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rmanova_with_null_data_full_code.Rmd
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rmanova_with_null_data_full_code.Rmd
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---
title: "rmanova_with_null_data"
author: "jyh"
date: "3/26/2020"
output: html_document
---
## Initialize
```{r Initialize, echo=FALSE}
rm(list=ls())
#install & bring packages to the workspace
pacman::p_load(emmeans, rstatix, grid, knitr, jmv, data.table, plyr, ggplot2, RColorBrewer, tidyverse, devtools, easyGgplot2, papaja, psych, lme4, afex)
#pacman::p_load(jmv, ggplot, tidyverse, devtools, easyGgplot2, papaja, psych, lme4, afex, emmeans)
printf <- function(...) cat(sprintf(...))
```
## data info.
```{r data info., include=FALSE}
#rows:subjects
#1:10000 columns: permutation results
#10001 column: real data
#params.
nPerm=10000
#path and directories
path = '/Users/auna/Dropbox/2018-2019skku/CrM/from_local_fMRIfiles/Decoding_CrM/stat'
csv_path = file.path(path, 'rmanova_pval_results')
output_path=file.path(path, 'pdf_fig')
ifelse(!dir.exists(output_path), dir.create(output_path), FALSE)
ifelse(!dir.exists(csv_path), dir.create(csv_path), FALSE)
```
```{r Repeated-measures ANOVA: C vs. I vs. A main effect within each visual area}
#basic info.
cond = c("cong", "incong", "amb")
roi = c("MT", "FFA", "PPA")
#make df for results
df_perm_aov = data.frame(matrix(vector(), 0, 5,
dimnames=list(c(), c("effect", "ROI", "nPerm", "Fvalue", "p"))),
stringsAsFactors=Y)
#melt data structure
for (xROI in 1:length(roi)){
wide_df <- data.frame(Date=as.Date(character()),
File=character(),
User=character(),
stringsAsFactors=TRUE)
for (xCond in 1:length(cond)){
prefix = sprintf("%s/%s_%s_acc_all_dist.csv", cin_path, cond[xCond], roi[xROI])
data = read.csv(file.path(prefix), header=TRUE)
data = data %>% mutate(SN = row_number(), cond = cond[xCond], roi = roi[xROI])
wide_df = rbind(wide_df, data)
}
cia_df <- melt(setDT(wide_df),
id.vars = c("SN", "cond", "roi"),
variable.name = c("Perm"))
names(cia_df) = c("SN", "cond", "roi", "Perm", "acc")
cia_df$cond = as.factor(cia_df$cond)
cia_df$roi = as.factor(cia_df$roi)
cia_df$SN = as.factor(cia_df$SN)
#anova for each permutation
printf('C vs. I vs. A one-way RM ANOVA\n')
printf('starting no. %d out of %d ROI %s\n', xROI, length(roi), roi[xROI])
for (i in 1:(nPerm+1)){
test_perm = cia_df %>% filter(Perm %in% paste("V", i, sep=""))
res.aov = anova_test(data = test_perm, dv = acc, wid = SN, within = cond)$ANOVA
df_perm_aov = rbind(df_perm_aov, data.frame(effect="cia",
roi=roi[xROI],
nPerm=i,
Fvalue=res.aov$F,
p=res.aov$p))
if (i %% 500 == 0) {
printf('%s permutation testing no. %d\n', roi[xROI], i)
}
}
}
```
``` {r save dataframe as csv }
knitr::kable(df_cia_pval)
df_cia_pval = data.frame(matrix(vector(), 0, 4,
dimnames=list(c(), c("effect", "ROI", "pvalue", "sig"))),
stringsAsFactors=Y)
roi = unique(df_perm_aov$roi)
cond = unique(df_perm_aov$cond)
#p-value
sig_notsig = c("not significant", "significant")
for (xROI in 1:length(roi)){
