myfunc(d2.mopp.data)
library(gplots)
library(plotrix)
library(colorspace)
library(colorRamps)
library(RColorBrewer)
mav <- function(data,n){
stats::filter(data,rep(1/n,n), sides=2)
}
myfunc <- function(data){
data[is.na(data),]
adjustcolor("blanchedalmond",alpha.f = 0.3)
plot.ts((data$Time),data$EDR.BR,cex=1.5,
type="l",las=1,col="green", ylim = c(0,max(data$ECG.HR)), xlab = "Time", ylab = "", main="Patient's Health Record")
grid(NA, 5, lwd = 2)
lines(wapply(as.numeric(data$Time),data$EDR.BR, mean),col="red", lwd = 3)
rect(par("usr")[1],par("usr")[3],par("usr")[2],par("usr")[4],col = "grey")
points(mav(data$ECG.HR, n=5),col="blue",lty=1,lwd=1)
lines(wapply(as.numeric(data$Time),data$ECG.HR, mean),col="red", lwd = 3)
points(data$Temp!=0,col="orange",lty=1,lwd=1)
points(data$Belt.BR,col="pink",lty=1,lwd=1)
lines(wapply(as.numeric(data$Time),data$Belt.BR, mean),col="red", lwd = 3)
points(data$CoreTemp,col="yellow",lty=1,lwd=1)
lines(wapply(as.numeric(data$Time),data$CoreTemp, mean),col="red", lwd = 3)
legend("topleft",cex=0.8,inset = 0.01,ncol = 5, fill = c("green","blue","orange","pink","yellow"),
legend = c("EDR.BR","ECG.HR","Temp","Belt.BR","CoreTemp"))
grid()
}
myfunc(d2.mopp.data)
d2.mopp.data
data <- d2.mopp.data
library(gplots)
library(plotrix)
library(colorspace)
library(colorRamps)
library(RColorBrewer)
mav <- function(data,n){
stats::filter(data,rep(1/n,n), sides=2)
}
myfunc <- function(data){
#data[is.na(data),]
adjustcolor("blanchedalmond",alpha.f = 0.3)
plot.ts((data$Time),data$EDR.BR,cex=1.5,
type="l",las=1,col="green", ylim = c(0,max(data$ECG.HR)), xlab = "Time", ylab = "", main="Patient's Health Record")
grid(NA, 5, lwd = 2)
lines(wapply(as.numeric(data$Time),data$EDR.BR, mean),col="red", lwd = 3)
rect(par("usr")[1],par("usr")[3],par("usr")[2],par("usr")[4],col = "grey")
points(mav(data$ECG.HR, n=5),col="blue",lty=1,lwd=1)
lines(wapply(as.numeric(data$Time),data$ECG.HR, mean),col="red", lwd = 3)
points(data$Temp!=0,col="orange",lty=1,lwd=1)
points(data$Belt.BR,col="pink",lty=1,lwd=1)
lines(wapply(as.numeric(data$Time),data$Belt.BR, mean),col="red", lwd = 3)
points(data$CoreTemp,col="yellow",lty=1,lwd=1)
lines(wapply(as.numeric(data$Time),data$CoreTemp, mean),col="red", lwd = 3)
legend("topleft",cex=0.8,inset = 0.01,ncol = 5, fill = c("green","blue","orange","pink","yellow"),
legend = c("EDR.BR","ECG.HR","Temp","Belt.BR","CoreTemp"))
grid()
}
myfunc(d2.mopp.data)
knitr::opts_chunk$set(echo = TRUE)
set.seed(100)
x1 <- (runif(50)-.5)*5 + rnorm(50)
x2 <- x1 + runif(50)+rnorm(50)*.65
x3 <- sort(x1) + runif(50) + rnorm(50) * .66
x4 <- exp(sort(x1) + runif(50) + rnorm(50) * .66)
x5 <- (x1-3)*(x1-0)*(x1+2)+rnorm(50)*2
set.seed(200)
gender <- as.factor(sample(c("M","F"),50,replace=T))
tvshow <-c("Game of Thrones","Better Call Saul","Stranger Things","Empire")[ round(as.numeric(gender)+rnorm(50)*.7)+1]
hero <- sample(c("Spiderman","Thanos","Deadpool","The Moth"),50,replace=T)
