ERP-NIRS_ver6
Data and Packages
ERPdata (from erpR)
library(ERP);require(mnormt);require(fdrtool);library(ggplot2);library(dplyr);library(gridExtra)
library(erpR);require(akima);library(reshape2);library(boot);library(plotly);library(ggmap);library(SCBmeanfd)
load("ERPdata.RData")Single Function : fdaplot
It could draw several plots
1. Raw data plot
2. Average Signal Plot
3. Bootstraping Interval Plot
4. test plot
5. test plot with signal
# "fdaplot" Code
fdaplot <- function(data, # The ERP or NIRS data
frames = NULL, # The time point of the data
data.var , # The column of "Data"
subj.var = NULL, # The column of "Subject"
chan.var = NULL, # The column of "Channel"
test.var = NULL, # The column of the variable that you want to compare (e.g. "Condition")
subj.select = NULL, # The Subject you want to select
chan.select = NULL, # The Channels you want to select
type = c("raw","average","boot.ci","test","test_signal"), # five types of the possible plot
curve.col = c("blue","red"), # the color for the curve (e.g. two conditions need two colors)
labs = list(x = "Frames", y = "Signal"), # Labs of the plot
scalp = FALSE, # Do you want to plot on the scalp ?
ylim = c(20,-20), # y limits of the plot
curve.fun = function(x, d){return(mean(x[d]))}, # when type == average or bootci
boot.num = 500 , # when type == bootci, the bootstraping number
boot.intval = c(0.025,0.975), # when type == bootci, the bootstraping interval
ci.alpha = 0.3, # when type == bootci, the alpha value of the ribbon interval
cor = F, # use in correlation settings (not yet)
significant.col = "pink", # when type == test | type = test_signal, the color of the significant window
significant.alpha = 0.2, # when type == test | type = test_signal, the alpha value of the significant window
# the argument for erpfatest
design = NULL, design0 = NULL,...){
# Some Check Functions (need More)
if (!is.null(test.var)){
if (length((levels(as.factor(data[,test.var])))) != length(curve.col)){
stop("curve.col should be equal to levels of test.var !")
}
}
if (is.null(frames)){frames = 1:length(data.var)}
# the function of selecting data
data_select <- function(data,data.var , subj.var = NULL, chan.var = NULL,
subj.select = NULL,chan.select = NULL){
dta <- data
if (is.null(subj.select) == FALSE & is.null(chan.select) == FALSE ) {
dta_s <- subset(dta,dta[,chan.var] %in% chan.select & dta[,subj.var] %in% subj.select)
} else if (is.null(subj.select) == FALSE & is.null(chan.select) == TRUE) {
dta_s <- subset(dta,dta[,subj.var] %in% subj.select )
} else if (is.null(subj.select) == TRUE & is.null(chan.select) == FALSE) {
dta_s <- subset(dta,dta[,chan.var] %in% chan.select)
} else {
dta_s <- dta
}
dta_s[,chan.var] <- as.factor(as.character(dta_s[,chan.var]))
dta_s[,subj.var] <- as.factor(as.character(dta_s[,subj.var]))
return (dta_s)
}
# Select the data
dta <- data_select(data, data.var, subj.var, chan.var,subj.select,chan.select)
# Some plot functions
edaplot <- function(data,frames=NULL,data.var,subj.var=NULL,chan.var=NULL){
colnames(data)[chan.var] <- "Channel"
oth.var = (1:dim(data)[2]) [! 1:dim(data)[2] %in% c(data.var,subj.var,chan.var)]
data$Info <- NA
for (i in 1 : dim(data)[1]){
info1 <- paste0(as.character(data[i,oth.var]),collapse=";")
data$Info[i] <- paste(data[i,chan.var],data[i,subj.var],info1,sep=";")
}
datalong <- melt(data,
id=c(variable.names(data)[c(subj.var,chan.var,oth.var)],
"Info"))
datalong <- datalong[order(datalong$Info),]
datalong$frames <- rep(frames,length(datalong[,1])/length(frames))
plot <- ggplot(datalong,aes(x=frames,y=value,group=Info))+
geom_line()
return(plot)
}
# The test function
chan_test <- function(data,data.var = NULL,chan.var = NULL,design_model,design0_model=NULL,...){
dta <- data
levelnum <- length(levels(dta[,chan.var]))
if (levelnum == 1) {
design <- model.matrix(design_model,data=data)
if (is.null(design0_model)==F){
design0 <- model.matrix(design0_model,data=data)
}
test_list <- erpfatest(dta[,data.var],design,design0,...)