real_fval = df_perm_aov %>% filter(nPerm %in% 10001 & effect %in% "cia" & roi %in% levels(roi)[xROI])
count = df_perm_aov %>% filter(effect %in% "cia" & roi %in% levels(roi)[xROI] & Fvalue >= real_fval$Fvalue)
cur_pval = round((dim(count)[1]/nPerm), 4)
cur_sig = as.integer(cur_pval < 0.025)
df_cia_pval = rbind(df_cia_pval, data.frame(effect="cia",
roi=roi[xROI],
sig=cur_sig,
value=cur_pval))
#print results
printf("C vs. I vs. A main effect in %s is %s, p=%2.3f\n",
roi[xROI], sig_notsig[cur_sig+1], cur_pval)
}
#FDR correction
pval_varea = df_cia_pval$value
adj_pval_varea = p.adjust(pval_varea, method = "fdr", n = length(pval_varea))
df_cia_pval = df_cia_pval %>% mutate(adj_pval = adj_pval_varea)
## save dataframe tables as .csv files ##
fval_csv_name=sprintf("%s/cia_rmanova_%s_fval_varea_perms_results.csv", csv_path, "CIN")
pval_csv_name=sprintf("%s/cia_rmanova_%s_pval_varea_results.csv", csv_path, "CIN")
write.csv(df_perm_aov, file.path(fval_csv_name))
write.csv(df_cia_pval, file.path(pval_csv_name))
```
```{undefined r Repeated-measures ANOVA: 3 x 2 cia vs. fp, include=FALSE}
cond = c("cong", "incong", "amb")
roi = c("V1", "V2", "V3")
region= c("fovea3.5_fmasked", "periphery-fovea3.5_fmasked")
df_perm_aov = data.frame(matrix(vector(), 0, 5,
dimnames=list(c(), c("effect", "ROI", "nPerm", "Fvalue", "p"))),
stringsAsFactors=Y)
wide_df <- data.frame(Date=as.Date(character()),
File=character(),
User=character(),
stringsAsFactors=TRUE)
for (xROI in 1:length(roi)){
for (xFP in 1:length(region)){
for (xCond in 1:length(cond)){
prefix = sprintf("%s/%s_%s_%s_acc_all_dist.csv", cin_path, cond[xCond], roi[xROI], region[xFP])
data = read.csv(file.path(prefix), header=TRUE)
data = data %>% mutate(SN = row_number(), cond = cond[xCond], roi = roi[xROI], fp = region[xFP])
wide_df = rbind(wide_df, data)
}}}
cia_fp_df <- melt(setDT(wide_df),
id.vars = c("SN", "cond", "roi", "fp"),
variable.name = c("Perm"))
names(cia_fp_df) = c("SN", "cond", "roi", "fp", "Perm", "acc")
cia_fp_df$cond = as.factor(cia_fp_df$cond)
cia_fp_df$SN = as.factor(cia_fp_df$SN)
cia_fp_df$fp = as.factor(cia_fp_df$fp)
cia_fp_df$roi = as.factor(cia_fp_df$roi)
#anova for each permutation
printf('3 (cia) x 2 (fp) RM ANOVA\n')
printf('starting no. %d out of %d ROI %s\n', xROI, length(roi), roi[xROI])
roi = unique(cia_fp_df$roi)
for (xROI in 1:length(roi)){
for (i in 1:nPerm+1){
test_perm = cia_fp_df %>% filter(Perm %in% paste("V", i, sep=""), roi %in% levels(roi)[xROI])
res.aov = anova_test(data = test_perm, dv = acc, wid = SN, within = c(cond, fp))$ANOVA
df_perm_aov = rbind(df_perm_aov, data.frame(effect=res.aov$Effect[1],
roi=roi[xROI],
nPerm=i,
Fvalue=res.aov$F[1],
p=res.aov$p[1]))
df_perm_aov = rbind(df_perm_aov, data.frame(effect=res.aov$Effect[2],
roi=roi[xROI],
nPerm=i,
Fvalue=res.aov$F[2],
p=res.aov$p[2]))
df_perm_aov = rbind(df_perm_aov, data.frame(effect=res.aov$Effect[3],
roi=roi[xROI],
nPerm=i,
Fvalue=res.aov$F[3],
p=res.aov$p[3]))
if (i %% 500 == 0) {
printf('%s permutation testing no. %d\n', roi[xROI], i)
}
}
}
```
``` {r save as csv file}
knitr::kable(df_cia_pval)
