ERP package
Question :
data in a folder: How do we help user to read the data ?
Improve : check functions
Example for several functions
NIRS data
Tutorial:Example for several functions
Project paper outline : find a demo paper
package
documentation
single .R file or a function a file ?
Outline
data
1. ERPdata : maybe a formal data for package
2. NIRSdata
preprocessing
1. list_to_df
2. data_select
3. data_summarize
4. downsample
EDA
1.edaplot
2.ciplot
tests
1.chan_test
2.mcplot
ERP only
1. scalpplot
2. topograph
Data
1. ERPdata
2. NIRSdata
library(ERP);require(mnormt);require(fdrtool);library(ggplot2);library(dplyr)
library(erpR);require(akima);library(reshape2);library(boot)
data(ERPsets)
load("ERPdata.RData")preprocessing
1. list_to_df
2. data_select
3. data_summarize
4. Down sample
list_to_df
It is a function that could transformed a list to a dataframe for our package.
###list2data
list_to_df <- function(list_data,frames){
# some check function
for (i in 1:length(list_data)){
list_data[[i]]$frames <-frames
list_data[[i]]$list.name <- names(list_data)[i]
}
Data_list <- lapply(list_data,melt,id=c("frames","list.name"))
Data_list <- lapply(Data_list,reshape,
timevar = "frames",idvar = c("variable","list.name"),
direction = "wide")
Ana_data <- Data_list[[1]]
for (i in 2:length(ERPsets)){
Ana_data <- rbind(Ana_data,Data_list[[i]])
}
colnames(Ana_data)[2] <- "Channel"
rownames(Ana_data) <- 1:dim(Ana_data)[1]
return(Ana_data)
}ERP_df <- list_to_df(list_data=ERPsets, # input list_data
frames= 1:426) # input frames
ERP_df <- AddExpCondSub(data = ERP_df, # input the transformed data
list.name_col = 1) # list.name column
#ERP_df <- ERP_df[,-1] # remove list name column, Depend on user's need
head(ERP_df[,c(1:3,427:430)],3) # look at the data list.name Channel value.1 value.425 value.426 Experiment
1 Exp1_word_subj1 Fp1 0.36995740 14.160980 9.971283 Exp1
2 Exp1_word_subj1 Fp2 -0.02759907 13.750350 9.678596 Exp1
3 Exp1_word_subj1 F3 0.42971300 6.269233 4.413941 Exp1
Condition
1 word
2 word
3 word2. data_select
It could select data by subject and channel
data_select <- function(data,frames,datacol,subjcol=NULL,chancol=NULL,othvarcol=NULL,
select_subj=NULL,
select_chan=NULL,...){
# some check function
subj_select <- function(data,frames,datacol,subjcol,chancol=NULL,othvarcol=NULL,
select_subj,...){
dta <- data
num <- length(select_subj)
data_list <- list()
for (i in 1:num){
data_new <- subset(dta,dta[,subjcol]==select_subj[i])
data_list[[i]] <- data_new
}
data_select <- data_list[[1]]
if (num != 1) {
for (i in 2:num) {
data_select <- rbind(data_select,data_list[[i]])
}
}
rownames(data_select) <- 1:dim(data_select)[1]
return (data_select)
}
chan_select <- function(data,frames,datacol,subjcol=NULL,chancol,othvarcol=NULL,
select_chan,...){
dta <- data
num <- length(select_chan)
data_list <- list()
for (i in 1:num){
data_new <- subset(dta,dta[,chancol]==select_chan[i])
data_list[[i]] <- data_new
}
data_select <- data_list[[1]]
if (num != 1){
for (i in 2:num){
data_select <- rbind(data_select,data_list[[i]])
}
}
rownames(data_select) <- 1:dim(data_select)[1]
return (data_select)
}
joint_select <- function(data,frames,datacol,subjcol,chancol,othvarcol=NULL,
select_subj,
select_chan,...){
dta <- data
ind_data <- subj_select(dta,frames,datacol,subjcol,chancol,othvarcol,
select_subj = select_subj)
ind_ele_data <- chan_select(ind_data,frames,datacol,subjcol,chancol,othvarcol,
select_chan = select_chan)
rownames(ind_ele_data) <- 1 :dim(ind_ele_data)[1]
return(ind_ele_data)
}
if (is.null(select_subj)==FALSE & is.null(select_chan)==FALSE){
dta <- joint_select(data = data,frames = frames,
datacol,subjcol,chancol,othvarcol,
select_subj=select_subj,
select_chan=select_chan)
} else if (is.null(select_subj)==FALSE & is.null(select_chan)== TRUE) {
dta <- subj_select(data=data,frames=frames,
datacol,subjcol,chancol,othvarcol,
select_subj=select_subj)
} else if (is.null(select_chan)==FALSE & is.null(select_subj)== TRUE) {
dta <- chan_select(data,frames,
datacol,subjcol,chancol,othvarcol,
select_chan=select_chan)
} else {
dta <- data
}
return(dta)
}S1S10Fp1Fp2 <- data_select(data = ERPdata,
frames = 1:426,
datacol = 2:427,
subjcol = 430,
chancol = 1,
othvarcol = c(428:429,431:432),
select_subj = c("subj1","subj2"),
select_chan=c("CZ","Fp1"))
CZ <- data_select(data = ERPdata,
frames = 1:426,
datacol = 2:427,
subjcol = 430,
chancol = 1,
othvarcol = c(428:429,431:432),
#select_subj = c("subj1","subj10"),
select_chan=c("CZ"))
S1S10 <- data_select(data = ERPdata,
frames = 1:426,
datacol = 2:427,
subjcol = 430,
chancol = 1,
othvarcol = c(428:429,431:432),
select_subj = c("subj1","subj10"))
dim(S1S10Fp1Fp2);dim(CZ);dim(S1S10)## [1] 8 432## [1] 40 432## [1] 136 4323. Data_summarize :
We could aggregate data by the column like conditions, subjects or channel.