} else {
test_list=list()
dta_list=list()
for (i in 1:levelnum) {
dta_list[[i]] <- subset(dta,dta[,chan.var]==(levels(dta[,chan.var])[i]))
design <- model.matrix(design_model,data=dta_list[[i]])
if (is.null(design0_model)==F){
design0 <- model.matrix(design0_model,data=dta_list[[i]])
}
test_list[[i]] <- erpfatest(dta_list[[i]][,data.var],design,design0,...)
names(test_list)[i] <- levels(dta[,chan.var])[i]
}
}
return(test_list)
}
# Produce the test results called "tests_rst", when type == "test" or "test_signal"
if (type == "test" | type == "test_signal"){tests_rst <- chan_test(dta,data.var,chan.var,...)}
# The summarize function
data_summarize <- function(data,data.var,summary.var,fun=mean,...){
dta <- data
options(warn=-1)
agglength <- length(summary.var)
aggvar_list <- list(dta[,summary.var[1]])
if (agglength > 1){
for (i in 2:agglength ){
aggvar_list <- append(aggvar_list,list(dta[,summary.var[2]]))
}
}
aggdata <- aggregate(dta[,data.var],by=aggvar_list,
fun,...)
aggdata <- aggdata[,1:(agglength+length(data.var))]
for (i in 1: agglength){
colnames(aggdata)[i] <- colnames(dta)[summary.var[i]]
}
rownames(aggdata) <- 1:dim(aggdata)[1]
return(aggdata)
}
# The bootstraping function
bootstrap <- function(x,bootnum,bootfun,bootintval=c(0.025,0.975),quantilenum,...){
boot_result <- boot(x,statistic = bootfun,R = bootnum,...)
return(quantile(boot_result$t,bootintval,na.rm = T)[quantilenum])
}
# Produce the data of confidence interval
if (type == "bootci"){
if (is.null(test.var)==TRUE){
data_FUN <- data_summarize(dta,data.var,summary.var = chan.var,fun = curve.fun)
data_Q1 <- data_summarize(dta,data.var,summary.var = chan.var,
fun=bootstrap,bootnum=boot.num,bootfun=curve.fun,
bootintval=boot.intval,quantilenum=1)
data_Q2 <- data_summarize(dta,data.var,summary.var = chan.var,
fun=bootstrap,bootnum=boot.num,bootfun=curve.fun,
bootintval=boot.intval,quantilenum=2)
data_fun_long <- melt(data_FUN,id=c(colnames(dta)[chan.var]))
colnames(data_fun_long)[3] <- "FUN"
data_Q1_long <- melt(data_Q1,id=c(colnames(dta)[chan.var]))
colnames(data_Q1_long)[3] <- "Q1"
data_Q2_long <- melt(data_Q2,id=c(colnames(dta)[chan.var]))
colnames(data_Q2_long)[3] <- "Q2"
data_ci <- full_join(data_fun_long,data_Q1_long,by = c(colnames(dta)[chan.var],"variable"))
data_ci <- full_join(data_ci,data_Q2_long,by = c(colnames(dta)[chan.var],"variable"))
colnames(data_ci)[1] <- c("Channel")
}
else {
dta[,test.var] <- as.factor(dta[,test.var])
dta[,chan.var] <- as.factor(dta[,chan.var])
data_FUN <- data_summarize(dta,data.var,summary.var = c(chan.var,test.var),fun = curve.fun)
data_Q1 <- data_summarize(dta,data.var,summary.var = c(chan.var,test.var),
fun=bootstrap,bootnum=boot.num,bootfun=curve.fun,
bootintval=boot.intval,quantilenum=1)
data_Q2 <- data_summarize(dta,data.var,summary.var = c(chan.var,test.var),
fun=bootstrap,bootnum=boot.num,bootfun=curve.fun,
bootintval=boot.intval,quantilenum=2)
data_fun_long <- melt(data_FUN,id=c(colnames(dta)[chan.var],colnames(dta)[test.var]))
colnames(data_fun_long)[c(1,2,4)] <- c("Channel","Condition","FUN")
