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model_LongitudinalMediation.R
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# bind with wave2 ---------------------------------------------------------
library(tidyverse)
cortical.fs$ID = as.character(cortical.fs$ID)
cortical.long <- ses_wave2_data %>%
dplyr::select(-contains('_1')) %>%
dplyr::select(number, general_2:executive_2) %>%
mutate(number = as.character(number)) %>%
left_join(., cortical.fs, by = c('number' = 'ID')) %>%
filter(!is.na(EstimatedTotalIntraCranialVol))
# missing data imputation -------------------------------------------------
library(mice)
summary(cortical.long[,2:9]) # check missing number of beh data
# general: 1
# em: 13
# wm: 4
# logical: 252
# spatial: 11
# language: 189
# attention: 18
# executive: 20
data.to.mice <- cortical.long %>%
dplyr::select(general_2:executive_2) # cognitive domains have missing value for mice to do multiple impute
md.pattern(data.to.mice) # check the feature of missing data
# multiple impute
tmpdata <- mice(data.to.mice, m = 5, maxit = 50, seed = 123)
summary(tmpdata) # PredictorMatrix is the predictor variable matrix
# the first line: general has missing data, use the other variavles "1" for imputation
tmpdata$imp$INTELLECTURAL # line94 was missing in general, five imputation give five values
# returen complete dataset
data.complete <- complete(tmpdata, action = 5) # action: choose which time of imputation
summary(data.complete)
densityplot(tmpdata) # blue is the original data, red are imputations
# choose imputations
densityplot(tmpdata, ~ em_2 + logical_2 + spatial_2 + language_2 | .imp)
data.complete <- complete(tmpdata, action = 3)
# combine data: y and 8 cognitive domains
cortical.long <- as_tibble(cbind(
data.complete, cortical.long[, c(1, 10:299)]))
write.csv(cortical.long, file = './derivatives/FS_SES_L_543_IMPUTE.csv',
row.names = F)
# ses-leisure-volume med ----------------------------------------------------
library(lavaan)
# sacle intellectual and _volume data
columns.to.scale <- ends_with('_volumn') | between(seq_along(cortical.long, 246, 299))
cortical.long.scaled <- cortical.long %>%
mutate_at(vars(ends_with('_volume'),
'INTELLECTURAL', 'PHYSICAL', 'SOCIAL',
'Left-Lateral-Ventricle':'EstimatedTotalIntraCranialVol'),
scale) %>%
write.csv(
file = './derivatives/FS_SES_L_543_IMPUTE_SCALE.csv',
row.names = F)
library(mediation)
cortical.long.scaled <- list(cortical.long.scaled = cortical.long.scaled)
mediators.leisure <- c('INTELLECTURAL', 'PHYSICAL', 'SOCIAL')
med <- mediations(datasets = cortical.long.scaled, treatment = 'SES',
mediators = mediators.leisure,
outcome = mediators.roi, covariates = covars.tmp,
conf.level = 0.95, sims = 1000)
## select each mediation coeff from models
# empty tibble of int
med.intellectual.result <- tibble(
roi = character(),
medeff = numeric(),
medlow = numeric(),
medup = numeric(),
medp = numeric(),
deff = numeric(),
dlow = numeric(),