data <- data.frame(x1,x2,x3,x4,x5,gender,tvshow,hero)
write.table(data,"exam2.csv",row.names=F)
data <- read.table("exam2.csv",header=T)
t.test(data$x1)
t.test(data$x2)
t.test(data$x1,data$x2)
t.test(data$x1,data$x2,paired=T)
t.test(x2~gender,data=data)
library(BayesFactor)
ttestBF(x1)
ttestBF(x2)
ttestBF(x1,x2)
ttestBF(x1,x2,paired=T)
ttestBF(x2[gender=="M"],x2[gender=="F"])
table(data$tvshow,data$gender)
table(data$tvshow,data$hero)
table(data$gender,data$hero)
chisq.test(data$gender,data$tvshow)
chisq.test(data$gender,data$hero)
contingencyTableBF(table(data$tvshow,data$gender),sampleType="indepMulti",fixedMargin="cols")
contingencyTableBF(table(data$tvshow,data$hero),sampleType="indepMulti",fixedMargin="cols")
contingencyTableBF(table(data$gender,data$hero),sampleType="indepMulti",fixedMargin="cols")
table(data$gender,data$hero)
pairs(data[,1:4])
cor.test(x1,x2,data=data)
cor.test(x1,x3,data=data)
cor.test(x1,x4,data=data)
cor.test(x1,x4,data=data)
cor.test(x1,x4,data=data,method="spearman")
lm(x1~x2+x3+x4 + gender)$coef
model <- lm(x1~x2+x3+x4 + gender)
summary(model)
small <- step(model)
summary(model)$r.squared
summary(small)$r.squared
small
library(rcompanion)
tmp <- transformTukey(x4)
par(mfrow=c(1,2))
plot(data$x3,data$x4,col="gold",pch=16,main="Raw x4")
points(data$x3,data$x4)
plot(data$x3,tmp,col="gold",pch=16,main="Transformed x4 (x^.05)")
points(data$x3,tmp)
m1 <- lm(x3~x4,data=data)
m2 <- lm(x3~tmp,data=data)
summary(m1)$r.squared
summary(m2)$r.squared
m1$residuals
hist(m1$residuals)
hist(x4)
hist(log(x4))
tmp <- transformTukey(x4)
tmp
tranformTukey(x4)
transformTukey(x4)
m1 <- lm(x3~x4,data=data)
m2 <- lm(x3~tmp,data=data)
summary(m1)$r.squared
summary(m2)$r.squared
tmp <- transformTukey(x4)
plot(x1,x5)
poly6 <- lm(x5~poly(x1,6))
poly5 <- lm(x5~poly(x1,5))
poly4 <- lm(x5~poly(x1,4))
poly3 <- lm(x5~poly(x1,3))
poly2 <- lm(x5~poly(x1,2))
poly1 <- lm(x5~poly(x1,1))
poly33 <- lm(x5~poly(x1,3),data=data)
anova(poly1,poly2,poly3,poly4,poly5,poly6)
step(poly6)
step(poly5)
step(poly4)
step(poly3)
step(poly2)
step(poly1)
plot(x1,x5)
poly6 <- lm(x5~poly(x1,6))
poly5 <- lm(x5~poly(x1,5))
poly4 <- lm(x5~poly(x1,4))
poly3 <- lm(x5~poly(x1,3))
poly2 <- lm(x5~poly(x1,2))
poly1 <- lm(x5~poly(x1,1))
poly33 <- lm(x5~poly(x1,3),data=data)
anova(poly1,poly2,poly3,poly4,poly5,poly6)
step(poly6)
step(poly5)
step(poly4)
step(poly3)
step(poly2)
step(poly1)
step(poly6)
anova(poly1,poly2,poly3,poly4,poly5,poly6)
predict(poly3,list(x1=c(-5,-1,2,4)))
knitr::opts_chunk$set(echo = TRUE)
t.test(data$x1)
knitr::opts_chunk$set(echo = TRUE)
set.seed(100)
x1 <- (runif(50)-.5)*5 + rnorm(50)
x2 <- x1 + runif(50)+rnorm(50)*.65
x3 <- sort(x1) + runif(50) + rnorm(50) * .66
x4 <- exp(sort(x1) + runif(50) + rnorm(50) * .66)
x5 <- (x1-3)*(x1-0)*(x1+2)+rnorm(50)*2
set.seed(200)
gender <- as.factor(sample(c("M","F"),50,replace=T))
tvshow <-c("House of the Dragon","Better Call Saul","Stranger Things","Rings of Power")[ round(as.numeric(gender)+rnorm(50)*.7)+1]