data_Q1_long <- melt(data_Q1,id=c(colnames(dta)[chan.var],colnames(dta)[test.var]))
colnames(data_Q1_long)[c(1,2,4)] <- c("Channel","Condition","Q1")
data_Q2_long <- melt(data_Q2,id=c(colnames(dta)[chan.var],colnames(dta)[test.var]))
colnames(data_Q2_long)[c(1,2,4)] <- c("Channel","Condition","Q2")
data_ci <- full_join(data_fun_long,data_Q1_long,by = c("Channel","Condition","variable"))
data_ci <- full_join(data_ci,data_Q2_long,by = c("Channel","Condition","variable"))
}
}
# Draw the plot (not on 10/10 system)
if (scalp == F){
if (type == "raw"){
data_plot <- dta
colnames(data_plot)[chan.var] <- "Channel"
if (is.null(test.var)){
plot <- edaplot(data = data_plot,frames = frames,data.var = data.var,subj.var=subj.var,
chan.var=chan.var)+
geom_line(col = curve.col[1])+
facet_wrap(~Channel)+
labs(labs)+
ylim(ylim)+
theme_bw()
}
else {
colnames(data_plot)[test.var] <- "Condition"
values <- curve.col[1]
for (i in 2:length(levels(as.factor(dta[,test.var])))){
values <- c(values,curve.col[i])
}
plot <- edaplot(data = data_plot,frames = frames,data.var = data.var,subj.var=subj.var,
chan.var=chan.var)+
geom_line(aes(col = Condition))+
facet_wrap(~Channel)+
labs(labs)+
scale_color_manual(values = values, name = colnames(dta)[test.var])+
ylim(ylim)+
theme_bw()
}
}
if (type == "average") {
if (is.null(test.var)){
data_plot <- data_summarize(dta,data.var ,summary.var = chan.var,fun = curve.fun)
data_plot <- melt(data_plot)
colnames(data_plot)[1] <- "Channel"
data_plot <- data_plot[order(data_plot$Channel,data_plot$variable),]
data_plot$frames <- rep(frames,dim(data_plot)[1]/length(frames))
plot <- ggplot(data = data_plot,aes(x = frames, y = value))+
geom_line(col = curve.col[1])+
facet_wrap(~Channel)+
theme_bw()+
labs(labs)+
ylim(ylim)+
theme_bw()
}
else {
values <- curve.col[1]
for (i in 2:length(levels(as.factor(dta[,test.var])))){
values <- c(values,curve.col[i])
}
data_plot <- data_summarize(dta,data.var ,summary.var = c(chan.var,test.var),fun = curve.fun)
data_plot <- melt(data_plot)
colnames(data_plot)[c(1,2)] <- c("Channel","Condition")
data_plot <- data_plot[order(data_plot$Channel,data_plot$Condition,data_plot$variable),]
data_plot$frames <- rep(frames,dim(data_plot)[1]/length(frames))
plot <- ggplot(data = data_plot,aes(x = frames, y = value))+
geom_line(aes(col = Condition))+
facet_wrap(~Channel)+
theme_bw()+
labs(labs)+
scale_color_manual(values = values, name = colnames(dta)[test.var])+
ylim(ylim)+
theme_bw()
}
}
if (type == "bootci"){
if (is.null(test.var)){
data_ci <- data_ci[order(data_ci$Channel),]
data_ci$frames <- rep(frames,dim(data_ci)[1]/length(frames))
plot <- ggplot(data = data_ci,aes(x = frames, y = FUN))+
geom_ribbon(aes(ymax = Q1,ymin = Q2),alpha = ci.alpha, fill = curve.col[1])+
geom_line(col = curve.col[1])+
facet_wrap(~Channel)+
theme_bw()+
labs(labs)+
ylim(ylim)+
theme_bw()
}
else {
values <- curve.col[1]
for (i in 2:length(levels(as.factor(dta[,test.var])))){
values <- c(values,curve.col[i])
}
data_ci <- data_ci[order(data_ci$Channel,data_ci$Condition,data_ci$variable),]