data_summarize <- function(data,frames,datacol,subjcol=NULL,chancol=NULL,othvarcol=NULL,
summarycol,
fun=mean,
select_subj=NULL,
select_chan=NULL,...){
#some check function
options(warn=-1) # should close the warnings?
# selection
dta <- data_select(data,frames,datacol,subjcol,chancol,othvarcol,
select_subj,
select_chan)
# process
agglength <- length(summarycol)
aggvar_list <- list(dta[,summarycol[1]])
if (agglength > 1){
for (i in 2:agglength ){
aggvar_list <- append(aggvar_list,list(dta[,summarycol[i]]))
}
}
aggdata <- aggregate(dta[,datacol],by=aggvar_list,
fun,...)
aggdata <- aggdata[,1:(agglength+length(datacol))]
for (i in 1: agglength){
colnames(aggdata)[i] <- colnames(dta)[summarycol[i]]
}
rownames(aggdata) <- 1:dim(aggdata)[1]
return(aggdata)
}A <-data_summarize (data = ERPdata,
frames = 1:426,
datacol = 2:427,
#you could put all elements you want to aggregate in datacol argument
subjcol = 430,
chancol = 1,
othvarcol = c(428:429,431:432),
summarycol= c(1,429),
fun=median,
# could also select data
select_chan = c("CZ","Fp1"),
select_subj = c("subj1","subj10"))
tail(A[,1:6]);dim(A) Channel Condition value.1 value.2 value.3 value.4
1 Fp1 nonword -1.4049110 -1.9667935 -1.7905150 -1.7634650
2 CZ nonword -1.8309385 -2.4948925 -2.1159490 -1.9471855
3 Fp1 word 0.4695684 0.1779768 -0.5842498 -0.9740931
4 CZ word 0.8391661 0.8317206 0.2696740 0.1090901[1] 4 4284. Down Sample:
We could down sample the signal
downsample <- function(data,
datacol,
binwidth=10,
movinginterval=NULL) {
if (is.null(movinginterval) == FALSE) {
if (movinginterval >= binwidth){stop("movinginterval should not bigger than binwidth!")}
dta <- data
dta_signal <- dta[,datacol]
dta_othvar <- dta[,-datacol]
num1 <- (dim(dta_signal)[2] - (dim(dta_signal)[2] %% binwidth)) /binwidth
num2 <- dim(dta_signal)[2] %% binwidth
dta_downsample <- data.frame(melt(apply(dta_signal[,1:binwidth],1,mean)))
i = movinginterval - 1
while ((binwidth+i) <= dim(dta_signal)[2]){
dta_downsample <- cbind(dta_downsample,melt(apply(dta_signal[,(1+i):(binwidth+i)],1,mean)))
i = i + movinginterval -1
}
if (num2 >= 1) {
dta_downsample <- cbind(dta_downsample,
value=melt(apply(dta_signal[,(1+i):dim(dta_signal)[2]],1,mean)))
}
colnames(dta_downsample) <- paste("value",1:dim(dta_downsample)[2],sep=".")
dta_final <- cbind(dta_othvar,dta_downsample)
} else {
dta <- data
dta_signal <- dta[,datacol]
dta_othvar <- dta[,-datacol]
num1 <- (dim(dta_signal)[2] - (dim(dta_signal)[2] %% binwidth))/binwidth
num2 <- dim(dta_signal)[2] %% binwidth
dta_downsample <- data.frame(melt(apply(dta_signal[,1:binwidth],1,mean)))
for (i in 1 : num1-1){
dta_downsample <- cbind(dta_downsample,
melt(apply(dta_signal[,(i*binwidth+1):((i+1)*binwidth)],
1,mean)))
}
if (num2 == 1) {
dta_downsample <- cbind(dta_downsample,
value=dta_signal[,(num1*binwidth+num2)])
###### notice
}
if (num2 > 1) {
dta_downsample <- cbind(dta_downsample,
melt(apply(dta_signal[,(num1*binwidth+1):(num1*binwidth+num2)],
1,mean)))
}
dta_downsample <- dta_downsample[,-1]
colnames(dta_downsample) <- paste("value",1:dim(dta_downsample)[2],sep=".")
dta_final <- cbind(dta_othvar,dta_downsample)
}
return(dta_final)
}down1 <- downsample(data = ERPdata,
datacol = 2:427,
binwidth = 10,
movinginterval=NULL)
down2 <- downsample(data = ERPdata,
datacol = 2:427,
binwidth = 10,
movinginterval=9) # movinginterval shoud not bigger than binwidth
dim(down1) ; dim(down2)[1] 1360 49[1] 1360 60EDA
1.edaplot
2.bootplot
1.edaplot
The function “edaplot”" uses ggplot2 grammar, so it has several flexible options.
