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Copy pathmodel_CogOnBrainOnSES.R
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model_CogOnBrainOnSES.R
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# all ses participants with FS meas: cortical.fs
cortical.fs <- read.csv('./derivatives/FS_SES_841_IMPUTE.csv') %>%
rename_all(~ gsub('\\.', '-', .))
library(tidyverse)
# eTIV
eticv.model <- aov(EstimatedTotalIntraCranialVol ~ NEWSESGROUP +
AGE + GENDER + HPT + DIABETES + HPL,
data = cortical.fs)
summary(eticv.model)
emmeans::emmeans(eticv.model, specs = pairwise ~ NEWSESGROUP, adjust = 'Tukey')
# subcortical GMV
subgmv.model <- aov(SubCortGrayVol ~ NEWSESGROUP +
AGE + GENDER + HPT + DIABETES + HPL,
data = cortical.fs)
summary(subgmv.model)
emmeans::emmeans(subgmv.model, specs = pairwise ~ NEWSESGROUP, adjust = 'Tukey')
# cortex volume
cortex.model <- aov(CortexVol ~ NEWSESGROUP +
AGE + GENDER + HPT + DIABETES + HPL,
data = cortical.fs)
summary(cortex.model)
emmeans::emmeans(cortex.model, specs = pairwise ~ NEWSESGROUP, adjust = 'Tukey')
lhcortex.model <- aov(lhCortexVol ~ NEWSESGROUP +
AGE + GENDER + HPT + DIABETES + HPL,
data = cortical.fs)
summary(lhcortex.model)
emmeans::emmeans(lhcortex.model, specs = pairwise ~ NEWSESGROUP, adjust = 'Tukey')
rhcortex.model <- aov(rhCortexVol ~ NEWSESGROUP +
AGE + GENDER + HPT + DIABETES + HPL,
data = cortical.fs)
summary(rhcortex.model)
emmeans::emmeans(rhcortex.model, specs = pairwise ~ NEWSESGROUP, adjust = 'Tukey')
# cortical.fs is the final participants pool
# thickness ---------------------------------------------------------------
DKROI <- gsub('_thickness', '', DK)
thickness.model <- cortical.fs %>%
dplyr::select(ends_with('thickness')) %>%
map(~ aov(.x ~ SES + AGE + GENDER + EstimatedTotalIntraCranialVol +
HPT + DIABETES + HPL,
data = cortical.fs)) %>%
map(summary) %>%
map_dfr(~ as.data.frame(.[[1]][1,4:5]))
thickness.model['ROI'] <- DKROI
thickness.model['padj'] <- p.adjust(thickness.model$`Pr(>F)`, method = 'fdr')
colnames(thickness.model)[2] = 'punadj'
thickness.results <- subset(thickness.model, padj < 0.05)
# area --------------------------------------------------------------------
areaROI <- names(area.model) %>%
gsub('_area', '', .)
area.model <- cortical.fs %>%
dplyr::select(ends_with('area')) %>%
map(~ aov(.x ~ SES + AGE + GENDER + EstimatedTotalIntraCranialVol +
HPT + DIABETES + HPL,
data = cortical.fs)) %>%
map(summary) %>%
map_dfr(~ as.data.frame(.[[1]][1,4:5]))
area.model['ROI'] <- areaROI
area.model['padj'] <- p.adjust(area.model$`Pr(>F)`, method = 'fdr')
colnames(area.model)[2] = 'punadj'
area.results <- subset(area.model, padj < 0.05)
# volume ------------------------------------------------------------------
volume.model <- cortical.fs %>%
dplyr::select(ends_with('volume')) %>%
map(~ aov(.x ~ SES + AGE + GENDER + EstimatedTotalIntraCranialVol +
HPT + DIABETES + HPL,
data = cortical.fs)) %>%
map(summary) %>%
map_dfr(~ as.data.frame(.[[1]][1,4:5]))
volume.model['ROI'] <- DKROI
volume.model['padj'] <- p.adjust(volume.model$`Pr(>F)`, method = 'fdr')
colnames(volume.model)[2] = 'punadj'
volume.results <- subset(volume.model, padj < 0.05)
# aseg --------------------------------------------------------------------
library(ggseg)
plot(aseg)
asegROI <- names(cortical.fs)[238:276]
asegplot <- aseg$data
# 238-275 columns were aseg results
aseg.model <- cortical.fs %>%
dplyr::select(`Left-Lateral-Ventricle`:CC_Anterior) %>%
map(~ aov(.x ~ SES + AGE + GENDER + EstimatedTotalIntraCranialVol +