dup = numeric(),
dp = numeric(),
teff = numeric(),
tlow = numeric(),
tup = numeric(),
tp = numeric(),
prop = numeric(),
proplow = numeric(),
propup = numeric(),
propp = numeric()
)
write.csv(med.intellectual.result,
file = './derivatives/medresults_intellectual.csv',
row.names = FALSE)
# empty tibble of physical
med.physical.result <- tibble(
roi = character(),
medeff = numeric(),
medlow = numeric(),
medup = numeric(),
medp = numeric(),
deff = numeric(),
dlow = numeric(),
dup = numeric(),
dp = numeric(),
teff = numeric(),
tlow = numeric(),
tup = numeric(),
tp = numeric(),
prop = numeric(),
proplow = numeric(),
propup = numeric(),
propp = numeric()
)
write.csv(med.physical.result,
file = './derivatives/medresults_physical.csv',
row.names = FALSE)
# empty tibble of social
med.social.result <- tibble(
roi = character(),
medeff = numeric(),
medlow = numeric(),
medup = numeric(),
medp = numeric(),
deff = numeric(),
dlow = numeric(),
dup = numeric(),
dp = numeric(),
teff = numeric(),
tlow = numeric(),
tup = numeric(),
tp = numeric(),
prop = numeric(),
proplow = numeric(),
propup = numeric(),
propp = numeric()
)
write.csv(med.social.result,
file = './derivatives/medresults_social.csv',
row.names = FALSE)
for (i in 1:length(med)) {
if(med[[i]]$mediator == 'INTELLECTURAL'){
roi.results <- tibble(
roi = DKROI[(i+2)/3],
medeff = med[[i]]$d.avg,
medlow = med[[i]]$d.avg.ci[[1]],
medup = med[[i]]$d.avg.ci[[2]],
medp = med[[i]]$d.avg.p,
deff = med[[i]]$z.avg,
dlow = med[[i]]$z.avg.ci[[1]],
dup = med[[i]]$z.avg.ci[[2]],
dp = med[[i]]$z.avg.p,
teff = med[[i]]$tau.coef,
tlow = med[[i]]$tau.ci[[1]],
tup = med[[i]]$tau.ci[[2]],
tp = med[[i]]$tau.p,
prop = med[[i]]$n.avg,
proplow = med[[i]]$n.avg.ci[[1]],
propup = med[[i]]$d.avg.ci[[2]],
propp = med[[i]]$d.avg.p
)
med.intellectual.result <- read.csv('./derivatives/medresults_intellectual.csv') %>%
rbind(roi.results) %>%
write.csv(file = './derivatives/medresults_intellectual.csv',
row.names = FALSE)
}
else if(med[[i]]$mediator == 'PHYSICAL'){
roi.results <- tibble(
roi = DKROI[(i+1)/3],
medeff = med[[i]]$d.avg,
medlow = med[[i]]$d.avg.ci[[1]],
medup = med[[i]]$d.avg.ci[[2]],
medp = med[[i]]$d.avg.p,
deff = med[[i]]$z.avg,
dlow = med[[i]]$z.avg.ci[[1]],
dup = med[[i]]$z.avg.ci[[2]],
dp = med[[i]]$z.avg.p,
teff = med[[i]]$tau.coef,
tlow = med[[i]]$tau.ci[[1]],
tup = med[[i]]$tau.ci[[2]],
tp = med[[i]]$tau.p,
prop = med[[i]]$n.avg,
proplow = med[[i]]$n.avg.ci[[1]],
propup = med[[i]]$d.avg.ci[[2]],
propp = med[[i]]$d.avg.p
)
med.physical.result <- read.csv('./derivatives/medresults_physical.csv') %>%
rbind(roi.results) %>%
write.csv(file = './derivatives/medresults_physical.csv',
row.names = FALSE)
}
else{
roi.results <- tibble(
roi = DKROI[(i/3)],
medeff = med[[i]]$d.avg,
medlow = med[[i]]$d.avg.ci[[1]],
medup = med[[i]]$d.avg.ci[[2]],
medp = med[[i]]$d.avg.p,
deff = med[[i]]$z.avg,
dlow = med[[i]]$z.avg.ci[[1]],
dup = med[[i]]$z.avg.ci[[2]],
dp = med[[i]]$z.avg.p,