df_cia_fp_pval = data.frame(matrix(vector(), 0, 4,
dimnames=list(c(), c("effect", "ROI", "pvalue", "sig"))),
stringsAsFactors=Y)
effects = unique(df_perm_aov$effect)
#p-value
sig_notsig = c("not significant", "significant")
for (xROI in 1:length(roi)){
for (xEff in 1:length(effects)){
real_fval = df_perm_aov[df_perm_aov$nPerm == 10001 & df_perm_aov$effect == effects[xEff] & df_perm_aov$roi == roi[xROI], ]
#df_perm_aov %>% filter(nPerm %in% 10001 & effect %in% effects[xEff] & roi %in% roi[xROI])
count = df_perm_aov[df_perm_aov$effect == effects[xEff] & df_perm_aov$roi == roi[xROI] & df_perm_aov$Fvalue >= real_fval$Fvalue,]
cur_pval = round((dim(count)[1]/nPerm), 5)
cur_sig = as.integer(cur_pval < 0.025)
df_cia_fp_pval = rbind(df_cia_fp_pval, data.frame(effect=effects[xEff],
roi=roi[xROI],
sig=cur_sig,
value=cur_pval))
#print results
printf("3x2 RM ANOVA: %s effect in %s is %s, p=%2.3f\n",
effects[xEff], roi[xROI], sig_notsig[cur_sig+1], cur_pval)
}}
## save dataframe tables as .csv files ##
fval_csv_name=sprintf("%s/rmanova_cfp_%s_fval_varea_perms_results.csv", csv_path, "CIN")
pval_csv_name=sprintf("%s/rmanova_cfp_%s_pval_varea_results.csv", csv_path, "CIN")
write.csv(df_perm_aov, file.path(fval_csv_name))
write.csv(df_cia_fp_pval, file.path(pval_csv_name))
```
```{r Post-hoc test for 3 x 2 ANOVA}
#condition & ROI names
cond = c("cong", "incong", "amb")
roi = c("V1", "V2", "V3")
region= c("fovea3.5_fmasked", "periphery-fovea3.5_fmasked")
#make empty df.s for result
df_perm_aov = data.frame(matrix(vector(), 0, 8,
dimnames=list(c(), c("contrast", "ROI", "nPerm", "SE", "df", "tratio", "estimate", "p"))),
stringsAsFactors=Y)
wide_df <- data.frame(Date=as.Date(character()),
File=character(),
User=character(),
stringsAsFactors=TRUE)
#afex options
afex_options("round_p"=7)
afex_options("include_aov" = TRUE)
subnum=1:14
SN_s = paste("S", subnum, sep="")
SN_s = as_factor(SN_s)
#load each perm files
for (xROI in 1:length(roi)){
for (xFP in 1:length(region)){
for (xCond in 1:length(cond)){
prefix = sprintf("%s/%s_%s_%s_acc_all_dist.csv", cin_path, cond[xCond], roi[xROI], region[xFP])
data = read.csv(file.path(prefix), header=TRUE)
data = data %>% mutate(SN = SN_s, cond = cond[xCond], roi = roi[xROI], fp = region[xFP])
wide_df = rbind(wide_df, data)
}}}
cia_fp_df <- melt(setDT(wide_df),
id.vars = c("SN", "cond", "roi", "fp"),
variable.name = c("Perm"))
names(cia_fp_df) = c("SN", "cond", "roi", "fp", "Perm", "acc")
#cia_fp_df$SN = as.factor(cia_fp_df$SN)
cia_fp_df$cond = as.factor(cia_fp_df$cond)
cia_fp_df$SN = as.factor(cia_fp_df$SN)
cia_fp_df$fp = as.factor(cia_fp_df$fp)
cia_fp_df$roi = as.factor(cia_fp_df$roi)
for (xROI in 1:length(roi)){
#anova for each permutation
printf('3 (cia) x 2 (fp) RM ANOVA post-hoc \n')
printf('starting no. %d out of %d ROI %s\n', xROI, length(roi), roi[xROI])
for (i in 1:(nPerm+1)){
test_perm = cia_fp_df %>% filter(Perm %in% paste("V", i, sep=""), roi %in% levels(roi)[xROI])
res.aov = aov_car(acc ~ cond*fp + Error(SN/cond*fp), data=test_perm, anova_table = list(correction = "none"))
#post hoc test permutation..