HPT + DIABETES + HPL,
data = cortical.fs)) %>%
map(summary) %>%
map_dfr(~ as.data.frame(.[[1]][1,4:5]))
aseg.model['ROI'] <- asegROI
aseg.model['padj'] <- p.adjust(aseg.model$`Pr(>F)`, method = 'fdr')
colnames(aseg.model)[2] = 'punadj'
aseg.results <- subset(aseg.model, padj < 0.05)
# causal mediation: ses-brain-cog -----------------------------------------
install.packages('mediation')
library(mediation)
# ses-cortex volume-cognition mediation model
model.M <- lm(CortexVol ~ SES + AGE + GENDER + HPT + DIABETES + HPL,
data = cortical.fs)
model.Y <- lm(WORKING ~ SES + CortexVol + AGE + GENDER + HPT + DIABETES + HPL,
data = cortical.fs)
out <- mediate(model.m = model.M, model.y = model.Y,
treat = 'SES', mediator = 'CortexVol', boot = TRUE, sims = 1000)
summary(out)
plot(out)
# mediations
# GENEGRAL
generaldata <- cortical.fs %>%
filter(!is.na(GENERAL))
mediators.roi <- paste0(DKROI, '_volume')
covars.tmp <- c('AGE + GENDER + EstimatedTotalIntraCranialVol + HPT + DIABETES + HPL')
generaldata <- list(generaldata = generaldata)
# the mediations function, dataset have to be a list
med.dataset <- list(med.dataset = cortical.fs)
# general -----------------------------------------------------------------
med.general <- mediations(datasets = generaldata, treatment = 'SES',
mediators = mediators.roi,
outcome = 'GENERAL', covariates = covars.tmp,
conf.level = 0.95, sims = 1000)
med.general.result <- tibble(
roi = character(),
medeff = numeric(),
medlow = numeric(),
medup = numeric(),
medp = numeric(),
deff = numeric(),
dlow = numeric(),
dup = numeric(),
dp = numeric(),
teff = numeric(),
tlow = numeric(),
tup = numeric(),
tp = numeric(),
prop = numeric(),
proplow = numeric(),
propup = numeric(),
propp = numeric()
)
write.csv(med.general.result, file = './derivatives/medresults_general.csv',
row.names = FALSE)
for (i in 1:length(med.general)) {
roi.results <- tibble(
roi = DKROI[i],
medeff = med.general[[i]]$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]]$d.avg.p
)
med.general.result <- read.csv('./derivatives/medresults_general.csv') %>%
rbind(roi.results) %>%
write.csv(file = './derivatives/medresults_general.csv',
row.names = FALSE)
}
# fdr correction of mediation results
med.general.result <- read.csv('./derivatives/medresults_general.csv') %>%
mutate(medpfdr = p.adjust(medp, method = 'fdr'),
proppfdr = p.adjust(propp, method = 'fdr')
)
# episodic ----------------------------------------------------------------
med.episodic <- mediations(datasets = med.dataset, treatment = 'SES',
mediators = mediators.roi,
outcome = 'EPISODIC', covariates = covars.tmp,
conf.level = 0.95, sims = 1000)
med.episodic.result <- tibble(
roi = character(),
medeff = numeric(),
medlow = numeric(),
medup = numeric(),
medp = numeric(),
deff = numeric(),
dlow = numeric(),
dup = numeric(),
dp = numeric(),
teff = numeric(),
tlow = numeric(),
tup = numeric(),
tp = numeric(),
prop = numeric(),
proplow = numeric(),
propup = numeric(),
propp = numeric()
)
write.csv(med.episodic.result, file = './derivatives/medresults_episodic.csv',
row.names = FALSE)
for (i in 1:length(med.episodic)) {
roi.results <- tibble(
roi = DKROI[i],
medeff = med.episodic[[i]]$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]]$d.avg.p
)
med.episodic.result <- read.csv('./derivatives/medresults_episodic.csv') %>%
rbind(roi.results) %>%
write.csv(file = './derivatives/medresults_episodic.csv',
row.names = FALSE)
}
# fdr correction of mediation results
med.episodic.result <- read.csv('./derivatives/medresults_episodic.csv') %>%