data_ci$frames <- rep(frames,dim(data_ci)[1]/length(frames))
plot <- ggplot(data = data_ci,aes(x = frames, y = FUN))+
geom_ribbon(aes(ymax = Q2,ymin=Q1, fill = Condition),alpha = ci.alpha)+
geom_line(aes(col = Condition))+
facet_wrap(~Channel)+
theme_bw()+
labs(labs)+
scale_color_manual(values = values ,
name = colnames(dta)[test.var])+
scale_fill_manual(values = values ,
name = colnames(dta)[test.var])+
ylim(ylim)+
theme_bw()
}
}
if (type == "test") {
if (length(levels(dta[,chan.var]))==1){
data_test <- data.frame(signal=as.numeric(tests_rst$signal))
data_test$frames = frames
data_test$significant <-
ifelse(data_test $frames %in% data_test $frames[tests_rst$significant],
"sig","non-sig")
data_test$sign_frames <-
ifelse(data_test $frames %in% data_test$frames[tests_rst$significant],
data_test $frames,NA)
data_test $group <- rep(0,length(data_test $signal))
data_test $r2 <- tests_rst$r2
if (cor == TRUE){
plot <- ggplot(data_test ,
aes(x=frames,y=sign(signal)*sqrt(r2),group=group))+
geom_vline(data=data_test ,
aes(xintercept = sign_frames,
col=significant.col),
alpha=significant.alpha)+
geom_line()+
labs(labs)+
theme_bw()+
theme(legend.position="none")+
ylim(ylim)
}
else {
plot <- ggplot(data_test ,aes(x=frames,y=signal,group=group))+
geom_vline(data=data_test ,
aes(xintercept = sign_frames),
col=significant.col,
alpha=significant.alpha)+
geom_line()+
labs(labs)+
theme_bw()+
theme(legend.position="none")+
ylim(ylim)
}
}
else {
listlen <- length(tests_rst)
data_list <- list()
for (k in 1:listlen){
data_list[[k]] <- data.frame(signal=as.numeric(tests_rst[[k]]$signal))
data_list[[k]]$frames = frames
data_list[[k]]$significant<-ifelse(data_list[[k]]$frames %in% data_list[[k]]$frames[tests_rst[[k]]$significant],"sig","non-sig")
data_list[[k]]$sign_frames <- ifelse(data_list[[k]]$frames %in% data_list[[k]]$frames[tests_rst[[k]]$significant],data_list[[k]]$frames,NA)
data_list[[k]]$group <- rep(0,length(data_list[[k]]$signal))
data_list[[k]]$r2 <- tests_rst[[k]]$r2
data_list[[k]]$Channel <- names(tests_rst)[k]
}
data_plot <- data_list[[1]]
for (j in 2 : listlen){
data_plot <- rbind(data_plot,data_list[[j]])
}
if (cor == TRUE){
plot <- ggplot(data_plot,aes(x=frames,y=sign(signal)*sqrt(r2),group=group))+
geom_vline(data=data_plot,
aes(xintercept = sign_frames),
col=significant.col,
alpha=significant.alpha)+
geom_line()+
facet_wrap(~Channel)+
labs(labs)+
theme_bw()+
theme(legend.position="none")+
ylim(ylim)
}
else {
plot <- ggplot(data_plot,aes(x=frames,y=signal,group=group))+
geom_vline(data=data_plot,
aes(xintercept = sign_frames),
col=significant.col,
alpha=significant.alpha)+
geom_line()+
labs(labs)+
facet_wrap(~Channel)+
theme_bw()+
theme(legend.position="none")+
ylim(ylim)
}
}
}
if (type == "test_signal"){
if (length(levels(dta[,chan.var]))==1){
if (is.null(test.var)){
data_sign <- data.frame(frames=frames)
data_sign$significant <-
ifelse(data_sign$frames %in% data_sign$frames[tests_rst$significant],"sig","non-sig")
data_sign$sign_frames <-