hero <- sample(c("Spiderman","Thanos","Deadpool","The Moth"),50,replace=T)
data <- data.frame(x1,x2,x3,x4,x5,gender,tvshow,hero)
write.table(data,"exam2.csv",sep=",",row.names=F)
data <- read.csv("exam2.csv",header=T)
t.test(data$x1)
t.test(data$x2)
t.test(data$x1,data$x2)
t.test(data$x1,data$x2,paired=T)
t.test(x2~gender,data=data)
library(BayesFactor)
ttestBF(x1)
ttestBF(x2)
ttestBF(x1,x2)
ttestBF(x1,x2,paired=T)
ttestBF(x2[gender=="M"],x2[gender=="F"])
table(data$tvshow,data$gender)
table(data$tvshow,data$hero)
table(data$gender,data$hero)
chisq.test(data$gender,data$tvshow)
chisq.test(data$gender,data$hero)
contingencyTableBF(table(data$tvshow,data$gender),sampleType="indepMulti",fixedMargin="cols")
contingencyTableBF(table(data$tvshow,data$hero),sampleType="indepMulti",fixedMargin="cols")
contingencyTableBF(table(data$gender,data$hero),sampleType="indepMulti",fixedMargin="cols")
pairs(data[,1:4])
cor.test(x1,x2,data=data)
cor.test(x1,x3,data=data)
cor.test(x1,x4,data=data)
cor.test(x1,x4,data=data,method="spearman")
cc <- lm(x1~x2+x3+x4 + gender)$coef
print(cc)
print(cc[5])
model <- lm(x1~x2+x3+x4 + gender)
summary(model)
small <- step(model)
summary(model)$r.squared
summary(small)$r.squared
library(rcompanion)
tmp <- transformTukey(x4)
par(mfrow=c(1,2))
plot(data$x3,data$x4,col="gold",pch=16,main="Raw x4")
points(data$x3,data$x4)
plot(data$x3,tmp,col="gold",pch=16,main="Transformed x4 (x^.05)")
points(data$x3,tmp)
m1 <- lm(x3~x4,data=data)
m2 <- lm(x3~tmp,data=data)
summary(m1)$r.squared
summary(m2)$r.squared
##let's re-sort to make x1 in order
ord <- order(x1)
plot(x1,x5)
poly6 <- lm(x5~poly(x1,6))
poly5 <- lm(x5~poly(x1,5))
poly4 <- lm(x5~poly(x1,4))
poly3 <- lm(x5~poly(x1,3))
poly2 <- lm(x5~poly(x1,2))
poly1 <- lm(x5~poly(x1,1))
poly33 <- lm(x5~poly(x1,3),data=data)
anova(poly1,poly2,poly3,poly4,poly5,poly6)
step(poly6)
step(poly5)
step(poly4)
step(poly3)
step(poly2)
step(poly1)
plot(x1,x5)
points(x1[ord],poly1$fit[ord],type="l")
points(x1[ord],poly2$fit[ord],type="l")
points(x1[ord],poly3$fit[ord],type="l")
points(x1[ord],poly4$fit[ord],type="l")
points(x1[ord],poly5$fit[ord],type="l")
points(x1[ord],poly6$fit[ord],type="l")
library(splines)
ns6 <- lm(x5~ns(x1,6))
ns5 <- lm(x5~ns(x1,5))
ns4 <- lm(x5~ns(x1,4))
ns3 <- lm(x5~ns(x1,3))
ns2 <- lm(x5~ns(x1,2))
ns1 <- lm(x5~ns(x1,1))
ns10 <- lm(x5~ns(x1,10))
ns11 <- lm(x5~ns(x1,11))
anova(ns1,ns2,ns3,ns4,ns5,ns6,ns10,ns11)
plot(x1,x5,main="Spline fit models")
points(x1[ord],ns1$fit[ord],type="l")
points(x1[ord],ns2$fit[ord],type="l")
points(x1[ord],ns3$fit[ord],type="l")
points(x1[ord],ns4$fit[ord],type="l")
points(x1[ord],ns5$fit[ord],type="l")
points(x1[ord],ns6$fit[ord],type="l")
points(x1[ord],ns10$fit[ord],type="l")
points(x1[ord],ns11$fit[ord],type="l")
predict(poly3,list(x1=c(-5,-1,2,4)))
cc <- lm(x1~x2+x3+x4 + gender)$coef
print(cc)
print(cc[5])