mutate(medpfdr = p.adjust(medp, method = 'fdr'),
proppfdr = p.adjust(propp, method = 'fdr')
)
# working -----------------------------------------------------------------
med.spatial <- mediations(datasets = med.dataset, treatment = 'SES',
mediators = mediators.roi,
outcome = 'WORKING', covariates = covars.tmp,
conf.level = 0.95, sims = 1000)
med.spatial.result <- tibble(
roi = character(),
medeff = numeric(),
medlow = numeric(),
medup = numeric(),
medp = numeric(),
deff = numeric(),
dlow = numeric(),
dup = numeric(),
dp = numeric(),
teff = numeric(),
tlow = numeric(),
tup = numeric(),
tp = numeric(),
prop = numeric(),
proplow = numeric(),
propup = numeric(),
propp = numeric()
)
write.csv(med.spatial.result, file = './derivatives/medresults_working.csv',
row.names = FALSE)
for (i in 1:length(med.spatial)) {
roi.results <- tibble(
roi = DKROI[i],
medeff = med.spatial[[i]]$d.avg,
medlow = med.spatial[[i]]$d.avg.ci[[1]],
medup = med.spatial[[i]]$d.avg.ci[[2]],
medp = med.spatial[[i]]$d.avg.p,
deff = med.spatial[[i]]$z.avg,
dlow = med.spatial[[i]]$z.avg.ci[[1]],
dup = med.spatial[[i]]$z.avg.ci[[2]],
dp = med.spatial[[i]]$z.avg.p,
teff = med.spatial[[i]]$tau.coef,
tlow = med.spatial[[i]]$tau.ci[[1]],
tup = med.spatial[[i]]$tau.ci[[2]],
tp = med.spatial[[i]]$tau.p,
prop = med.spatial[[i]]$n.avg,
proplow = med.spatial[[i]]$n.avg.ci[[1]],
propup = med.spatial[[i]]$d.avg.ci[[2]],
propp = med.spatial[[i]]$d.avg.p
)
med.spatial.result <- read.csv('./derivatives/medresults_working.csv') %>%
rbind(roi.results) %>%
write.csv(file = './derivatives/medresults_working.csv',
row.names = FALSE)
}
# fdr correction of mediation results
med.spatial.result <- read.csv('./derivatives/medresults_working.csv') %>%
mutate(medpfdr = p.adjust(medp, method = 'fdr'),
proppfdr = p.adjust(propp, method = 'fdr')
)
# logic -------------------------------------------------------------------
med.logic <- mediations(datasets = med.dataset, treatment = 'SES',
mediators = mediators.roi,
outcome = 'LOGIC', covariates = covars.tmp,
conf.level = 0.95, sims = 1000)
med.logic.result <- tibble(
roi = character(),
medeff = numeric(),
medlow = numeric(),
medup = numeric(),
medp = numeric(),
deff = numeric(),
dlow = numeric(),
dup = numeric(),
dp = numeric(),
teff = numeric(),
tlow = numeric(),
tup = numeric(),
tp = numeric(),
prop = numeric(),
proplow = numeric(),
propup = numeric(),
propp = numeric()
)
write.csv(med.logic.result, file = './derivatives/medresults_logic.csv',
row.names = FALSE)
for (i in 1:length(med.logic)) {
roi.results <- tibble(
roi = DKROI[i],
medeff = med.logic[[i]]$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]]$d.avg.p
)
med.logic.result <- read.csv('./derivatives/medresults_logic.csv') %>%
rbind(roi.results) %>%
write.csv(file = './derivatives/medresults_logic.csv',
row.names = FALSE)
}
# fdr correction of mediation results
med.logic.result <- read.csv('./derivatives/medresults_logic.csv') %>%
mutate(medpfdr = p.adjust(medp, method = 'fdr'),
proppfdr = p.adjust(propp, method = 'fdr')
)
# spatial -----------------------------------------------------------------
med.spatial <- mediations(datasets = med.dataset, treatment = 'SES',
mediators = mediators.roi,
outcome = 'SPATIAL', covariates = covars.tmp,
conf.level = 0.95, sims = 1000)
med.spatial.result <- tibble(
roi = character(),
medeff = numeric(),
medlow = numeric(),
medup = numeric(),
medp = numeric(),
deff = numeric(),
dlow = numeric(),
dup = numeric(),