teff = med[[i]]$tau.coef,
tlow = med[[i]]$tau.ci[[1]],
tup = med[[i]]$tau.ci[[2]],
tp = med[[i]]$tau.p,
prop = med[[i]]$n.avg,
proplow = med[[i]]$n.avg.ci[[1]],
propup = med[[i]]$d.avg.ci[[2]],
propp = med[[i]]$d.avg.p
)
med.social.result <- read.csv('./derivatives/medresults_social.csv') %>%
rbind(roi.results) %>%
write.csv(file = './derivatives/medresults_social.csv',
row.names = FALSE)
}
}
# fdr correction of mediation results
med.social.result <- read.csv('./derivatives/medresults_social.csv') %>%
mutate(medpfdr = p.adjust(medp, method = 'fdr'),
proppfdr = p.adjust(propp, method = 'fdr')
)
# plot --------------------------------------------------------------------
#
# med.scico <- scico(3, palette = 'lajolla', begin = 0.2, end = 0.7,
# alpha = 0.8, direction = 1)
# scico(3, palette = 'lajolla', begin = 0, end = 1,
# alpha = 0.8, direction = 1)
# "#F6D868CC" "#E48551CC" "#A04543CC"
med.physical.plot <- tibble(label = dk$data$label) %>%
left_join(., dplyr::select(med.physical.result,
roi, medeff, medp, medpfdr, teff, tp,
prop, propp, proppfdr),
by = c('label' = 'roi')) %>%
filter(!is.na(label)) %>%
subset(medp <= 0.05) %>%
subset(prop > 0) %>%
slice(rep(1:n(), times = 2)) %>%
mutate(side = rep(c('lateral', 'medial'),
each = nrow(.)/2))
# med.scico <- scico(3, palette = 'lajolla', begin = 0.2, end = 0.7,
# alpha = 0.8, direction = 1)
# scico(3, palette = 'lajolla', begin = 0, end = 1,
# alpha = 0.8, direction = 1)
# "#F6D868CC" "#E48551CC" "#A04543CC"
# ses-intellectual-brain, with fdr results
med.intellectual.plot <- tibble(label = dk$data$label) %>%
left_join(., dplyr::select(med.intellectual.result,
roi, medeff, medp, medpfdr, teff, tp,
prop, propp, proppfdr),
by = c('label' = 'roi')) %>%
filter(!is.na(label)) %>%
subset(medp <= 0.05 | tp <= 0.05) %>%
subset(prop > 0)
med.social.plot %>%
ggseg(mapping = aes(fill = prop*100),
color = 'black', size = 0.65,
position = 'dispersed',) +
scale_fill_gradient2(low = "#F6D868CC", mid = "#E48551CC", high = "#A04543CC",
midpoint = mean(range(10, 90)),
breaks = seq(10, 90, 20),
limits = c(10, 90),
na.value = 'lightgrey') +
# scale_fill_scico(palette = 'lajolla',
# alpha = 0.8,
# begin = 0.2,
# end = 0.7,
# direction = 1,
# na.value = 'lightgrey') +
labs(title = 'social support',
fill = '% Mediated') +
theme_classic() +
theme(axis.title.x = element_blank(),
axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
axis.line.x = element_blank(),
axis.title.y = element_blank(),
axis.text.y = element_blank(),
axis.line.y = element_blank(),
plot.title = element_text(size = 12, hjust = 0),
plot.caption = element_text(size = 12, hjust = 0),
legend.text = element_text(size = 8),
legend.title = element_text(size = 10, hjust = 0))