# group comparison please put in data after aggregate_raw
edaplot <- function(data,frames=NULL,datacol,subjcol=NULL,chancol=NULL,othvarcol=NULL,
outlinesub=NULL,outcolor="red",
select_subj=c(NULL),
select_chan=c(NULL),...){
#some check functions
#if (is.null(frames) == FALSE)
# if (length(frames) != (ncol(data)-1-length(othvarcol)))
# stop(paste("frames should be either null or of length",
# (ncol(data)-1-length(othvarcol))))
#if (is.null(frames) == FALSE) {
# if (any(frames != sort(frames)))
# stop("frames should be an ascending sequence of integers")
# }
#if (is.null(frames))
# frames = 1:(ncol(data)-1-length(othvarcol))
#selection
dta <- data_select(data,frames,datacol,subjcol,chancol,othvarcol,
select_subj,
select_chan,...)
# plot
subvar <- variable.names(dta)[subjcol]
dta$groupvar <- rownames(dta)
datalong <- melt(dta,
id=c(variable.names(dta)[c(subjcol,chancol,othvarcol)],
"groupvar"))
datalong <- datalong[order(datalong$groupvar),]
datalongorder <- datalong
datalongorder$frames <- rep(frames,length(datalongorder[,1])/length(frames))
if (is.null(outlinesub) == FALSE){ # how to outline several subjects (and color)
data2 <- subset(datalongorder,datalongorder[,1]==outlinesub)
plot <- ggplot(datalongorder,
aes(x=frames,y=value,group=groupvar,...))+
geom_line()+
geom_line(data=data2,aes(x=frames,y=value),col=outcolor)
# need warning for covering geom_line()
} else {
plot <- ggplot(datalongorder,
aes(x=frames,y=value,group=groupvar))+
geom_line()
}
return(plot)
}#Although Full data (all trials) work fine, I recommened that the argument erpdata should be a single subject data or an aggregate data.
edaplot(ERPdata,
frames = 1:426,
datacol=2:427,
subjcol=430,
chancol=1,
othvarcol=c(428:429,431:432),
# choose the subject you want to show(optional)
select_sub = c("subj9","subj1","subj5","subj8"),
# choose the channel you want to show(optional)
select_chan = c("F3","F4"))+
facet_grid(Channel~Condition)
edaplot(ERPdata,
frames = 1:426,
datacol=2:427,
subjcol=430,
chancol=1,
othvarcol=c(428:429,431:432),
# choose the subject you want to show(optional)
select_sub = c("subj9","subj1","subj5","subj8"),
# choose the channel you want to show(optional)
select_chan = c("F3","F4"),
# highlight a single subject with the color you want
outlinesub="subj9",outcolor = "blue")+
facet_grid(Channel~Condition)
edaplot(ERPdata,
frames=1:426,
datacol = 2:427,subjcol=430,chancol=1,othvarcol=c(428:429,431:432),
select_chan = c("F3","F4","CZ"),
select_sub = c("subj9","subj1","subj5","subj8"))+
# One subject one color
geom_line(aes(col=Subject))+ # will cover outline sub
facet_grid(Condition~Channel)
edaplot(ERPdata,
frames=1:426,
datacol = 2:427,subjcol=430,chancol=1,othvarcol=c(428:429,431:432),
select_chan = c("F3","F4","CZ"))+
# Color depend on IQ (Size , alpha either)
geom_line(aes(col=IQ,alpha=Channel,size=Condition))+
facet_grid(Condition~Channel)
edaplot(ERPdata,
frames=1:426,
datacol = 2:427,subjcol=430,chancol=1,othvarcol=c(428:429,431:432),
select_chan = c("F3","F4","CZ"))+
# One Condition one color
geom_line(aes(col=Condition))+
facet_grid(.~Channel)
edaplot(ERPdata,
frames=1:426,
datacol = 2:427,subjcol=430,chancol=1,othvarcol=c(428:429,431:432),
select_chan = c("F3","F4","CZ"))+
# One Condition one color
geom_line(aes(col=Condition))+
facet_grid(.~Channel)+
scale_colour_manual(values=c("red","blue"),name="Cond",label=c("A","B"))
edaplot(ERPdata,
frames=1:426,
datacol = 2:427,subjcol=430,chancol=1,othvarcol=c(428:429,431:432),
select_chan = c("F3","F4","CZ"))+
geom_line(aes(col=Condition))+
facet_grid(Condition~Channel)
edaplot(ERPdata,
frames=1:426,
datacol = 2:427,subjcol=430,chancol=1,othvarcol=c(428:429,431:432),
select_chan = c("F3","F4","CZ"))+
geom_line(aes(col=Condition))+
facet_grid(Condition~Channel)+
# put on the summary line NOTE: need a group=NULL argument
stat_summary(aes(group=NULL),fun.y = "mean",
colour = "purple", size = 0.5, geom = "line")
edaplot(ERPdata,
frames=1:426,
datacol = 2:427,subjcol=430,chancol=1,othvarcol=c(428:429,431:432),
select_chan = c("F3","F4","CZ"))+
geom_line(aes(col=Condition))+
facet_grid(Condition~Channel)+
theme_bw()+
theme(legend.position="top")+
xlim(-100,500)+
ylim(-50,50)+
labs(list(title = "Flexible",x="time",y="signal"))+
geom_vline(xintercept = c(0,213,426),col="yellow")
2.ciplot
ciplot use a bootstaping method to draw the confidence interval on two (or more) compared
variable.