summary(cc[5])
cc <- lm(x1~x2+x3+x4 + gender)$coef
print(cc)
summary(cc[5])
cc <- lm(x1~x2+x3+x4 + gender)$coef
print(cc)
summary(cc)
cc
cc <- lm(x1~x2+x3+x4 + gender)
print(cc)
summary(cc)
small <- step(model)
summary(model)$r.squared
summary(small)$r.squared
library(rcompanion)
tmp <- transformTukey(x4)
par(mfrow=c(1,2))
plot(data$x3,data$x4,col="gold",pch=16,main="Raw x4")
points(data$x3,data$x4)
plot(data$x3,tmp,col="gold",pch=16,main="Transformed x4 (x^.05)")
points(data$x3,tmp)
m1 <- lm(x3~x4,data=data)
m2 <- lm(x3~tmp,data=data)
summary(m1)$r.squared
summary(m2)$r.squared
tmp
plot(data$x3,tmp,col="gold",pch=16,main="Transformed x4 (x^.05)")
points(data$x3,tmp)
m1 <- lm(x3~x4,data=data)
m2 <- lm(x3~tmp,data=data)
summary(m1)$r.squared
summary(m2)$r.squared
##let's re-sort to make x1 in order
ord <- order(x1)
plot(x1,x5)
poly6 <- lm(x5~poly(x1,6))
poly5 <- lm(x5~poly(x1,5))
poly4 <- lm(x5~poly(x1,4))
poly3 <- lm(x5~poly(x1,3))
poly2 <- lm(x5~poly(x1,2))
poly1 <- lm(x5~poly(x1,1))
poly33 <- lm(x5~poly(x1,3),data=data)
anova(poly1,poly2,poly3,poly4,poly5,poly6)
step(poly6)
step(poly5)
step(poly4)
step(poly3)
step(poly2)
step(poly1)
plot(x1,x5)
points(x1[ord],poly1$fit[ord],type="l")
points(x1[ord],poly2$fit[ord],type="l")
points(x1[ord],poly3$fit[ord],type="l")
points(x1[ord],poly4$fit[ord],type="l")
points(x1[ord],poly5$fit[ord],type="l")
points(x1[ord],poly6$fit[ord],type="l")
step(poly6)
step(poly5)
step(poly3)
step(poly2)
step(poly1)
step(poly3)
plot(x1,x5)
points(x1[ord],poly1$fit[ord],type="l")
points(x1[ord],poly2$fit[ord],type="l")
points(x1[ord],poly3$fit[ord],type="l")
points(x1[ord],poly4$fit[ord],type="l")
points(x1[ord],poly5$fit[ord],type="l")
points(x1[ord],poly6$fit[ord],type="l")
library(splines)
ns6 <- lm(x5~ns(x1,6))
ns5 <- lm(x5~ns(x1,5))
ns4 <- lm(x5~ns(x1,4))
ns3 <- lm(x5~ns(x1,3))
ns2 <- lm(x5~ns(x1,2))
ns1 <- lm(x5~ns(x1,1))
ns10 <- lm(x5~ns(x1,10))
ns11 <- lm(x5~ns(x1,11))
anova(ns1,ns2,ns3,ns4,ns5,ns6,ns10,ns11)
plot(x1,x5,main="Spline fit models")
points(x1[ord],ns1$fit[ord],type="l")
points(x1[ord],ns2$fit[ord],type="l")
points(x1[ord],ns3$fit[ord],type="l")
points(x1[ord],ns4$fit[ord],type="l")
points(x1[ord],ns5$fit[ord],type="l")
points(x1[ord],ns6$fit[ord],type="l")
points(x1[ord],ns10$fit[ord],type="l")
points(x1[ord],ns11$fit[ord],type="l")
install.packages("prepdat")
library(prepdat)
dat <- read.csv("tmt.csv")
dat
par(mfrow=c(1,2))
plot(dat$time~(dat$age)+dat$type)
dat <- read.csv("tmt.csv")
setwd("~/Dropbox/courses/5210-2024/web-5210/psy5210/Projects/Chapter14")
dat <- read.csv("tmt.csv")
dat$age <- as.factor(dat$age)
dat
dat$time
hist(dat$time)
hist(time~age,data=dat)
boxplot(time~age,data=dat)
boxplot(log(time)~age,data=dat)
source("~/Dropbox/courses/5210-2024/web-5210/psy5210/Projects/Chapter14/Chapter14Walkthrough.R", echo=TRUE)
lm(time~age,data=dat)
summary(lm(time~age,data=dat))
?prepdat
library(prepdat)
library(prepdat)
?prepdat