# ses-leisure-aseg med ----------------------------------------------------
# library(lavaan)
# sacle intellectual and _volume data
# columns.to.scale <- ends_with('_volumn') | between(seq_along(cortical.long, 246, 299))
#
# cortical.long.scaled <- cortical.long %>%
# mutate_at(vars(ends_with('_volume'), 'Left-Lateral-Ventricle':'EstimatedTotalIntraCranialVol'),
# scale)
# cortical.long$INTELLECTURAL ~ a1 * cortical.long$SES
# cortical.long$lh_bankssts_area ~ a2 * cortical.long$SES + d21 * cortical.long$INTELLECTURAL
# cortical.long$general_2 ~ cp * cortical.long$SES + b1 * cortical.long$INTELLECTURAL + b2 * cortical.long$lh_bankssts_area
#
# indeff := a1 * d21 * b2
#
# model <- '
#
# INTELLECTURAL ~ a1 * SES
# lh_bankssts_area ~ a2 * SES + d21 * INTELLECTURAL
# general_2 ~ cp * SES + b1 * INTELLECTURAL + b2 * lh_bankssts_area
#
# indeff := a1 * d21 * b2
#
# '
# fit <- sem(model = model, data = cortical.long, se = 'boot', bootstrap = 1000)
# parameterEstimates(fit, boot.ci.type = 'bca.simple')
library(mediation)
# cortical.long.scaled <- list(cortical.long.scaled = cortical.long.scaled)
# mediators.leisure <- c('INTELLECTURAL', 'PHYSICAL', 'SOCIAL')
cortical.long.scaled <- read.csv('./derivatives/FS_SES_L_543_IMPUTE_SCALE.csv')
cortical.long.scaled <- list(cortical.long.scaled = cortical.long.scaled)
med.aseg <- mediations(datasets = cortical.long.scaled, treatment = 'SES',
mediators = mediators.leisure,
outcome = mediators.aseg, covariates = covars.tmp,
conf.level = 0.95, sims = 1000)
## select each mediation coeff from models
# empty tibble of int
med.aseg.intellectual.result <- tibble(
roi = character(),
medeff = numeric(),
medlow = numeric(),
medup = numeric(),
medp = numeric(),
deff = numeric(),
dlow = numeric(),
dup = numeric(),
dp = numeric(),
teff = numeric(),
tlow = numeric(),
tup = numeric(),
tp = numeric(),
prop = numeric(),
proplow = numeric(),
propup = numeric(),
propp = numeric()
)
write.csv(med.aseg.intellectual.result,
file = './derivatives/medaseg_intellectual.csv',
row.names = FALSE)
# empty tibble of physical
med.aseg.physical.result <- tibble(
roi = character(),
medeff = numeric(),
medlow = numeric(),
medup = numeric(),
medp = numeric(),
deff = numeric(),
dlow = numeric(),
dup = numeric(),
dp = numeric(),
teff = numeric(),
tlow = numeric(),
tup = numeric(),
tp = numeric(),
prop = numeric(),
proplow = numeric(),
propup = numeric(),
propp = numeric()
)
write.csv(med.aseg.physical.result,
file = './derivatives/medaseg_physical.csv',
row.names = FALSE)
# empty tibble of social
med.aseg.social.result <- tibble(
roi = character(),
medeff = numeric(),
medlow = numeric(),
medup = numeric(),
medp = numeric(),
deff = numeric(),
dlow = numeric(),
dup = numeric(),
dp = numeric(),
teff = numeric(),
tlow = numeric(),
tup = numeric(),
tp = numeric(),
prop = numeric(),
proplow = numeric(),
propup = numeric(),
propp = numeric()
)
write.csv(med.aseg.social.result,
file = './derivatives/medaseg_social.csv',
row.names = FALSE)
for (i in 1:length(med.aseg)) {
if(med.aseg[[i]]$mediator == 'INTELLECTURAL'){
roi.results <- tibble(
roi = mediators.aseg[(i+2)/3],
medeff = med.aseg[[i]]$d.avg,
medlow = med.aseg[[i]]$d.avg.ci[[1]],