ifelse(data_sign$frames %in% data_sign$frames[tests_rst$significant],data_sign$frames,NA)
data_sign$group <- rep(0,length(frames))
data_plot <- data_summarize(dta,data.var ,summary.var = chan.var,fun = curve.fun)
data_plot <- melt(data_plot)
colnames(data_plot)[1] <- "Channel"
data_plot <- data_plot[order(data_plot$Channel,data_plot$variable),]
data_plot$frames <- rep(frames,dim(data_plot)[1]/length(frames))
plot <- ggplot(data = data_plot,aes(x = frames, y = value))+
geom_line(col = curve.col[1])+
geom_vline(data = data_sign,aes(xintercept = sign_frames),
col = significant.col, alpha = significant.alpha)+
facet_wrap(~Channel)+
theme_bw()+
labs(labs)+
ylim(ylim)+
theme_bw()
}
else {
values <- curve.col[1]
for (i in 2:length(levels(as.factor(dta[,test.var])))){
values <- c(values,curve.col[i])
}
data_sign <- data.frame(frames=frames)
data_sign$significant <-
ifelse(data_sign$frames %in% data_sign$frames[tests_rst$significant],"sig","non-sig")
data_sign$sign_frames <-
ifelse(data_sign$frames %in% data_sign$frames[tests_rst$significant],data_sign$frames,NA)
data_sign$group <- rep(0,length(frames))
data_plot <- data_summarize(dta,data.var ,summary.var = c(chan.var,test.var),fun = curve.fun)
data_plot <- melt(data_plot)
colnames(data_plot)[1:2] <- c("Channel","Condition")
data_plot <- data_plot[order(data_plot$Channel,data_plot$Condition,data_plot$variable),]
data_plot$frames <- rep(frames,dim(data_plot)[1]/length(frames))
plot <- ggplot(data = data_plot,aes(x = frames, y = value))+
geom_line(aes(col=Condition))+
geom_vline(data = data_sign,aes(xintercept = sign_frames),
col = significant.col, alpha = significant.alpha)+
facet_wrap(~Channel)+
labs(labs)+
ylim(ylim)+
theme_bw()+
scale_color_manual(values = values,name = colnames(dta)[test.var])
}
}
else {
listlen <- length(tests_rst)
test_list = list()
for (i in 1: listlen){
test_list[[i]] <- data.frame(frames=frames)
test_list[[i]]$significant <- ifelse(test_list[[i]]$frames %in% test_list[[i]]$frames[tests_rst[[i]]$significant],"sig","non-sig")
test_list[[i]]$sign_frames <- ifelse(test_list[[i]]$frames %in% test_list[[i]]$frames[tests_rst[[i]]$significant],test_list[[i]]$frames,NA)
test_list[[i]]$group <- rep(0,length(frames))
test_list[[i]]$Channel <- names(tests_rst)[i]
}
test_plot <- test_list[[1]]
for (k in 2:listlen){
test_plot <- rbind(test_plot,test_list[[k]])
}
if (is.null(test.var)){
data_fun <- data_summarize(data = dta,data.var = data.var,
summary.var=chan.var,fun=curve.fun)
data_fun_long <- melt(data_fun,id=c(colnames(dta)[chan.var]))
data_fun_long <- data_fun_long[order(data_fun_long[,1],
data_fun_long[,2],
data_fun_long[,3]),]
colnames(data_fun_long)[3] <- "FUN"
colnames(data_fun_long)[1] <- c("Channel")
data_fun_long$frames <- rep(frames,dim(data_fun_long)[1]/length(frames))
data_plot <- merge(data_fun_long,
test_plot,
by =c("Channel","frames"))
plot <- ggplot(data_plot,aes(x=frames))+
geom_vline(aes(xintercept = sign_frames),
col=significant.col,
alpha=significant.alpha)+
geom_line(aes(y = FUN),col = curve.col[1])+
facet_wrap(~Channel)+
ylim(ylim)+
labs(labs)+