ciplot <- function(data,frames,datacol,subjcol=NULL,chancol=NULL,othvarcol=NULL,
cpvarcol=NULL, signal_line_col="black",
fun=samplemean <- function(x, d){return(mean(x[d]))}, # very unfriendly setting
bootnum=500,
bootintval=c(.025,.975),
bootalpha=0.3,
select_subj = NULL,
select_chan = NULL,...){
# need some check function
bootstrap <- function(x,bootnum,bootfun,bootintval=c(0.025,0.975),quantilenum,...){
boot_result <- boot(x,statistic = bootfun,R = bootnum,...) #fun
return(quantile(boot_result$t,bootintval,na.rm = T)[quantilenum])
}
# data selection
dta <- data_select(data,frames,datacol,subjcol,chancol,othvarcol,
select_subj,
select_chan,...)
# Do you want to compare between variable ?
if (is.null(cpvarcol)==TRUE){
data_fun <- data_summarize(dta,frames,datacol,subjcol,chancol,othvarcol,
summarycol = c(chancol),fun=fun)
data_Q1 <- data_summarize(dta,frames,datacol,subjcol,chancol,othvarcol,
summarycol = c(chancol),
fun=bootstrap,bootnum=bootnum,bootfun=fun,
bootintval=bootintval,quantilenum=1)
data_Q2 <- data_summarize(dta,frames,datacol,subjcol,chancol,othvarcol,
summarycol = c(chancol),
fun=bootstrap,bootnum=bootnum,bootfun=fun,
bootintval=bootintval,quantilenum=2)
data_fun_long <- melt(data_fun,id=c(colnames(dta)[chancol]))
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"
data_Q1_long <- melt(data_Q1,id=c(colnames(dta)[chancol]))
data_Q1_long <- data_Q1_long[order(data_Q1_long[,1],
data_Q1_long[,2],
data_Q1_long[,3]),]
data_Q2_long <- melt(data_Q2,id=c(colnames(dta)[chancol]))
data_Q2_long <- data_Q2_long[order(data_Q2_long[,1],
data_Q2_long[,2],
data_Q2_long[,3]),]
data_fun_long$Q1 <- data_Q1_long[,3]
data_fun_long$Q2 <- data_Q2_long[,3]
data_fun_long$frames <- c(rep(frames,(dim(data_fun_long)[1]/length(frames)))) #change
colnames(data_fun_long)[1] <- c("Channel")
plot <- ggplot(data_fun_long,aes(x=frames))+
geom_ribbon(aes(x=frames, ymax=Q2, ymin=Q1),
fill=signal_line_col,
alpha=bootalpha)+
geom_line(aes(y = FUN),
col=signal_line_col)+
labs(y="Signal")+# Need some changes ?
facet_wrap(~Channel)+
theme(legend.position="none")
} else {
dta[,cpvarcol] <- as.factor(dta[,cpvarcol])
dta[,chancol] <- as.factor(dta[,chancol])
data_fun <- data_summarize(dta,frames,datacol,subjcol,chancol,othvarcol,
summarycol = c(chancol,cpvarcol),fun=fun)
data_Q1 <- data_summarize(dta,frames,datacol,subjcol,chancol,othvarcol,
summarycol = c(chancol,cpvarcol),
fun=bootstrap,bootnum=bootnum,bootfun=fun,
bootintval=bootintval,quantilenum=1)
data_Q2 <- data_summarize(dta,frames,datacol,subjcol,chancol,othvarcol,
summarycol = c(chancol,cpvarcol),
fun=bootstrap,bootnum=bootnum,bootfun=fun,
bootintval=bootintval,quantilenum=2)
data_fun_long <- melt(data_fun,id=c(colnames(dta)[chancol],colnames(dta)[cpvarcol]))
data_fun_long <- data_fun_long[order(data_fun_long[,1],
data_fun_long[,2],
data_fun_long[,3]),]
colnames(data_fun_long)[4] <- "FUN"
data_Q1_long <- melt(data_Q1,id=c(colnames(dta)[chancol],colnames(dta)[cpvarcol]))
data_Q1_long <- data_Q1_long[order(data_Q1_long[,1],
data_Q1_long[,2],
data_Q1_long[,3]),]
data_Q2_long <- melt(data_Q2,id=c(colnames(dta)[chancol],colnames(dta)[cpvarcol]))
data_Q2_long <- data_Q2_long[order(data_Q2_long[,1],
data_Q2_long[,2],
data_Q2_long[,3]),]
data_fun_long$Q1 <- data_Q1_long[,4]
data_fun_long$Q2 <- data_Q2_long[,4]
data_fun_long$frames <- c(rep(frames,(dim(data_fun_long)[1]/length(frames))))
colnames(data_fun_long)[1:2] <- c("Channel","Condition")
plot <- ggplot(data_fun_long,aes(x=frames,group=Condition))+
geom_ribbon(aes(x=frames, ymax=Q2, ymin=Q1,fill=Condition),
alpha=bootalpha)+
geom_line(aes(y = FUN,col=Condition))+
labs(y="Signal")+# Need some changes ?