medup = med.aseg[[i]]$d.avg.ci[[2]],
medp = med.aseg[[i]]$d.avg.p,
deff = med.aseg[[i]]$z.avg,
dlow = med.aseg[[i]]$z.avg.ci[[1]],
dup = med.aseg[[i]]$z.avg.ci[[2]],
dp = med.aseg[[i]]$z.avg.p,
teff = med.aseg[[i]]$tau.coef,
tlow = med.aseg[[i]]$tau.ci[[1]],
tup = med.aseg[[i]]$tau.ci[[2]],
tp = med.aseg[[i]]$tau.p,
prop = med.aseg[[i]]$n.avg,
proplow = med.aseg[[i]]$n.avg.ci[[1]],
propup = med.aseg[[i]]$d.avg.ci[[2]],
propp = med.aseg[[i]]$d.avg.p
)
med.aseg.intellectual.result <- read.csv('./derivatives/medaseg_intellectual.csv') %>%
rbind(roi.results) %>%
write.csv(file = './derivatives/medaseg_intellectual.csv',
row.names = FALSE)
}
else if(med.aseg[[i]]$mediator == 'PHYSICAL'){
roi.results <- tibble(
roi = mediators.aseg[(i+1)/3],
medeff = med.aseg[[i]]$d.avg,
medlow = med.aseg[[i]]$d.avg.ci[[1]],
medup = med.aseg[[i]]$d.avg.ci[[2]],
medp = med.aseg[[i]]$d.avg.p,
deff = med.aseg[[i]]$z.avg,
dlow = med.aseg[[i]]$z.avg.ci[[1]],
dup = med.aseg[[i]]$z.avg.ci[[2]],
dp = med.aseg[[i]]$z.avg.p,
teff = med.aseg[[i]]$tau.coef,
tlow = med.aseg[[i]]$tau.ci[[1]],
tup = med.aseg[[i]]$tau.ci[[2]],
tp = med.aseg[[i]]$tau.p,
prop = med.aseg[[i]]$n.avg,
proplow = med.aseg[[i]]$n.avg.ci[[1]],
propup = med.aseg[[i]]$d.avg.ci[[2]],
propp = med.aseg[[i]]$d.avg.p
)
med.aseg.physical.result <- read.csv('./derivatives/medaseg_physical.csv') %>%
rbind(roi.results) %>%
write.csv(file = './derivatives/medaseg_physical.csv',
row.names = FALSE)
}
else{
roi.results <- tibble(
roi = mediators.aseg[(i/3)],
medeff = med.aseg[[i]]$d.avg,
medlow = med.aseg[[i]]$d.avg.ci[[1]],
medup = med.aseg[[i]]$d.avg.ci[[2]],
medp = med.aseg[[i]]$d.avg.p,
deff = med.aseg[[i]]$z.avg,
dlow = med.aseg[[i]]$z.avg.ci[[1]],
dup = med.aseg[[i]]$z.avg.ci[[2]],
dp = med.aseg[[i]]$z.avg.p,
teff = med.aseg[[i]]$tau.coef,
tlow = med.aseg[[i]]$tau.ci[[1]],
tup = med.aseg[[i]]$tau.ci[[2]],
tp = med.aseg[[i]]$tau.p,
prop = med.aseg[[i]]$n.avg,
proplow = med.aseg[[i]]$n.avg.ci[[1]],
propup = med.aseg[[i]]$d.avg.ci[[2]],
propp = med.aseg[[i]]$d.avg.p
)
med.aseg.social.result <- read.csv('./derivatives/medaseg_social.csv') %>%
rbind(roi.results) %>%
write.csv(file = './derivatives/medaseg_social.csv',
row.names = FALSE)
}
}
# fdr correction of mediation results
med.aseg.intellectual.result <- read.csv('./derivatives/medaseg_intellectual.csv') %>%
mutate(medpfdr = p.adjust(medp, method = 'fdr'),
proppfdr = p.adjust(propp, method = 'fdr')
)
# aseg plot ---------------------------------------------------------------
med.aseg.social.plot <- med.aseg.social.result %>%
mutate(roi = str_replace_all(roi, '\\.', '-')) %>%
mutate(roi = ifelse(row_number() == 9, 'x3rd-ventricle', roi),
roi = ifelse(row_number() == 10, 'x4th-ventricle', roi)) %>%
left_join(tibble(label = aseg$data$label), ., by = c('label' = 'roi')) %>%
dplyr::select(label, medeff, medp, medpfdr,
prop, propp, proppfdr) %>%
filter(!is.na(label)) %>%
subset(medp <= 0.05)
