7.716e-01 2.400e+02 -0.648 0.51758
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation matrix not shown by default, as p = 32 > 12.
Use print(x, correlation=TRUE) or
vcov(x) if you need itCode
cat(rep('_', 100), '\n', sep = '')____________________________________________________________________________________________________Code
anova(explicit_similarity_lmer)Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
group 0.06 0.058 1 38.414 0.0973 0.756826
sex 0.26 0.261 1 38.414 0.4392 0.511465
primer 545.18 181.727 3 60.000 305.2632 < 2.2e-16 ***
session 14.45 14.445 1 240.000 24.2651 1.564e-06 ***
group:sex 0.93 0.926 1 38.414 1.5562 0.219774
group:primer 2.86 0.952 3 60.000 1.5997 0.198881
sex:primer 0.46 0.153 3 60.000 0.2571 0.855998
group:session 0.13 0.125 1 240.000 0.2100 0.647203
sex:session 0.20 0.195 1 240.000 0.3281 0.567326
primer:session 8.40 2.799 3 240.000 4.7025 0.003286 **
group:sex:primer 1.20 0.401 3 60.000 0.6735 0.571668
group:sex:session 1.12 1.125 1 240.000 1.8898 0.170511
group:primer:session 2.62 0.875 3 240.000 1.4698 0.223387
sex:primer:session 1.46 0.487 3 240.000 0.8180 0.485005
group:sex:primer:session 1.75 0.583 3 240.000 0.9799 0.402885
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Code
cat(rep('_', 100), '\n', sep = '')____________________________________________________________________________________________________Code
emmeans(explicit_similarity_lmer, pairwise ~ primer)NOTE: Results may be misleading due to involvement in interactions$emmeans
primer emmean SE df lower.CL upper.CL
it 1.55 0.123 53.7 1.30 1.79
other 3.83 0.177 61.4 3.47 4.18
you 4.58 0.178 61.5 4.22 4.93
me 6.50 0.119 52.3 6.26 6.74
Results are averaged over the levels of: group, sex, session
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95
$contrasts
contrast estimate SE df t.ratio p.value
it - other -2.28 0.210 60 -10.846 <.0001
it - you -3.03 0.213 60 -14.262 <.0001
it - me -4.95 0.169 60 -29.224 <.0001
other - you -0.75 0.144 60 -5.224 <.0001
other - me -2.67 0.182 60 -14.645 <.0001
you - me -1.92 0.190 60 -10.091 <.0001
Results are averaged over the levels of: group, sex, session
Degrees-of-freedom method: kenward-roger
P value adjustment: tukey method for comparing a family of 4 estimates Code
cat(rep('_', 100), '\n', sep = '')____________________________________________________________________________________________________Code
emmeans(explicit_similarity_lmer, pairwise ~ session)NOTE: Results may be misleading due to involvement in interactions$emmeans
session emmean SE df lower.CL upper.CL
t01 3.95 0.106 52.7 3.73 4.16
t02 4.28 0.106 52.7 4.07 4.49
Results are averaged over the levels of: group, sex, primer
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95
$contrasts
contrast estimate SE df t.ratio p.value
t01 - t02 -0.336 0.0682 240 -4.926 <.0001
Results are averaged over the levels of: group, sex, primer
Degrees-of-freedom method: kenward-roger Code
cat(rep('_', 100), '\n', sep = '')____________________________________________________________________________________________________Code
emmeans(explicit_similarity_lmer, pairwise ~ session|primer)NOTE: Results may be misleading due to involvement in interactions$emmeans