dp = numeric(),
teff = numeric(),
tlow = numeric(),
tup = numeric(),
tp = numeric(),
prop = numeric(),
proplow = numeric(),
propup = numeric(),
propp = numeric()
)
write.csv(med.spatial.result, file = './derivatives/medresults_spatial.csv',
row.names = FALSE)
for (i in 1:length(med.spatial)) {
roi.results <- tibble(
roi = DKROI[i],
medeff = med.spatial[[i]]$d.avg,
medlow = med.spatial[[i]]$d.avg.ci[[1]],
medup = med.spatial[[i]]$d.avg.ci[[2]],
medp = med.spatial[[i]]$d.avg.p,
deff = med.spatial[[i]]$z.avg,
dlow = med.spatial[[i]]$z.avg.ci[[1]],
dup = med.spatial[[i]]$z.avg.ci[[2]],
dp = med.spatial[[i]]$z.avg.p,
teff = med.spatial[[i]]$tau.coef,
tlow = med.spatial[[i]]$tau.ci[[1]],
tup = med.spatial[[i]]$tau.ci[[2]],
tp = med.spatial[[i]]$tau.p,
prop = med.spatial[[i]]$n.avg,
proplow = med.spatial[[i]]$n.avg.ci[[1]],
propup = med.spatial[[i]]$d.avg.ci[[2]],
propp = med.spatial[[i]]$d.avg.p
)
med.spatial.result <- read.csv('./derivatives/medresults_spatial.csv') %>%
rbind(roi.results) %>%
write.csv(file = './derivatives/medresults_spatial.csv',
row.names = FALSE)
}
# fdr correction of mediation results
med.spatial.result <- read.csv('./derivatives/medresults_spatial.csv') %>%
mutate(medpfdr = p.adjust(medp, method = 'fdr'),
proppfdr = p.adjust(propp, method = 'fdr')
)
# language ----------------------------------------------------------------
med.language <- mediations(datasets = med.dataset, treatment = 'SES',
mediators = mediators.roi,
outcome = 'LANGUAGE', covariates = covars.tmp,
conf.level = 0.95, sims = 1000)
med.language.result <- tibble(
roi = character(),
medeff = numeric(),
medlow = numeric(),
medup = numeric(),
medp = numeric(),
deff = numeric(),
dlow = numeric(),
dup = numeric(),
dp = numeric(),
teff = numeric(),
tlow = numeric(),
tup = numeric(),
tp = numeric(),
prop = numeric(),
proplow = numeric(),
propup = numeric(),
propp = numeric()
)
write.csv(med.language.result, file = './derivatives/medresults_language.csv',
row.names = FALSE)
for (i in 1:length(med.language)) {
roi.results <- tibble(
roi = DKROI[i],
medeff = med.language[[i]]$d.avg,
medlow = med.language[[i]]$d.avg.ci[[1]],
medup = med.language[[i]]$d.avg.ci[[2]],
medp = med.language[[i]]$d.avg.p,
deff = med.language[[i]]$z.avg,
dlow = med.language[[i]]$z.avg.ci[[1]],
dup = med.language[[i]]$z.avg.ci[[2]],
dp = med.language[[i]]$z.avg.p,
teff = med.language[[i]]$tau.coef,
tlow = med.language[[i]]$tau.ci[[1]],
tup = med.language[[i]]$tau.ci[[2]],
tp = med.language[[i]]$tau.p,
prop = med.language[[i]]$n.avg,
proplow = med.language[[i]]$n.avg.ci[[1]],
propup = med.language[[i]]$d.avg.ci[[2]],
propp = med.language[[i]]$d.avg.p
)
med.language.result <- read.csv('./derivatives/medresults_language.csv') %>%
rbind(roi.results) %>%
write.csv(file = './derivatives/medresults_language.csv',
row.names = FALSE)
}
# fdr correction of mediation results
med.language.result <- read.csv('./derivatives/medresults_language.csv') %>%
mutate(medpfdr = p.adjust(medp, method = 'fdr'),
proppfdr = p.adjust(propp, method = 'fdr')
)
# attention ---------------------------------------------------------------
med.attention <- mediations(datasets = med.dataset, treatment = 'SES',
mediators = mediators.roi,
outcome = 'ATTENTION', covariates = covars.tmp,
conf.level = 0.95, sims = 1000)
med.attention.result <- tibble(
roi = character(),
medeff = numeric(),
medlow = numeric(),
medup = numeric(),
medp = numeric(),
deff = numeric(),
dlow = numeric(),
dup = numeric(),
dp = numeric(),
teff = numeric(),