facet_wrap(~Channel)
}
return(plot)
}
ciplot(ERPdata, # input the data
frames=1:426,
datacol=2:427,
subjcol=430,
chancol=1,
othvarcol=c(428:429,431:432),
# Important : the column of that single variable you want to compare
# and if (NULL) the function will return a single line and interval
cpvarcol = NULL ,
signal_line_col = "blue", # work if cpvarcol = NULL
# boot package
fun=samplemean <- function(x, d){return(mean(x[d]))}, # the function use to draw boot interval and line
bootnum=300, # bootsraping number
bootintval=c(.025,.975), # bootstrap confidence interval
bootalpha=0.5,# the value of alpha on the plot
#sim = "parametric", # other setting in "boot package"
# Data selection
select_chan = c("Fp1","Fp2"))+ # select data
#select_subj = c("subj1","subj2","subj3","subj10"))+
#other setting in ggplot2
ylim(-5,5)
ciplot(ERPdata, # input the data
frames=1:426,
datacol=2:427,
subjcol=430,
chancol=1,
othvarcol=c(428:429,431:432),
# have cpvarcol
cpvarcol = 429 ,
#signal_line_col = "blue",
fun=samplemean <- function(x, d){return(mean(x[d]))},
bootnum=300,
bootintval=c(.025,.975),
bootalpha=0.5)+
ylim(-10,10)+
#other setting in ggplot2
scale_fill_manual(values=c("red","blue"),name="Word or Non Word",label=c("No","Yes"))+
scale_colour_manual(values=c("red","blue"),name="Word or Non Word",label=c("No","Yes"))+
theme(legend.position = "bottom")
tests
1.chan_test
2.mcplot
1.multichtest
chan_test <- function(data,datacol,chancol,
testtype="erpfatest",
# do not specify model.matrix like original test function###
design_model,
# do not specify model.matrix like original test function
design0_model=NULL,...){
dta <- data
dta[,chancol]=as.factor(dta[,chancol])
levelnum <- length(levels(dta[,chancol]))
if (levelnum == 1) {
design <- model.matrix(design_model,data=data)
if (is.null(design0_model)==F){
design0 <- model.matrix(design0_model,data=data)
}
if ( testtype == "erpavetest" ){
test_list <- erpavetest(dta[,datacol],design,design0,...)
}
if (testtype == "erpfatest") {
test_list <- erpfatest(dta[,datacol],design,design0,...)
}
if (testtype == "erptest") {
test_list <- erptest(dta[,datacol], design, design0,...)
}
if (testtype == "gbtest") {
test_list <- gbtest(dta[,datacol], design, design0,...)
}
} else {
test_list=list()
dta_list=list()
for (i in 1:levelnum) {
dta_list[[i]] <- subset(dta,dta[,chancol]==(levels(dta[,chancol])[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]])
}
if ( testtype == "erpavetest" ){
test_list[[i]] <- erpavetest(dta_list[[i]][,datacol],
design,design0,...)
}
if (testtype == "erpfatest") {
test_list[[i]] <- erpfatest(dta_list[[i]][,datacol],
design,design0,...)
}
if (testtype == "erptest") {
test_list[[i]] <- erptest(dta_list[[i]][,datacol],
design, design0,...)
}
if (testtype == "gbtest") {
test_list[[i]] <- gbtest(dta_list[[i]][,datacol],
design, design0,...)