res.posthoc = pairs(emmeans(res.aov, ~ cond*fp), adjust='None')
res.posthoc = as.data.frame(res.posthoc)
df_perm_aov = rbind(df_perm_aov, data.frame(contrast=res.posthoc$contrast,
roi=roi[xROI],
nPerm=i,
SE = res.posthoc$SE,
df = res.posthoc$df,
tratio= res.posthoc$t.ratio,
estimate= res.posthoc$estimate,
p=res.posthoc$p.value))
if (i %% 500 == 0) {
printf('%s post-hoc permutation testing no. %d\n', roi[xROI], i)
}
}}
```
```{r post-hoc results save}
df_posthoc_pval = data.frame(matrix(vector(), 0, 5,
dimnames=list(c(), c("contrast", "df", "ROI", "pvalue", "sig"))),
stringsAsFactors=Y)
contrast = unique(df_perm_aov$contrast)
#p-value
sig_notsig = c("not significant", "significant")
for (xROI in 1:length(roi)){
for (xEff in 1:length(contrast)){
real_fval = df_perm_aov[df_perm_aov$nPerm == 10001 & df_perm_aov$contrast == contrast[xEff] & df_perm_aov$roi == roi[xROI], ]
#df_perm_aov %>% filter(nPerm %in% 10001 & effect %in% effects[xEff] & roi %in% roi[xROI])
if (real_fval$tratio < 0 ){
count = df_perm_aov[df_perm_aov$contrast == contrast[xEff] & df_perm_aov$roi == roi[xROI] & df_perm_aov$tratio <= real_fval$tratio,]
}else{
if (real_fval$tratio > 0)
count = df_perm_aov[df_perm_aov$contrast == contrast[xEff] & df_perm_aov$roi == roi[xROI] & df_perm_aov$tratio >= real_fval$tratio,]
}
cur_pval = round((dim(count)[1]/nPerm)*2, 7)
cur_sig = as.integer(cur_pval < 0.05)
df_posthoc_pval = rbind(df_posthoc_pval, data.frame(contrast=contrast[xEff],
df=real_fval$df,
roi=roi[xROI],
sig=cur_sig,
value=cur_pval))
#print results
printf("3x2 RM ANOVA post-hoc: %s contrast in %s is %s, p=%2.3f\n",
contrast[xEff], roi[xROI], sig_notsig[cur_sig+1], cur_pval)
}}
## save dataframe tables as .csv files ##
fval_csv_name=sprintf("%s/rmanova_posthoc_%s_tval_varea_perms_results.csv", csv_path, "CIN")
pval_csv_name=sprintf("%s/rmanova_posthoc_%s_pval_varea_results.csv", csv_path, "CIN")
write.csv(df_perm_aov, file.path(fval_csv_name))
write.csv(df_posthoc_pval, file.path(pval_csv_name))
```
```{r Repeated-measures ANOVA with vOnly or aOnly cond. : visual area main effect}
#basic info.
cond = c("vOnlyCoh1")
xCond=1
roi = c("V1_fmasked", "V2_fmasked", "V3_fmasked")
#make df for results
df_perm_aov1 = data.frame(matrix(vector(), 0, 4,
dimnames=list(c(), c("effect", "nPerm", "Fvalue", "p"))),
stringsAsFactors=Y)
#melt data structure
wide_df <- data.frame(Date=as.Date(character()),
File=character(),
User=character(),
stringsAsFactors=TRUE)
for (xROI in 1:length(roi)){
prefix = sprintf("%s/%s_%s_acc_all_dist.csv", path, cond[xCond], roi[xROI])
data = read.csv(file.path(prefix), header=FALSE)
data = data %>% mutate(SN = row_number(), cond = cond[xCond], roi = roi[xROI])
wide_df = rbind(wide_df, data)
}
cond_df <- melt(setDT(wide_df),
id.vars = c("SN", "cond", "roi"),
variable.name = c("Perm"))
names(cond_df) = c("SN", "cond", "roi", "Perm", "acc")
cond_df$cond = as.factor(cond_df$cond)
cond_df$roi = as.factor(cond_df$roi)
cond_df$SN = as.factor(cond_df$SN)
#anova for each permutation
printf('%s visual area one-way RM ANOVA\n', cond[xCond])
printf('Starting with fmasked roi...\n')
for (i in 1:(nPerm+1)){
test_perm = cond_df %>% filter(Perm %in% paste("V", i, sep=""))
res.aov = anova_test(data = test_perm, dv = acc, wid = SN, within = roi)$ANOVA
df_perm_aov1 = rbind(df_perm_aov1, data.frame(effect="varea",
nPerm=i,
Fvalue=res.aov$F,
p=res.aov$p))
if (i %% 500 == 0) {
printf('no.%s permutation testing\n',i)
}
}
```
```{r Repeated-measures ANOVA with vOnly cond. - anat : visual area main effect}
#basic info.