# slice(rep(1:n(), times = 2)) %>%
# mutate(side = rep(c('lateral', 'medial'),
# each = nrow(.)/2))
# med.scico <- scico(3, palette = 'lajolla', begin = 0.2, end = 0.7,
# alpha = 0.8, direction = 1)
# scico(3, palette = 'lajolla', begin = 0, end = 1,
# alpha = 0.8, direction = 1)
# "#F6D868CC" "#E48551CC" "#A04543CC"
med.aseg.intellectual.plot %>%
ggseg(atlas = 'aseg',
mapping = aes(fill = prop*100),
color = 'black', size = 0.65,
position = 'dispersed',) +
scale_fill_gradient2(low = "#F6D868CC", mid = "#E48551CC", high = "#A04543CC",
midpoint = mean(range(20, 60)),
breaks = seq(20, 60, 20),
limits = c(20, 60),
na.value = 'lightgrey') +
# scale_fill_scico(palette = 'lajolla',
# alpha = 0.8,
# begin = 0.2,
# end = 0.7,
# direction = 1,
# na.value = 'lightgrey') +
labs(
fill = '% Mediated') +
theme_classic() +
theme(axis.title.x = element_blank(),
axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
axis.line.x = element_blank(),
axis.title.y = element_blank(),
axis.text.y = element_blank(),
axis.line.y = element_blank(),
plot.title = element_blank(),
plot.caption = element_text(size = 12, hjust = 0),
legend.text = element_text(size = 8),
legend.title = element_text(size = 10, hjust = 0))
# ses-volume-cog_2 med ----------------------------------------------------
# general -----------------------------------------------------------------
med.general <- mediations(datasets = cortical.long.scaled, treatment = 'SES',
mediators = mediators.roi,
outcome = 'general_2', covariates = covars.tmp,
conf.level = 0.95, sims = 1000)
med.general.result <- tibble(
roi = character(),
medeff = numeric(),
medlow = numeric(),
medup = numeric(),
medp = numeric(),
deff = numeric(),
dlow = numeric(),
dup = numeric(),
dp = numeric(),
teff = numeric(),
tlow = numeric(),
tup = numeric(),
tp = numeric(),
prop = numeric(),
proplow = numeric(),
propup = numeric(),
propp = numeric()
)
write.csv(med.general.result, file = './derivatives/LongitudinalMediation/medresults_general.csv',
row.names = FALSE)
for (i in 1:length(med.general)) {
roi.results <- tibble(
roi = DKROI[i],
medeff = med.general[[i]]$d.avg,
medlow = med.general[[i]]$d.avg.ci[[1]],
medup = med.general[[i]]$d.avg.ci[[2]],
medp = med.general[[i]]$d.avg.p,
deff = med.general[[i]]$z.avg,
dlow = med.general[[i]]$z.avg.ci[[1]],
dup = med.general[[i]]$z.avg.ci[[2]],
dp = med.general[[i]]$z.avg.p,
teff = med.general[[i]]$tau.coef,
tlow = med.general[[i]]$tau.ci[[1]],
tup = med.general[[i]]$tau.ci[[2]],
tp = med.general[[i]]$tau.p,
prop = med.general[[i]]$n.avg,
proplow = med.general[[i]]$n.avg.ci[[1]],
propup = med.general[[i]]$d.avg.ci[[2]],
propp = med.general[[i]]$d.avg.p
)
med.general.result <- read.csv('./derivatives/LongitudinalMediation/medresults_general.csv') %>%
rbind(roi.results) %>%
write.csv(file = './derivatives/LongitudinalMediation/medresults_general.csv',
row.names = FALSE)
}
# fdr correction of mediation results
med.general.result <- read.csv('./derivatives/LongitudinalMediation/medresults_general.csv') %>%