}
names(test_list)[i] <- levels(dta[,chancol])[i]
}
}
return(test_list)
}
ERP_test <- chan_test(ERPdata,2:427,chancol=1,testtype="erpfatest",
design_model=(~Subject+Condition), # do not specify "model.matrix" like original test function
design0_model=(~Subject)) # do not specify "model.matrix" like original test function
# other seeting in ERP package2. mcplot
plot original signal curve and highlight the significant location
and it is possible to add some bootstrap interval
mcplot <- function(tests_rst,
type = "test", # type = "test" or "signal"
multi = F, # IMPORTANT : depend on your tests rst
# IMPORTANT : Work only if type = test
cor = FALSE,
# IMPORTANT : depend on your tests rst and data
data,frames,datacol,subjcol=NULL,chancol=NULL,othvarcol=NULL,cpvarcol=NULL,
significant_col = "pink" , significant_alpha = 0.2,
# IMPORTANT : work only if type = signal
wantbootplot = FALSE,
fun = samplemean <- function(x, d){return(mean(x[d]))},
bootnum = 10,
bootintval = c(.025,.975),
bootalpha = 0.3 ) {
options(warn=-1)
# some check functions
if (type == "test") {
if (multi==FALSE){
data <- data.frame(signal=as.numeric(tests_rst$signal))
data$frames = frames
data$significant <-
ifelse(data$frames %in% data$frames[tests_rst$significant],
"sig","non-sig")
data$sign_frames <-
ifelse(data$frames %in% data$frames[tests_rst$significant],
data$frames,NA)
data$group <- rep(0,length(data$signal))
data$r2 <- tests_rst$r2
if (cor == TRUE){
plot <- ggplot(data,
aes(x=frames,y=sign(signal)*sqrt(r2),group=group))+
geom_vline(data=data,
aes(xintercept = sign_frames,
col=significant_col),
alpha=significant_alpha)+
geom_line()+
labs(y = "Correlation")+
theme(legend.position="none")
} else {
plot <- ggplot(data,aes(x=frames,y=signal,group=group))+
geom_vline(data=data,
aes(xintercept = sign_frames),
col=significant_col,
alpha=significant_alpha)+
geom_line()+
labs(y = "Signal")+
theme(legend.position="none")
}
} else {
listlen <- length(tests_rst)
data_list <- list()
for (k in 1:listlen){
data_list[[k]] <- data.frame(signal=as.numeric(tests_rst[[k]]$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]]$Channel <- names(tests_rst)[k]
}
data_plot <- data_list[[1]]
for (j in 2 : listlen){
data_plot <- rbind(data_plot,data_list[[j]])
}
if (cor == TRUE){
plot <- ggplot(data_plot,aes(x=frames,y=sign(signal)*sqrt(r2),group=group))+
geom_vline(data=data_plot,
aes(xintercept = sign_frames),
col=significant_col,
alpha=significant_alpha)+
geom_line()+
facet_wrap(~Channel)+
labs(y = "Correlation")+
theme(legend.position="none")
} else {
plot <- ggplot(data_plot,aes(x=frames,y=signal,group=group))+
geom_vline(data=data_plot,
aes(xintercept = sign_frames),
col=significant_col,
alpha=significant_alpha)+
geom_line()+
labs(y="Signal")+
facet_wrap(~Channel)+
theme(legend.position="none")
}
}
}
if (type == "signal") {
bootstrap <- function(x,bootnum,bootfun,bootintval=c(0.025,0.975),quantilenum,...){
boot_result <- boot(x,statistic = bootfun,R = bootnum,...) #fun
return(quantile(boot_result$t,bootintval,na.rm = T)[quantilenum])}
dta <- data # selection ?
if (multi == F ){
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))
plot <- ciplot(dta,frames,
datacol,subjcol,chancol,othvarcol=othvarcol,
cpvarcol,
fun = fun,bootnum,bootintval=bootintval,
bootalpha=ifelse(wantbootplot==T,bootalpha,0))+
geom_vline(data=data_sign,
aes(xintercept = sign_frames),
col=significant_col,
alpha=significant_alpha)+
labs(y="Signal")
}
if (multi == T) {
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]]$Channel <- names(tests_rst)[i]
}
test_plot <- test_list[[1]]
for (k in 2:listlen){
test_plot <- rbind(test_plot,test_list[[k]])
}
if (is.null(cpvarcol)==TRUE){
data_fun <- data_summarize(dta,frames,
datacol,subjcol,chancol,othvarcol,
summarycol = c(chancol),fun=fun)
data_Q1 <- data_summarize(dta,frames,
datacol,subjcol,chancol,othvarcol,
summarycol = c(chancol),
fun=bootstrap,
bootnum=bootnum,bootfun=fun,
bootintval=bootintval,quantilenum=1)
data_Q2 <- data_summarize(dta,frames,
datacol,subjcol,chancol,othvarcol,
summarycol = c(chancol),
fun=bootstrap,bootnum=bootnum,bootfun=fun,
bootintval=bootintval,quantilenum=2)
data_fun_long <- melt(data_fun,id=c(colnames(dta)[chancol]))
data_fun_long <- data_fun_long[order(data_fun_long[,1],
data_fun_long[,2],
data_fun_long[,3]),]