roi = c("V1_fov+peri", "V2_fov+peri", "V3_fov+peri")
#make df for results
df_perm_aov2 = data.frame(matrix(vector(), 0, 4,
dimnames=list(c(), c("effect", "nPerm", "Fvalue", "p"))),
stringsAsFactors=Y)
#melt data structure
wide_df <- data.frame(Date=as.Date(character()),
File=character(),
User=character(),
stringsAsFactors=TRUE)
for (xROI in 1:length(roi)){
prefix = sprintf("%s/%s_%s_acc_all_dist.csv", path, cond[xCond], roi[xROI])
data = read.csv(file.path(prefix), header=FALSE)
data = data %>% mutate(SN = row_number(), cond = cond[xCond], roi = roi[xROI])
wide_df = rbind(wide_df, data)
}
vOnlyCoh1_df <- melt(setDT(wide_df),
id.vars = c("SN", "cond", "roi"),
variable.name = c("Perm"))
names(vOnlyCoh1_df) = c("SN", "cond", "roi", "Perm", "acc")
vOnlyCoh1_df$cond = as.factor(vOnlyCoh1_df$cond)
vOnlyCoh1_df$roi = as.factor(vOnlyCoh1_df$roi)
vOnlyCoh1_df$SN = as.factor(vOnlyCoh1_df$SN)
#anova for each permutation
printf('Visual area one-way RM ANOVA\n')
printf('Starting with anat roi...\n')
for (i in 1:(nPerm+1)){
test_perm = vOnlyCoh1_df %>% filter(Perm %in% paste("V", i, sep=""))
res.aov = anova_test(data = test_perm, dv = acc, wid = SN, within = roi)$ANOVA
df_perm_aov2 = rbind(df_perm_aov2, data.frame(effect="varea_anat",
cond=cond[xCond],
nPerm=i,
Fvalue=res.aov$F,
p=res.aov$p))
if (i %% 500 == 0) {
printf('no.%s permutation testing\n',i)
}
}
```
``` {r save dataframe as csv }
df_cond_pval = data.frame(matrix(vector(), 0, 3,
dimnames=list(c(), c("effect", "pvalue", "sig"))),
stringsAsFactors=Y)
df_perm_aov_FNL = full_join(df_perm_aov1, df_perm_aov2)
#p-value
effect=c("varea", "varea_anat")
for (xEffect in effect){
sig_notsig = c("not significant", "significant")
real_fval = df_perm_aov_FNL %>% filter(nPerm %in% 10001 & effect %in% xEffect)
count = df_perm_aov_FNL %>% filter(effect %in% xEffect & Fvalue >= real_fval$Fvalue)
cur_pval = round((dim(count)[1]/nPerm), 4)
cur_sig = as.integer(cur_pval < 0.05)
df_cond_pval = rbind(df_cond_pval, data.frame(effect=xEffect,
sig=cur_sig,
pvalue=cur_pval))
#print results
printf("Visual area main effect (%s) is %s, p=%2.3f\n",
xEffect, sig_notsig[cur_sig+1], cur_pval)
}
## save dataframe tables as .csv files ##
fval_csv_name=sprintf("%s/varea_rmanova_%s_fval_perms_results.csv", csv_path, cond)
pval_csv_name=sprintf("%s/varea_rmanova_%s_pval_results.csv", csv_path, cond)
write.csv(df_perm_aov_FNL, file.path(fval_csv_name))
write.csv(df_cond_pval, file.path(pval_csv_name))
```
``` {r visual area x eccentricity }
cond = c("vOnlyCoh1")
xCond=1
roi = c("V1", "V2", "V3")
region= c("fovea3.5_fmasked", "periphery-fovea3.5_fmasked")
df_perm_aov_fmasked = data.frame(matrix(vector(), 0, 7,
dimnames=list(c(), c("cond" ,"effect", "nPerm", "mask", "Fvalue", "p"))),
stringsAsFactors=Y)
wide_df <- data.frame(Date=as.Date(character()),
File=character(),
User=character(),
stringsAsFactors=TRUE)
for (xROI in 1:length(roi)){