primer = it:
session emmean SE df lower.CL upper.CL
t01 1.47 0.140 90.1 1.19 1.75
t02 1.62 0.140 90.1 1.35 1.90
primer = other:
session emmean SE df lower.CL upper.CL
t01 3.66 0.190 80.5 3.28 4.03
t02 4.00 0.190 80.5 3.62 4.38
primer = you:
session emmean SE df lower.CL upper.CL
t01 4.20 0.191 80.4 3.82 4.58
t02 4.95 0.191 80.4 4.57 5.33
primer = me:
session emmean SE df lower.CL upper.CL
t01 6.45 0.137 90.3 6.18 6.73
t02 6.55 0.137 90.3 6.27 6.82
Results are averaged over the levels of: group, sex
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95
$contrasts
primer = it:
contrast estimate SE df t.ratio p.value
t01 - t02 -0.1562 0.136 240 -1.146 0.2531
primer = other:
contrast estimate SE df t.ratio p.value
t01 - t02 -0.3438 0.136 240 -2.520 0.0124
primer = you:
contrast estimate SE df t.ratio p.value
t01 - t02 -0.7500 0.136 240 -5.499 <.0001
primer = me:
contrast estimate SE df t.ratio p.value
t01 - t02 -0.0938 0.136 240 -0.687 0.4925
Results are averaged over the levels of: group, sex
Degrees-of-freedom method: kenward-roger Code
cat(rep('_', 100), '\n', sep = '')____________________________________________________________________________________________________Code
ranova(explicit_similarity_lmer)ANOVA-like table for random-effects: Single term deletions
Model:
value ~ group + sex + primer + session + (primer | id) + (1 | duo) + group:sex + group:primer + sex:primer + group:session + sex:session + primer:session + group:sex:primer + group:sex:session + group:primer:session + sex:primer:session + group:sex:primer:session
npar logLik AIC LRT Df Pr(>Chisq)
<none> 44 -756.52 1601.0
primer in (primer | id) 35 -823.65 1717.3 134.263 9 <2e-16 ***
(1 | duo) 43 -757.15 1600.3 1.272 1 0.2594
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Positivity
Code
explicit_positivity_lmer <- lmer(value ~ group*sex*primer*session + (primer|id) + (1|duo),
subset(df_explicit, explicit == 'positivity'))
afex_plot(
explicit_positivity_lmer,
id = 'id',
x = 'primer',
trace = 'group',
panel = 'session',
error_arg = list(width = .4, lwd = .75),
dodge = .3,
data_arg = list(
position =
position_jitterdodge(
jitter.width = .3,
jitter.height = .1,
dodge.width = .3 ## needs to be same as dodge
)),
mapping = c('color'),
point_arg = list(size = 3)
)
Code
options(width = 160)
summary(explicit_positivity_lmer)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: value ~ group * sex * primer * session + (primer | id) + (1 | duo)
Data: subset(df_explicit, explicit == "positivity")
REML criterion at convergence: 1624.4
Scaled residuals:
Min 1Q Median 3Q Max
-3.3579 -0.4251 0.0231 0.4869 2.7874
Random effects:
Groups Name Variance Std.Dev. Corr
id (Intercept) 1.879e+00 1.3709055
primerother 2.850e+00 1.6883148 -0.62
primeryou 2.481e+00 1.5750167 -0.71 0.78
primerme 2.765e+00 1.6629038 -0.71 0.63 0.76
duo (Intercept) 3.178e-07 0.0005638
Residual 6.293e-01 0.7932815
Number of obs: 512, groups: id, 64; duo, 32
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 2.8750 0.3960 78.0412 7.261 2.50e-10 ***
groupmindfulness -0.7500 0.5600 78.0412 -1.339 0.18435
sexmale -0.2500 0.5600 78.0412 -0.446 0.65652
primerother 0.8750 0.5068 82.9841 1.727 0.08795 .