colnames(data_fun_long)[3] <- "FUN"
data_Q1_long <- melt(data_Q1,id=c(colnames(dta)[chancol]))
data_Q1_long <- data_Q1_long[order(data_Q1_long[,1],
data_Q1_long[,2],
data_Q1_long[,3]),]
data_Q2_long <- melt(data_Q2,id=c(colnames(dta)[chancol]))
data_Q2_long <- data_Q2_long[order(data_Q2_long[,1],
data_Q2_long[,2],
data_Q2_long[,3]),]
data_fun_long$Q1 <- data_Q1_long[,3]
data_fun_long$Q2 <- data_Q2_long[,3]
data_fun_long$frames <- c(rep(frames,
(dim(data_fun_long)[1]/length(frames))))
colnames(data_fun_long)[1] <- c("Channel")
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_ribbon(aes(x=frames, ymax=Q2, ymin=Q1),
alpha=ifelse(wantbootplot==T,bootalpha,0))+
geom_line(aes(y = FUN))+
facet_wrap(~Channel)+
labs(y="Signal")+
theme(legend.position="none")
} else {
dta[,cpvarcol] <- as.factor(dta[,cpvarcol])
dta[,chancol] <- as.factor(dta[,chancol])
data_fun <- data_summarize(dta,frames,
datacol,subjcol,chancol,othvarcol,
summarycol = c(chancol,cpvarcol),fun=fun)
data_Q1 <- data_summarize(dta,frames,
datacol,subjcol,chancol,othvarcol,
summarycol = c(chancol,cpvarcol),
fun=bootstrap,bootnum=bootnum,bootfun=fun,
bootintval=bootintval,quantilenum=1)
data_Q2 <- data_summarize(dta,frames,
datacol,subjcol,chancol,othvarcol,
summarycol = c(chancol,cpvarcol),
fun=bootstrap,bootnum=bootnum,bootfun=fun,
bootintval=bootintval,quantilenum=2)
data_fun_long <- melt(data_fun,
id=c(colnames(dta)[chancol],
colnames(dta)[cpvarcol]))
data_fun_long <- data_fun_long[order(data_fun_long[,1],
data_fun_long[,2],
data_fun_long[,3]),]
colnames(data_fun_long)[4] <- "FUN"
data_Q1_long <- melt(data_Q1,
id=c(colnames(dta)[chancol],
colnames(dta)[cpvarcol]))
data_Q1_long <- data_Q1_long[order(data_Q1_long[,1],
data_Q1_long[,2],
data_Q1_long[,3]),]
data_Q2_long <- melt(data_Q2,
id=c(colnames(dta)[chancol],
colnames(dta)[cpvarcol]))
data_Q2_long <- data_Q2_long[order(data_Q2_long[,1],
data_Q2_long[,2],
data_Q2_long[,3]),]
data_fun_long$Q1 <- data_Q1_long[,4]
data_fun_long$Q2 <- data_Q2_long[,4]
data_fun_long$frames <-
c(rep(frames,(dim(data_fun_long)[1]/length(frames))))
colnames(data_fun_long)[1:2] <- c("Channel","Condition")
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_ribbon(aes(x=frames, ymax=Q2, ymin=Q1,fill=Condition),
alpha=bootalpha)+
geom_line(aes(y = FUN,col=Condition))+
labs(y="Signal")+# Need some changes ?
facet_wrap(~Channel)+
theme(legend.position="none")
}
}
}
return(plot)
}#### type = test ; multi = TRUE ; cor = FALSE
ERP_test <- chan_test(ERPdata,2:427,chancol=1,testtype="erpfatest",
design_model=(~Subject+Condition),
design0_model=(~Subject))
mcplot(tests_rst = ERP_test,
# You could select , type = "test" or "signal"
type = "test",
# it is depend on your tests rst
multi = T,
# Work only if type = test
cor = FALSE,
# data information
data = ERPdata , frames = 1:426 , datacol = 2:427, subjcol = 430 , chancol = 1 , othvarcol = c(428:429,431:432),
# it is depend on your tests rst
# if type is "test" , it is not important
# cpvarcol = 429 ,
# the color of significant window
significant_col = "pink" , significant_alpha = 0.2)
# only if type = signal
# wantbootplot = TRUE,
# fun = samplemean <- function(x, d){return(mean(x[d]))}, # boot package
# bootnum = 10,
# bootintval = c(.025,.975),
# bootalpha = 0.3 )
#### type = test ; multi = FALSE ; cor = FALSE
# select CZ data
CZ <- data_select(data = ERPdata,frames = 1:426,
datacol = 2:427,subjcol = 430,chancol = 1,othvarcol = c(428:429,431:432),
select_chan=c("CZ"))
CZ_test <- erpfatest(CZ[2:427],
design=model.matrix(~Subject+Condition,data=CZ),
design0=model.matrix(~Subject,data=CZ))
mcplot(tests_rst = CZ_test,
type = "test",
# Single Channel, So multi = FALSE
multi = F,
cor = FALSE,
data = CZ , frames = 1:426 ,
datacol = 2:427, subjcol = 430 , chancol = 1 ,
othvarcol = c(428:429,431:432) , cpvarcol = 429 ,
significant_col = "lightblue" , significant_alpha = 0.5)
#### type = test; multi = T ; cor = T
ERP_cor_test <- chan_test(ERPdata,2:427,chancol=1,testtype="erpfatest",
design_model=(~IQ+Condition),
design0_model=(~Condition))
mcplot(tests_rst = ERP_cor_test ,
type = "test",
multi = T,
# Work only if type = test
cor = T,
data = ERPdata , frames = 1:426 ,
datacol = 2:427, subjcol = 430 , chancol = 1 ,
othvarcol = c(428:429,431:432) , cpvarcol = 429 ,
significant_col = "lightblue" , significant_alpha = 0.5)