tlow = numeric(),
tup = numeric(),
tp = numeric(),
prop = numeric(),
proplow = numeric(),
propup = numeric(),
propp = numeric()
)
write.csv(med.attention.result, file = './derivatives/medresults_attention.csv',
row.names = FALSE)
for (i in 1:length(med.attention)) {
roi.results <- tibble(
roi = DKROI[i],
medeff = med.attention[[i]]$d.avg,
medlow = med.attention[[i]]$d.avg.ci[[1]],
medup = med.attention[[i]]$d.avg.ci[[2]],
medp = med.attention[[i]]$d.avg.p,
deff = med.attention[[i]]$z.avg,
dlow = med.attention[[i]]$z.avg.ci[[1]],
dup = med.attention[[i]]$z.avg.ci[[2]],
dp = med.attention[[i]]$z.avg.p,
teff = med.attention[[i]]$tau.coef,
tlow = med.attention[[i]]$tau.ci[[1]],
tup = med.attention[[i]]$tau.ci[[2]],
tp = med.attention[[i]]$tau.p,
prop = med.attention[[i]]$n.avg,
proplow = med.attention[[i]]$n.avg.ci[[1]],
propup = med.attention[[i]]$d.avg.ci[[2]],
propp = med.attention[[i]]$d.avg.p
)
med.attention.result <- read.csv('./derivatives/medresults_attention.csv') %>%
rbind(roi.results) %>%
write.csv(file = './derivatives/medresults_attention.csv',
row.names = FALSE)
}
# fdr correction of mediation results
med.attention.result <- read.csv('./derivatives/medresults_attention.csv') %>%
mutate(medpfdr = p.adjust(medp, method = 'fdr'),
proppfdr = p.adjust(propp, method = 'fdr')
)
# executive ---------------------------------------------------------------
med.executive <- mediations(datasets = med.dataset, treatment = 'SES',
mediators = mediators.roi,
outcome = 'EXECUTIVE', covariates = covars.tmp,
conf.level = 0.95, sims = 1000)
med.executive.result <- tibble(
roi = character(),
medeff = numeric(),
medlow = numeric(),
medup = numeric(),
medp = numeric(),
deff = numeric(),
dlow = numeric(),
dup = numeric(),
dp = numeric(),
teff = numeric(),
tlow = numeric(),
tup = numeric(),
tp = numeric(),
prop = numeric(),
proplow = numeric(),
propup = numeric(),
propp = numeric()
)
write.csv(med.executive.result, file = './derivatives/medresults_executive.csv',
row.names = FALSE)
for (i in 1:length(med.executive)) {
roi.results <- tibble(
roi = DKROI[i],
medeff = med.executive[[i]]$d.avg,
medlow = med.executive[[i]]$d.avg.ci[[1]],
medup = med.executive[[i]]$d.avg.ci[[2]],
medp = med.executive[[i]]$d.avg.p,
deff = med.executive[[i]]$z.avg,
dlow = med.executive[[i]]$z.avg.ci[[1]],
dup = med.executive[[i]]$z.avg.ci[[2]],
dp = med.executive[[i]]$z.avg.p,
teff = med.executive[[i]]$tau.coef,
tlow = med.executive[[i]]$tau.ci[[1]],
tup = med.executive[[i]]$tau.ci[[2]],
tp = med.executive[[i]]$tau.p,
prop = med.executive[[i]]$n.avg,
proplow = med.executive[[i]]$n.avg.ci[[1]],
propup = med.executive[[i]]$d.avg.ci[[2]],
propp = med.executive[[i]]$d.avg.p
)
med.executive.result <- read.csv('./derivatives/medresults_executive.csv') %>%
rbind(roi.results) %>%
write.csv(file = './derivatives/medresults_executive.csv',
row.names = FALSE)
}
# fdr correction of mediation results
med.executive.result <- read.csv('./derivatives/medresults_executive.csv') %>%
mutate(medpfdr = p.adjust(medp, method = 'fdr'),
proppfdr = p.adjust(propp, method = 'fdr')
)
# cortex med plot ---------------------------------------------------------
# plot
med.working.plot <- tibble(label = dk$data$label) %>%
left_join(., dplyr::select(med.working.result,
roi, medeff, medp,
prop, propp),
by = c('label' = 'roi')) %>%
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.working.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(1.5, 5)),
breaks = seq(1.5, 5, 1),
limits = c(1.5, 5),