theme_bw()
}
else {
dta[,test.var] <- as.factor(dta[,test.var])
values <- curve.col[1]
for (i in 2:length(levels(as.factor(dta[,test.var])))){
values <- c(values,curve.col[i])
}
data_fun <- data_summarize(dta,data.var = data.var,summary.var = c(chan.var,test.var),fun=curve.fun)
data_fun_long <- melt(data_fun,id=c(colnames(dta)[chan.var],colnames(dta)[test.var]))
data_fun_long <- data_fun_long[order(data_fun_long[,1],data_fun_long[,2],data_fun_long[,3]),]
colnames(data_fun_long)[c(1,2,4)] <- c("Channel","Condition","FUN")
data_fun_long$frames <- c(rep(frames,(dim(data_fun_long)[1]/length(frames))))
data_plot <- merge(data_fun_long,test_plot,by =c("Channel","frames"))
plot <- ggplot(data_plot,aes(x=frames,group=Condition))+
geom_vline(aes(xintercept = sign_frames),
col=significant.col,alpha=significant.alpha)+
geom_line(aes(y = FUN,col=Condition))+
labs(labs)+# Need some changes ?
facet_wrap(~Channel)+
labs(labs)+
ylim(ylim)+
theme_bw()+
scale_color_manual(values = values,name = colnames(dta)[test.var])
}
}
}
}
# Draw the plot ( on 10/10 system)
if (scalp == T){} # not yet
# if type = "test" or "test_signal", return test results and plot
# else return only the plot (maybe we could return more things for users)
if (type == "test" | type == "test_signal") {
re_list <- list()
re_list[[1]] <- plot
re_list[[2]] <- tests_rst
names(re_list)[1:2] <- c("Plot","Test_Rst")
} else { re_list <- plot }
return(re_list)
}Examples
1. Explore the raw data
All subjects and all channels
Fig1 <- fdaplot(data = ERPdata ,
frames = 1:426 ,
data.var = 2:427 ,
subj.var = 430,
chan.var = 1,
test.var = 429,
type = "raw",
curve.col = c("blue","red"), # indicate the color of two conditions
ylim = c(20,-20))
Fig1
2. Explore the raw data
Select several subjects
Fig2 <- fdaplot(data = ERPdata ,
frames = 1:426 ,
data.var = 2:427 ,
subj.var = 430,
chan.var = 1,
test.var = 429,
subj.select = paste("subj",sample(1:20,3),sep =""), # Sample some subjects
type = "raw",
curve.col = c("blue","red"), # indicate the color of two conditions
ylim = c(20,-20))
Fig2
3. Explore the raw data
On the specific channel (e.g.Fp1)
Fig3 <- fdaplot(data = ERPdata ,
frames = 1:426 ,
data.var = 2:427 ,
subj.var = 430,
chan.var = 1,
test.var = 429,
subj.select = paste("subj",sample(1:20,3),sep =""), # Sample some subjects
chan.select = "Fp1",
type = "raw",
curve.col = c("blue","red"), # indicate the color of two conditions
ylim = c(20,-20))
Fig3
4. Summarize all subjects to a curve for gaining overall information
Type == “average” , return a summarize plot
Fig4 <- fdaplot(data = ERPdata ,
frames = 1:426 ,
data.var = 2:427 ,
subj.var = 430,
chan.var = 1,
test.var = 429,
type = "average",
curve.col = c("green","blue"), # Change the color of the curves
labs = list(x = "Time Points", y = "ERP Signal"), # Change the labs of the plot
curve.fun = function(x, d){return(mean(x[d]))}, # The default function is "mean"
ylim = c(20,-20))
Fig4
5. Summarize all subjects on a specific channel
Fig5 <- fdaplot(data = ERPdata ,
frames = 1:426 ,
data.var = 2:427 ,