# type = test; multi = F ; cor = T
CZ <- data_select(data = ERPdata,frames = 1:426,
datacol = 2:427,subjcol = 430,chancol = 1,othvarcol = c(428:429,431:432),
select_chan=c("CZ"))
CZ_word <- subset(CZ,CZ$Condition=="word")
CZ_word_test <- erpfatest(CZ_word[2:427],
design=model.matrix(~IQ,data=CZ_word))
mcplot(tests_rst = CZ_word_test ,
type = "test",
multi = F,
# Work only if type = test
cor=T,
data=CZ_word,frames=1:426,datacol=2:427,subjcol=430,chancol=1,othvarcol=c(428:429,431:432), #cpvarcol = NULL ,
significant_col = "purple" , significant_alpha = 0.2)
# type = signal ; multi = FALSE ; Have cpvar
CZ <- data_select(data = ERPdata,frames = 1:426,
datacol = 2:427,subjcol = 430,chancol = 1,othvarcol = c(428:429,431:432),
select_chan=c("CZ"))
CZ_test <- erpfatest(CZ[2:427],
design=model.matrix(~Subject+Condition,data=CZ),
design0=model.matrix(~Subject,data=CZ))
mcplot(tests_rst = CZ_test ,
# type = "test" or "signal"
type = "signal",
# depend on your tests_rst
multi = F,
# only if type = test
cor = FALSE,
data = CZ , frames = 1:426 ,datacol = 2:427, subjcol = 430 , chancol = 1 , othvarcol = c(428:429,431:432) ,
# depend on your r=test rst setting
cpvarcol = 429 ,
# the color of significant window
significant_col = "pink" , significant_alpha = 0.2,
# boot strap
wantbootplot = TRUE,
fun = samplemean <- function(x, d){return(mean(x[d]))},
bootnum = 10,
bootintval = c(.025,.975),
bootalpha=0.3)+
ylim(-10,10)
# type = signal ; multi = FALSE ; No cpvar (similar to correlation or simple T test setting)
CZ <- data_select(data = ERPdata,frames = 1:426,
datacol = 2:427,subjcol = 430,chancol = 1,othvarcol = c(428:429,431:432),
select_chan=c("CZ"))
CZ_word <- subset(CZ,CZ$Condition=="word")
CZ_word_test <- erpfatest(CZ_word[2:427],
design=model.matrix(~IQ,data=CZ_word))
mcplot(tests_rst = CZ_word_test ,
type = "signal", # type = "test" or "signal"
multi = F,
cor = FALSE, # only if type = test
data = CZ_word , frames = 1:426 ,datacol = 2:427, subjcol = 430 , chancol = 1 , othvarcol = c(428:429,431:432) ,
# No cpvar (similar to correlation or simple T test setting)
cpvarcol = NULL ,
significant_col = "purple" , significant_alpha = 0.2,# only if type = signal
wantbootplot = TRUE,
fun = samplemean <- function(x, d){return(mean(x[d]))},
bootnum = 10,
bootintval = c(.025,.975),
bootalpha = 0.3 )+
ylim(-10,10)
# type = signal ; multi = TRUE ; Have cpvar
mcplot(tests_rst = ERP_test,
type = "signal", # type = "test" or "signal"
multi = T,
cor = FALSE, # only if type = test
data = ERPdata , frames = 1:426 ,
datacol = 2:427, subjcol = 430 , chancol = 1 ,
othvarcol = c(428:429,431:432) , cpvarcol = 429 ,
significant_col = "pink" , significant_alpha = 0.2,# only if type = signal
wantbootplot = TRUE,
fun = samplemean <- function(x, d){return(mean(x[d]))},
bootnum = 10,
bootintval = c(.025,.975),
bootalpha = 0.3 )+
ylim(-10,10)
# type = signal ; multi = TRUE ; No cpvar
ERP_cor_test <- chan_test(ERPdata,2:427,chancol=1,testtype="erpfatest",
design_model=(~IQ+Condition),
design0_model=(~Condition))
mcplot(tests_rst = ERP_cor_test,
type = "signal",
multi = T,
cor = FALSE,
data = ERPdata , frames = 1:426 ,
datacol = 2:427, subjcol = 430 , chancol = 1 ,
othvarcol = c(428:429,431:432) ,
cpvarcol = NULL ,
significant_col = "pink" , significant_alpha = 0.2,# only if type = signal
wantbootplot = TRUE,
fun = samplemean <- function(x, d){return(mean(x[d]))},
bootnum = 10,
bootintval = c(.025,.975),
bootalpha = 0.3 )
ERP only
1. scalpplot
2. topograph
1. scalpplot
revised from erpR package
plot the scalp :
BUT I think the bootplot is a better choice !!!!!
ERPdata_agg <- data_summarize(ERPdata,frames=1:426,2:427,430,1,othvarcol=c(428,429,431,432),
summarycol=c(428,429,1),
fun=mean)
scalpplot(ERPdata_agg,frames=1:426,erpcol=4:429,subcol=NULL,elecol=3,othvarcol = 1,
cpvarcol=2)
2. topograph
revised from erpR
but I have not yet really understand the meaning of their setting.
ERP_word <- subset(ERPdata_agg,ERPdata_agg$Condition=="word")
topograph(ERP_word[,-c(1:2)], # you should unput ONLY the erpdata with electrode
frames = 1:426,
chcol = 1,
startmsec=1, endmsec=426,
win.ini=1,win.end=426,
draw.nose=T,projection="equalarea")WARNING: your data (after interpolation) are out of range as compared to the zlims specified.
Your data range is: -8.25, 5.63
Your zlims are: -8, 8 