na.value = 'lightgrey') +
# scale_fill_scico(palette = 'lajolla',
# alpha = 0.8,
# begin = 0.2,
# end = 0.7,
# direction = 1,
# na.value = 'lightgrey') +
labs(title = 'working function',
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))
# aseg general -----------------------------------------------------------------
library(tidyverse)
med.aseg.dataset <- read.csv('./derivatives/FS_SES_841_IMPUTE.csv') %>%
dplyr::select(ID:NEWSESGROUP,
Left.Lateral.Ventricle:CC_Anterior,
EstimatedTotalIntraCranialVol)
mediators.aseg <- names(med.aseg.dataset)[34:72]
med.aseg.dataset <- list(med.aseg.dataset = med.aseg.dataset)
generaldata <- med.aseg.dataset %>%
filter(!is.na(GENERAL))
generaldata <- list(generaldata = generaldata)
med.aseg.general <- mediations(datasets = generaldata, treatment = 'SES',
mediators = mediators.aseg,
outcome = 'GENERAL', covariates = covars.tmp,
conf.level = 0.95, sims = 1000)
med.aseg.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.aseg.general.result, file = './derivatives/medaseg_general.csv',
row.names = FALSE)
for (i in 1:length(med.aseg.general)) {
roi.results <- tibble(
roi = mediators.aseg[i],
medeff = med.aseg.general[[i]]$d.avg,
medlow = med.aseg.general[[i]]$d.avg.ci[[1]],
medup = med.aseg.general[[i]]$d.avg.ci[[2]],
medp = med.aseg.general[[i]]$d.avg.p,
deff = med.aseg.general[[i]]$z.avg,
dlow = med.aseg.general[[i]]$z.avg.ci[[1]],
dup = med.aseg.general[[i]]$z.avg.ci[[2]],
dp = med.aseg.general[[i]]$z.avg.p,
teff = med.aseg.general[[i]]$tau.coef,
tlow = med.aseg.general[[i]]$tau.ci[[1]],
tup = med.aseg.general[[i]]$tau.ci[[2]],
tp = med.aseg.general[[i]]$tau.p,
prop = med.aseg.general[[i]]$n.avg,
proplow = med.aseg.general[[i]]$n.avg.ci[[1]],
propup = med.aseg.general[[i]]$d.avg.ci[[2]],
propp = med.aseg.general[[i]]$d.avg.p
)
med.aseg.general.result <- read.csv('./derivatives/medaseg_general.csv') %>%
rbind(roi.results) %>%
write.csv(file = './derivatives/medaseg_general.csv',
row.names = FALSE)
}
# fdr correction of mediation results
med.aseg.general.result <- read.csv('./derivatives/medaseg_general.csv') %>%
mutate(medpfdr = p.adjust(medp, method = 'fdr'),
proppfdr = p.adjust(propp, method = 'fdr')
)
# aseg episodic -----------------------------------------------------------
med.aseg.episodic <- mediations(datasets = med.aseg.dataset, treatment = 'SES',
mediators = mediators.aseg,
outcome = 'EPISODIC', covariates = covars.tmp,
conf.level = 0.95, sims = 1000)
med.aseg.episodic.result <- tibble(
roi = character(),
medeff = numeric(),
medlow = numeric(),
medup = numeric(),
medp = numeric(),
deff = numeric(),
dlow = numeric(),
dup = numeric(),
dp = numeric(),
teff = numeric(),
tlow = numeric(),
tup = numeric(),
tp = numeric(),
prop = numeric(),
proplow = numeric(),
propup = numeric(),
propp = numeric()
)
write.csv(med.aseg.episodic.result, file = './derivatives/medaseg_episodic.csv',
row.names = FALSE)
for (i in 1:length(med.aseg.episodic)) {
roi.results <- tibble(
roi = mediators.aseg[i],
medeff = med.aseg.episodic[[i]]$d.avg,
medlow = med.aseg.episodic[[i]]$d.avg.ci[[1]],
medup = med.aseg.episodic[[i]]$d.avg.ci[[2]],
medp = med.aseg.episodic[[i]]$d.avg.p,
deff = med.aseg.episodic[[i]]$z.avg,
dlow = med.aseg.episodic[[i]]$z.avg.ci[[1]],
dup = med.aseg.episodic[[i]]$z.avg.ci[[2]],
dp = med.aseg.episodic[[i]]$z.avg.p,
teff = med.aseg.episodic[[i]]$tau.coef,
tlow = med.aseg.episodic[[i]]$tau.ci[[1]],
tup = med.aseg.episodic[[i]]$tau.ci[[2]],
tp = med.aseg.episodic[[i]]$tau.p,
prop = med.aseg.episodic[[i]]$n.avg,