for (xFP in 1:length(region)){
for (xCond in 1:length(cond)){
prefix = sprintf("%s/%s_%s_%s_acc_all_dist.csv", path, cond[xCond], roi[xROI], region[xFP])
data = read.csv(file.path(prefix), header=FALSE)
data = data %>% mutate(SN = row_number(), cond = cond[xCond], roi = roi[xROI], fp = region[xFP])
wide_df = rbind(wide_df, data)
}}}
varea_fp_df <- melt(setDT(wide_df),
id.vars = c("SN", "cond", "roi", "fp"),
variable.name = c("Perm"))
names(varea_fp_df) = c("SN", "cond", "roi", "fp", "Perm", "acc")
varea_fp_df$cond = as.factor(varea_fp_df$cond)
varea_fp_df$SN = as.factor(varea_fp_df$SN)
varea_fp_df$fp = as.factor(varea_fp_df$fp)
varea_fp_df$roi = as.factor(varea_fp_df$roi)
#anova for each permutation
printf('3 (varea) x 2 (fp) RM ANOVA\n')
printf('starting %s cond....\n', cond[xCond])
for (i in 1:(nPerm+1)){
test_perm = varea_fp_df %>% filter(Perm %in% paste("V", i, sep=""))
res.aov = anova_test(data = test_perm, dv = acc, wid = SN, within = c(roi, fp))$ANOVA
df_perm_aov = rbind(df_perm_aov, data.frame(cond=cond[xCond],
effect=res.aov$Effect[1],
nPerm=i,
mask="fmasked",
Fvalue=res.aov$F[1],
p=res.aov$p[1]))
df_perm_aov = rbind(df_perm_aov, data.frame(cond=cond[xCond],
effect=res.aov$Effect[2],
nPerm=i,
mask="fmasked",
Fvalue=res.aov$F[2],
p=res.aov$p[2]))
df_perm_aov = rbind(df_perm_aov, data.frame(cond=cond[xCond],
effect=res.aov$Effect[3],
nPerm=i,
mask="fmasked",
Fvalue=res.aov$F[3],
p=res.aov$p[3]))
if (i %% 500 == 0) {
printf('%s %s - permutation testing no. %d\n', cond[xCond], "fmasked", i)
}
}
##anat mask rm anova
region= c("fovea3.5", "periphery-fovea3.5")
df_perm_aov_anat = data.frame(matrix(vector(), 0, 7,
dimnames=list(c(), c("cond" ,"effect", "nPerm", "mask", "Fvalue", "p"))),
stringsAsFactors=Y)
wide_df <- data.frame(Date=as.Date(character()),
File=character(),
User=character(),
stringsAsFactors=TRUE)
for (xROI in 1:length(roi)){
for (xFP in 1:length(region)){
for (xCond in 1:length(cond)){
prefix = sprintf("%s/%s_%s_%s_acc_all_dist.csv", path, cond[xCond], roi[xROI], region[xFP])
data = read.csv(file.path(prefix), header=FALSE)
data = data %>% mutate(SN = row_number(), cond = cond[xCond], roi = roi[xROI], fp = region[xFP])
wide_df = rbind(wide_df, data)
}}}
varea_fp_df <- melt(setDT(wide_df),
id.vars = c("SN", "cond", "roi", "fp"),
variable.name = c("Perm"))
names(varea_fp_df) = c("SN", "cond", "roi", "fp", "Perm", "acc")
varea_fp_df$cond = as.factor(varea_fp_df$cond)
varea_fp_df$SN = as.factor(varea_fp_df$SN)
varea_fp_df$fp = as.factor(varea_fp_df$fp)
varea_fp_df$roi = as.factor(varea_fp_df$roi)
#anova for each permutation
printf('3 (varea) x 2 (fp) RM ANOVA\n')
printf('starting %s cond....\n', cond[xCond])
roi = unique(varea_fp_df$roi)
for (i in 1:(nPerm+1)){
test_perm = varea_fp_df %>% filter(Perm %in% paste("V", i, sep=""))
res.aov = anova_test(data = test_perm, dv = acc, wid = SN, within = c(roi, fp))$ANOVA
df_perm_aov_anat = rbind(df_perm_aov_anat, data.frame(cond=cond[xCond],