subj.var = 430,
chan.var = 1,
test.var = 429,
chan.select = "Fp1",
type = "average")
Fig5
6. Explore the comparison using bootstraping interval between two conditions
using all subjects on all channels
Fig6 <- fdaplot(data = ERPdata ,
frames = 1:426 ,
data.var = 2:427 ,
subj.var = 430,
chan.var = 1,
test.var = 429,
type = "bootci",
curve.fun = function(x, d){return(mean(x[d]))}, # The default function to bootstrap
boot.num = 500 , # The times for bootstraping, default == 500
boot.intval = c(0.025,0.975), # The confidence interval
ci.alpha = 0.3, # The alpha of the ribbon on the plot
ylim = c(30,-30)) # Change the y limit
Fig6
7. Explore the comparison using bootstraping interval between two conditions
using all subjects on a specific channels (e.g. Fp1)
Fig7 <- fdaplot(data = ERPdata ,
frames = 1:426 ,
data.var = 2:427 ,
subj.var = 430,
chan.var = 1,
test.var = 429,
chan.select = "Fp1",
type = "bootci")
Fig7
8. Return the results after erpfatest, type == test or test == test_signal
Type == test, return a plot with the difference between two curve
Type == test_signal, return a plot with the original signal curve (like type == average)
But they also return the test results (a list) in the second element of the list
type == test
result <- fdaplot(data = ERPdata ,
frames = 1:426 ,
data.var = 2:427 ,
subj.var = 430,
chan.var = 1,
test.var = 429,
type = "test",
significant.col = "pink", # The color of significant window
significant.alpha = 0.2, # The alpha value of the significant window
design_model=(~Subject+Condition), # Some settings abouterpfatest
design0_model=(~Subject)) # Some settings abouterpfatest
Fig8 <- result$Plot
Test_Results <- result$Test_Rst
Fig8
type == test_signal
result2 <- fdaplot(data = ERPdata ,
frames = 1:426 ,
data.var = 2:427 ,
subj.var = 430,
chan.var = 1,
test.var = 429,
type = "test_signal",
significant.col = "pink", # The color of significant window
significant.alpha = 0.2, # The alpha value of the significant window
design_model=(~Subject+Condition), # Some settings abouterpfatest
design0_model=(~Subject))
Fig9 <- result2$Plot
Fig9
9. Return a test results on a specific channel (e.g. Fp1)
type == test
result_Fp1 <- fdaplot(data = ERPdata ,
frames = 1:426 ,
data.var = 2:427 ,
subj.var = 430,
chan.var = 1,
test.var = 429,
type = "test",
chan.select = "Fp1",
significant.col = "pink", # The color of significant window
significant.alpha = 0.2, # The alpha value of the significant window
design_model=(~Subject+Condition), # Some settings abouterpfatest
design0_model=(~Subject)) # Some settings abouterpfatest
Fig10 <- result_Fp1$Plot
Fig10
##### type == test_signal
result2_Fp1 <- fdaplot(data = ERPdata ,
frames = 1:426 ,
data.var = 2:427 ,
subj.var = 430,
chan.var = 1,
test.var = 429,
chan.select = "Fp1",
type = "test_signal",
significant.col = "pink", # The color of significant window
significant.alpha = 0.2, # The alpha value of the significant window
design_model=(~Subject+Condition), # Some settings abouterpfatest
design0_model=(~Subject))
Fig11 <- result2_Fp1$Plot
Fig11