mutate(medpfdr = p.adjust(medp, method = 'fdr'),
proppfdr = p.adjust(propp, method = 'fdr')
)
# episodic ----------------------------------------------------------------
med.episodic <- mediations(datasets = cortical.long.scaled, treatment = 'SES',
mediators = mediators.roi,
outcome = 'em_2', covariates = covars.tmp,
conf.level = 0.95, sims = 1000)
med.episodic.result <- tibble(
roi = character(),
medeff = numeric(),
medlow = numeric(),
medup = numeric(),
medp = numeric(),
deff = numeric(),
dlow = numeric(),
dup = numeric(),
dp = numeric(),
teff = numeric(),
tlow = numeric(),
tup = numeric(),
tp = numeric(),
prop = numeric(),
proplow = numeric(),
propup = numeric(),
propp = numeric()
)
write.csv(med.episodic.result, file = './derivatives/LongitudinalMediation/medresults_episodic.csv',
row.names = FALSE)
for (i in 1:length(med.episodic)) {
roi.results <- tibble(
roi = DKROI[i],
medeff = med.episodic[[i]]$d.avg,
medlow = med.episodic[[i]]$d.avg.ci[[1]],
medup = med.episodic[[i]]$d.avg.ci[[2]],
medp = med.episodic[[i]]$d.avg.p,
deff = med.episodic[[i]]$z.avg,
dlow = med.episodic[[i]]$z.avg.ci[[1]],
dup = med.episodic[[i]]$z.avg.ci[[2]],
dp = med.episodic[[i]]$z.avg.p,
teff = med.episodic[[i]]$tau.coef,
tlow = med.episodic[[i]]$tau.ci[[1]],
tup = med.episodic[[i]]$tau.ci[[2]],
tp = med.episodic[[i]]$tau.p,
prop = med.episodic[[i]]$n.avg,
proplow = med.episodic[[i]]$n.avg.ci[[1]],
propup = med.episodic[[i]]$d.avg.ci[[2]],
propp = med.episodic[[i]]$d.avg.p
)
med.episodic.result <- read.csv(
'./derivatives/LongitudinalMediation/medresults_episodic.csv') %>%
rbind(roi.results) %>%
write.csv(file = './derivatives/LongitudinalMediation/medresults_episodic.csv',
row.names = FALSE)
}
# fdr correction of mediation results
med.episodic.result <- read.csv(
'./derivatives/LongitudinalMediation/medresults_episodic.csv') %>%
mutate(medpfdr = p.adjust(medp, method = 'fdr'),
proppfdr = p.adjust(propp, method = 'fdr')
)
# working -----------------------------------------------------------------
med.working <- mediations(datasets = cortical.long.scaled, treatment = 'SES',
mediators = mediators.roi,
outcome = 'wm_2', covariates = covars.tmp,
conf.level = 0.95, sims = 1000)
med.working.result <- tibble(
roi = character(),
medeff = numeric(),
medlow = numeric(),
medup = numeric(),
medp = numeric(),
deff = numeric(),
dlow = numeric(),
dup = numeric(),
dp = numeric(),
teff = numeric(),
tlow = numeric(),
tup = numeric(),
tp = numeric(),
prop = numeric(),
proplow = numeric(),
propup = numeric(),
propp = numeric()
)
write.csv(med.working.result, file = './derivatives/LongitudinalMediation/medresults_working.csv',
row.names = FALSE)
for (i in 1:length(med.working)) {
roi.results <- tibble(
roi = DKROI[i],
medeff = med.working[[i]]$d.avg,
medlow = med.working[[i]]$d.avg.ci[[1]],
medup = med.working[[i]]$d.avg.ci[[2]],
medp = med.working[[i]]$d.avg.p,
deff = med.working[[i]]$z.avg,