library(prepdat)
non_recursive_mc(dat$time)
out1 <- non_recursive_mc(dat$time)
out1 <- non_recursive_mc(dat$time)
out2 <- hybrid_recursive_mc(dat$time)
out3 <- hybrid_recursive_mc(dat$time)
out1
out2
out3
dat[1:5,]
table(dat$type)
agg2 <- aggregate(dat$time,list(dat$age),non_recursive_mc)
agg2
agg1 <- aggregate(dat$time,list(dat$age),mean)
agg2 <- aggregate(dat$time,list(dat$age),non_recursive_mc)
agg3 <- aggregate(dat$time,list(dat$age),recursive_mc)
agg1 <- aggregate(dat$time,list(dat$age),mean)
agg2 <- aggregate(dat$time,list(dat$age),non_recursive_mc)
agg3 <- aggregate(dat$time,list(dat$age),modified_recursive_mc)
agg4 <- aggregate(dat$time,list(dat$age),hybrid_recursive_mc)
agg1
agg2
grouped <- bind_rows(agg1,agg2[,1:2],agg3[,1:2],agg4[,1:2])
library(tidyverse)
grouped <- bind_rows(agg1,agg2[,1:2],agg3[,1:2],agg4[,1:2])
group$version <- rep(t("Mean","Non-recursive","Modified Recursive","Hybrid"),each=3)
grouped <- bind_rows(agg1,agg2[,1:2],agg3[,1:2],agg4[,1:2])
agg1
agg2
grouped <- rbind(agg1,agg2[,1:2],agg3[,1:2],agg4[,1:2])
grouped
group$version <- rep(t("Mean","Non-recursive","Modified Recursive","Hybrid"),each=3)
group$version <- rep(c("Mean","Non-recursive","Modified Recursive","Hybrid"),each=3)
grouped$version <- rep(c("Mean","Non-recursive","Modified Recursive","Hybrid"),each=3)
grouped
grouped$version <- rep(c("Mean","Non-recursive","Modified Recursive","Hybrid"),each=3)
grouped <- rbind(agg1,agg2[,1:2],agg3[,1:2],agg4[,1:2])
grouped$version <- rep(c("Mean","Non-recursive","Modified Recursive","Hybrid"),each=4)
library(tidyverse)
grouped <- rbind(agg1,agg2[,1:2],agg3[,1:2],agg4[,1:2])
grouped$Method <- rep(c("Mean","Non-recursive","Modified Recursive","Hybrid"),each=4)
colnames(grouped) <- c("Age","RT","Method")
grouped |> ggplot(aes(x=Age,group=Method,color=Method)) + geom_point() + geom_line()
grouped |> ggplot(aes(x=Age,y=RT,group=Method,color=Method)) + geom_point() + geom_line()
grouped <- rbind(agg1,agg2[,1:2],agg3[,1:2],agg4[,1:2])
grouped$Method <- factor(rep(c("Mean","Non-recursive","Modified Recursive","Hybrid"),each=4),levels=c("Mean","Non-recursive","Modified Recursive","Hybrid")
colnames(grouped) <- c("Age","RT","Method")
grouped <- rbind(agg1,agg2[,1:2],agg3[,1:2],agg4[,1:2])
grouped$Method <- factor(rep(c("Mean","Non-recursive","Modified Recursive","Hybrid"),each=4),levels=c("Mean","Non-recursive","Modified Recursive","Hybrid"))
colnames(grouped) <- c("Age","RT","Method")
grouped |> ggplot(aes(x=Age,y=RT,group=Method,color=Method)) + geom_point() + geom_line() +
theme_bw()
grouped <- rbind(agg1,agg2[,1:2],agg3[,1:2],agg4[,1:2])
grouped$Method <- factor(rep(c("Mean","Non-recursive","Modified Recursive","Hybrid"),each=4),levels=c("Mean","Non-recursive","Modified Recursive","Hybrid"))
colnames(grouped) <- c("Age","RT","Method")
grouped |> ggplot(aes(x=Age,y=RT,group=Method,color=Method)) + geom_point() + geom_line() +
theme_bw()  + ylim(0,200)