primeryou 2.1250 0.4834 85.8580 4.396 3.15e-05 ***
primerme 1.1250 0.5015 83.5893 2.243 0.02752 *
sessiont02 -0.3125 0.2805 240.0003 -1.114 0.26630
groupmindfulness:sexmale 1.3125 0.7919 78.0412 1.657 0.10147
groupmindfulness:primerother 0.6250 0.7167 82.9841 0.872 0.38568
groupmindfulness:primeryou 0.8750 0.6837 85.8580 1.280 0.20404
groupmindfulness:primerme 2.0625 0.7092 83.5893 2.908 0.00466 **
sexmale:primerother 1.1875 0.7167 82.9841 1.657 0.10130
sexmale:primeryou 0.3750 0.6837 85.8580 0.549 0.58477
sexmale:primerme 1.8125 0.7092 83.5893 2.556 0.01241 *
groupmindfulness:sessiont02 0.1875 0.3966 240.0003 0.473 0.63684
sexmale:sessiont02 0.8750 0.3966 240.0003 2.206 0.02833 *
primerother:sessiont02 0.5625 0.3966 240.0003 1.418 0.15744
primeryou:sessiont02 0.8125 0.3966 240.0003 2.048 0.04160 *
primerme:sessiont02 1.1250 0.3966 240.0003 2.836 0.00495 **
groupmindfulness:sexmale:primerother -1.6250 1.0135 82.9841 -1.603 0.11267
groupmindfulness:sexmale:primeryou -1.9375 0.9669 85.8580 -2.004 0.04823 *
groupmindfulness:sexmale:primerme -3.0625 1.0030 83.5893 -3.053 0.00303 **
groupmindfulness:sexmale:sessiont02 -0.9375 0.5609 240.0003 -1.671 0.09596 .
groupmindfulness:primerother:sessiont02 0.0625 0.5609 240.0003 0.111 0.91138
groupmindfulness:primeryou:sessiont02 -0.0625 0.5609 240.0003 -0.111 0.91138
groupmindfulness:primerme:sessiont02 -0.7500 0.5609 240.0003 -1.337 0.18247
sexmale:primerother:sessiont02 -1.1250 0.5609 240.0003 -2.006 0.04602 *
sexmale:primeryou:sessiont02 -0.5625 0.5609 240.0003 -1.003 0.31697
sexmale:primerme:sessiont02 -1.2500 0.5609 240.0003 -2.228 0.02678 *
groupmindfulness:sexmale:primerother:sessiont02 0.8750 0.7933 240.0003 1.103 0.27113
groupmindfulness:sexmale:primeryou:sessiont02 0.6250 0.7933 240.0003 0.788 0.43155
groupmindfulness:sexmale:primerme:sessiont02 0.9375 0.7933 240.0003 1.182 0.23845
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation matrix not shown by default, as p = 32 > 12.
Use print(x, correlation=TRUE) or
vcov(x) if you need itoptimizer (nloptwrap) convergence code: 0 (OK)
unable to evaluate scaled gradient
Model failed to converge: degenerate Hessian with 1 negative eigenvaluesCode
cat(rep('_', 100), '\n', sep = '')____________________________________________________________________________________________________Code
anova(explicit_positivity_lmer)Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
group 0.143 0.143 1 60 0.2279 0.63484
sex 1.880 1.880 1 60 2.9873 0.08906 .
primer 103.669 34.556 3 60 54.9127 < 2.2e-16 ***
session 11.580 11.580 1 240 18.4017 2.595e-05 ***
group:sex 0.720 0.720 1 60 1.1448 0.28893
group:primer 0.818 0.273 3 60 0.4335 0.72980
sex:primer 4.394 1.465 3 60 2.3275 0.08358 .
group:session 0.861 0.861 1 240 1.3687 0.24319
sex:session 0.018 0.018 1 240 0.0279 0.86741
primer:session 7.100 2.367 3 240 3.7606 0.01147 *
group:sex:primer 5.188 1.729 3 60 2.7481 0.05060 .