dlow = med.working[[i]]$z.avg.ci[[1]],
dup = med.working[[i]]$z.avg.ci[[2]],
dp = med.working[[i]]$z.avg.p,
teff = med.working[[i]]$tau.coef,
tlow = med.working[[i]]$tau.ci[[1]],
tup = med.working[[i]]$tau.ci[[2]],
tp = med.working[[i]]$tau.p,
prop = med.working[[i]]$n.avg,
proplow = med.working[[i]]$n.avg.ci[[1]],
propup = med.working[[i]]$d.avg.ci[[2]],
propp = med.working[[i]]$d.avg.p
)
med.working.result <- read.csv('./derivatives/LongitudinalMediation/medresults_working.csv') %>%
rbind(roi.results) %>%
write.csv(file = './derivatives/LongitudinalMediation/medresults_working.csv',
row.names = FALSE)
}
# fdr correction of mediation results
med.working.result <- read.csv('./derivatives/LongitudinalMediation/medresults_working.csv') %>%
mutate(medpfdr = p.adjust(medp, method = 'fdr'),
proppfdr = p.adjust(propp, method = 'fdr')
)
# logic -------------------------------------------------------------------
med.logic <- mediations(datasets = cortical.long.scaled, treatment = 'SES',
mediators = mediators.roi,
outcome = 'logical_2', covariates = covars.tmp,
conf.level = 0.95, sims = 1000)
med.logic.result <- tibble(
roi = character(),
medeff = numeric(),
medlow = numeric(),
medup = numeric(),
medp = numeric(),
deff = numeric(),
dlow = numeric(),
dup = numeric(),
dp = numeric(),
teff = numeric(),
tlow = numeric(),
tup = numeric(),
tp = numeric(),
prop = numeric(),
proplow = numeric(),
propup = numeric(),
propp = numeric()
)
write.csv(med.logic.result, file = './derivatives/LongitudinalMediation/medresults_logic.csv',
row.names = FALSE)
for (i in 1:length(med.logic)) {
roi.results <- tibble(
roi = DKROI[i],
medeff = med.logic[[i]]$d.avg,
medlow = med.logic[[i]]$d.avg.ci[[1]],
medup = med.logic[[i]]$d.avg.ci[[2]],
medp = med.logic[[i]]$d.avg.p,
deff = med.logic[[i]]$z.avg,
dlow = med.logic[[i]]$z.avg.ci[[1]],
dup = med.logic[[i]]$z.avg.ci[[2]],
dp = med.logic[[i]]$z.avg.p,
teff = med.logic[[i]]$tau.coef,
tlow = med.logic[[i]]$tau.ci[[1]],
tup = med.logic[[i]]$tau.ci[[2]],
tp = med.logic[[i]]$tau.p,
prop = med.logic[[i]]$n.avg,
proplow = med.logic[[i]]$n.avg.ci[[1]],
propup = med.logic[[i]]$d.avg.ci[[2]],
propp = med.logic[[i]]$d.avg.p
)
med.logic.result <- read.csv('./derivatives/LongitudinalMediation/medresults_logic.csv') %>%
rbind(roi.results) %>%
write.csv(file = './derivatives/LongitudinalMediation/medresults_logic.csv',
row.names = FALSE)
}
# fdr correction of mediation results
med.logic.result <- read.csv('./derivatives/LongitudinalMediation/medresults_logic.csv') %>%
mutate(medpfdr = p.adjust(medp, method = 'fdr'),
proppfdr = p.adjust(propp, method = 'fdr')
)
# spatial -----------------------------------------------------------------
med.spatial <- mediations(datasets = cortical.long.scaled, treatment = 'SES',
mediators = mediators.roi,
outcome = 'spatial_2', covariates = covars.tmp,
conf.level = 0.95, sims = 1000)
med.spatial.result <- tibble(
roi = character(),
medeff = numeric(),
medlow = numeric(),