library(trimr)
install.packages("trimr")
library(trimr)
library(trimr)
?trimr
data
dat
absoluteRT(data=dat,minRT=100,maxRT=500,pptVar="age",condVar="type",rtVar="time")
data(exampleData)
# perform the trimming, returning mean RT
trimmedData <- absoluteRT(data = exampleData, minRT = 150, maxRT = 2500,
returnType = "mean")
trimmedData
data$accuracy <- 1
trimmedData <- absoluteRT(data = data, minRT = 150, maxRT = 2500,
returnType = "mean")
data[1:5,]
dat$accuracy <- 1
trimmedData <- absoluteRT(data = dat, minRT = 150, maxRT = 2500,
returnType = "mean")
dat[1:5,]
dat$accuracy <- 1
trimmedData <- absoluteRT(data = dat, minRT = 150, maxRT = 2500, rttVar="time"
returnType = "mean")
dat$accuracy <- 1
trimmedData <- absoluteRT(data = dat, minRT = 150, maxRT = 2500, rttVar="time",
returnType = "mean")
dat$accuracy <- 1
trimmedData <- absoluteRT(data = dat, minRT = 150, maxRT = 2500, rtVar="time",
returnType = "mean")
trimmedData <- absoluteRT(data = dat, minRT = 150, maxRT = 2500, rtVar="time",accVar="accuracy",
pptVar="age",condVar="type",
returnType = "mean")
trimmedData
trimmedData <- absoluteRT(data = dat, minRT = 150, maxRT = 500, rtVar="time",accVar="accuracy",
pptVar="age",condVar="type",
returnType = "mean")
trimmedData
dat$accuracy <- 1
trimmedData2 <- sdTrim(data = dat, minRT = 150, sd=2.5, rtVar="time",accVar="accuracy",
pptVar="age",condVar="type",
returnType = "mean")
trimmedData2
grouped
aggregate(RT~Age+Method,FUN=mean,data=grouped)
aggregate(RT~Age+Method,FUN=mean,data=grouped)
tapply(RT~Age+Method,FUN=mean,data=grouped)
tapply(grouped$RT~grouped[,1:2],mean)
tapply(grouped$RT, grouped[,1:2],mean)
tapply(grouped$RT, grouped[,c(1,3)],mean)
round(tapply(grouped$RT, grouped[,c(1,3)],mean),0)
hybridRecursive <- hybridRecursive(data = exampleData, minRT = 150, maxRT = 2500,
returnType = "mean")
hybridRecursive(data = exampleData, minRT = 150, maxRT = 2500,
returnType = "mean")
tapply(grouped$RT, grouped[,c(1,3)],mean)
hybridRecursive(data = exampleData, minRT = 150, maxRT = 2500,
returnType = "mean")
hybridRecursive(data = exampleData, returnType = "mean")
hybridRecursive(data = dat,rtVar="time",pptVar="age")
hybridRecursive(data = dat,rtVar="time",pptVar="age",condVar="type")
hybridRecursive(data = dat,rtVar="time",pptVar="age",condVar="type",minRT=150)
hybridRecursive(data = dat,rtVar="time",pptVar="age",condVar="type",minRT=100)
trimmedData <- absoluteRT(data = exampleData, minRT = 100, maxRT = 2500,
returnType = "mean")
trimmedData
sdTrim(data = dat, minRT = 150, sd=2.5, rtVar="time",accVar="accuracy",
pptVar="age",condVar="type",
returnType = "mean")
sdTrim(data = dat, minRT = 150, sd=2.5, rtVar="time",accVar="accuracy",
pptVar="age",condVar="type",
returnType = "mean")
hybridRecursive(data = dat,rtVar="time",pptVar="age",condVar="type",minRT=100)
absoluteRT(data = dat, minRT = 150, maxRT = 500, rtVar="time",accVar="accuracy",
pptVar="age",condVar="type",returnType = "mean")