group:sex:session 0.861 0.861 1 240 1.3687 0.24319
group:primer:session 2.693 0.898 3 240 1.4267 0.23560
sex:primer:session 3.256 1.085 3 240 1.7246 0.16256
group:sex:primer:session 1.100 0.367 3 240 0.5825 0.62705
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Code
cat(rep('_', 100), '\n', sep = '')____________________________________________________________________________________________________Code
emmeans(explicit_positivity_lmer, pairwise ~ primer)NOTE: Results may be misleading due to involvement in interactions$emmeans
primer emmean SE df lower.CL upper.CL
it 2.70 0.185 50.8 2.32 3.07
other 4.20 0.185 50.7 3.82 4.57
you 5.29 0.158 44.7 4.97 5.61
me 5.17 0.164 46.3 4.84 5.50
Results are averaged over the levels of: group, sex, session
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95
$contrasts
contrast estimate SE df t.ratio p.value
it - other -1.500 0.233 60 -6.433 <.0001
it - you -2.594 0.220 60 -11.766 <.0001
it - me -2.477 0.230 60 -10.753 <.0001
other - you -1.094 0.167 60 -6.532 <.0001
other - me -0.977 0.205 60 -4.765 0.0001
you - me 0.117 0.172 60 0.683 0.9032
Results are averaged over the levels of: group, sex, session
Degrees-of-freedom method: kenward-roger
P value adjustment: tukey method for comparing a family of 4 estimates Code
cat(rep('_', 100), '\n', sep = '')____________________________________________________________________________________________________Code
emmeans(explicit_positivity_lmer, pairwise ~ session)NOTE: Results may be misleading due to involvement in interactions$emmeans
session emmean SE df lower.CL upper.CL
t01 4.19 0.124 34.1 3.94 4.44
t02 4.49 0.124 34.1 4.24 4.74
Results are averaged over the levels of: group, sex, primer
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95
$contrasts
contrast estimate SE df t.ratio p.value
t01 - t02 -0.301 0.0701 240 -4.290 <.0001
Results are averaged over the levels of: group, sex, primer
Degrees-of-freedom method: kenward-roger Code
cat(rep('_', 100), '\n', sep = '')____________________________________________________________________________________________________Code
emmeans(explicit_positivity_lmer, pairwise ~ session|primer)NOTE: Results may be misleading due to involvement in interactions$emmeans
primer = it:
session emmean SE df lower.CL upper.CL
t01 2.70 0.198 66.1 2.31 3.10
t02 2.69 0.198 66.1 2.29 3.08
primer = other:
session emmean SE df lower.CL upper.CL
t01 4.08 0.197 66.1 3.68 4.47
t02 4.31 0.197 66.1 3.92 4.71
primer = you:
session emmean SE df lower.CL upper.CL
t01 4.97 0.173 63.6 4.62 5.31
t02 5.61 0.173 63.6 5.26 5.96
primer = me:
session emmean SE df lower.CL upper.CL
t01 5.00 0.178 64.4 4.64 5.36
t02 5.34 0.178 64.4 4.99 5.70
Results are averaged over the levels of: group, sex
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95
$contrasts
primer = it:
contrast estimate SE df t.ratio p.value
t01 - t02 0.0156 0.14 240 0.111 0.9114
primer = other:
contrast estimate SE df t.ratio p.value
t01 - t02 -0.2344 0.14 240 -1.671 0.0960
primer = you:
contrast estimate SE df t.ratio p.value
t01 - t02 -0.6406 0.14 240 -4.568 <.0001
primer = me:
contrast estimate SE df t.ratio p.value
t01 - t02 -0.3438 0.14 240 -2.451 0.0149
Results are averaged over the levels of: group, sex
Degrees-of-freedom method: kenward-roger Code
cat(rep('_', 100), '\n', sep = '')____________________________________________________________________________________________________Code
ranova(explicit_positivity_lmer)ANOVA-like table for random-effects: Single term deletions
Model:
value ~ group + sex + primer + session + (primer | id) + (1 | duo) + group:sex + group:primer + sex:primer + group:session + sex:session + primer:session + group:sex:primer + group:sex:session + group:primer:session + sex:primer:session + group:sex:primer:session
npar logLik AIC LRT Df Pr(>Chisq)
<none> 44 -812.22 1712.5
primer in (primer | id) 35 -873.61 1817.2 122.77 9 <2e-16 ***
(1 | duo) 43 -812.22 1710.5 0.00 1 0.9995
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1