effect=res.aov$Effect[1],
nPerm=i,
mask="anat",
Fvalue=res.aov$F[1],
p=res.aov$p[1]))
df_perm_aov_anat = rbind(df_perm_aov_anat, data.frame(cond=cond[xCond],
effect=res.aov$Effect[2],
nPerm=i,
mask="anat",
Fvalue=res.aov$F[2],
p=res.aov$p[2]))
df_perm_aov_anat = rbind(df_perm_aov_anat, data.frame(cond=cond[xCond],
effect=res.aov$Effect[3],
nPerm=i,
mask="anat",
Fvalue=res.aov$F[3],
p=res.aov$p[3]))
if (i %% 500 == 0) {
printf('%s permutation testing no. %d\n', cond[xCond], i)
}
}
```
``` {r save as csv file}
knitr::kable(df_cia_pval)
df_varea_fp_pval = data.frame(matrix(vector(), 0, 3,
dimnames=list(c(), c("effect", "pvalue", "sig"))),
stringsAsFactors=Y)
effects = unique(df_perm_aov$effect)
#p-value
sig_notsig = c("not significant", "significant")
for (xEff in 1:length(effects)){
real_fval = df_perm_aov[df_perm_aov$nPerm == 10001 & df_perm_aov$effect == effects[xEff], ]
#df_perm_aov %>% filter(nPerm %in% 10001 & effect %in% effects[xEff] & roi %in% roi[xROI])
count = df_perm_aov[df_perm_aov$effect == effects[xEff] & df_perm_aov$Fvalue >= real_fval$Fvalue,]
cur_pval = round((dim(count)[1]/nPerm), 5)
cur_sig = as.integer(cur_pval < 0.05)
df_varea_fp_pval = rbind(df_varea_fp_pval, data.frame(effect=effects[xEff],
roi=roi[xROI],
sig=cur_sig,
value=cur_pval))
#print results
printf("3x2 RM ANOVA: %s effect in %s is %s, p=%2.3f\n",
effects[xEff], cond[xCond], sig_notsig[cur_sig+1], cur_pval)
}
df_varea_fp_pval2 = data.frame(matrix(vector(), 0, 3,
dimnames=list(c(), c("effect", "pvalue", "sig"))),
stringsAsFactors=Y)
effects = unique(df_perm_aov$effect)
#p-value
sig_notsig = c("not significant", "significant")
for (xEff in 1:length(effects)){
real_fval = df_perm_aov2[df_perm_aov$nPerm == 10001 & df_perm_aov$effect == effects[xEff], ]
#df_perm_aov %>% filter(nPerm %in% 10001 & effect %in% effects[xEff] & roi %in% roi[xROI])
count = df_perm_aov2[df_perm_aov$effect == effects[xEff] & df_perm_aov$Fvalue >= real_fval$Fvalue,]
cur_pval = round((dim(count)[1]/nPerm), 5)
cur_sig = as.integer(cur_pval < 0.05)
df_varea_fp_pval2 = rbind(df_varea_fp_pval2, data.frame(effect=effects[xEff],
roi=roi[xROI],
sig=cur_sig,
value=cur_pval))
#print results
printf("3x2 RM ANOVA: %s effect in %s is %s, p=%2.3f\n",
effects[xEff], cond[xCond], sig_notsig[cur_sig+1], cur_pval)
}
df_varea_fp_pval = df_varea_fp_pval %>% mutate(roi = "fmasked")
df_varea_fp_pval2 = df_varea_fp_pval2 %>% mutate(roi = "anat")
df_perm_aov_FNL = full_join(df_perm_aov1, df_perm_aov2)
df_varea_fp_pval_FNL = full_join(df_varea_fp_pval, df_varea_fp_pval2)
## save dataframe tables as .csv files ##
fval_csv_name=sprintf("%s/rmanova_varea_fp_%s_fval_perms_results.csv", csv_path, cond[xCond])
pval_csv_name=sprintf("%s/rmanova_varea_fp_%s_pval_results.csv", csv_path, cond[xCond])
write.csv(df_perm_aov, file.path(fval_csv_name))
write.csv(df_varea_fp_pval, file.path(pval_csv_name))
```
### END