critroi <- read.csv("Critregions/criticalregions.csv",header=F,as.is=T)
colnames(critroi) <- c("item","cond","position")
spillover <- critroi
spillover$pos<- spillover$pos+1
critroi$crit <- "crit"
## 110 subjects' data:
data <- read.table("data110.txt",as.is=T)
colnames(data) <- c("subj","expt","item","condition","pos","word","response","rt")
data <- subset(data,expt=="Farsi")
data.new<-subset(data,item!=7 & item!=12& item!=14 & item!=15 & item!=17 & item!=20 & item!=22 & item!=23 & item!=24)
length(unique(data.new$item))
data<-data.new
## table(data$rt<250)
#202 /(29607+202)*100=0.67765
## isolate question responses:
questions <- subset(data,pos=="?")
questions$response <- as.numeric(as.character(questions$response))
summary(questions)
round(with(questions,tapply(response,condition,mean))*100)
meansubjects <- with(questions,tapply(response,subj,mean))
#barplot(meansubjects)
## subj 67 has very low performance
#meansubjects
subjacc <- data.frame(subj=1:110,acc=meansubjects)
with(questions,tapply(-1000/rt,condition,mean))
## coding
pron <- ifelse(questions$condition%in%c("a","c"),-.5,.5)
rc <- ifelse(questions$condition%in%c("a","b"),-.5,.5)
inter <- ifelse(questions$condition%in%c("b","c"),-.5,.5)
questions$pron <- pron
questions$rc <- rc
questions$inter <- inter
library(lme4)
library(car)
questions<-subset(questions,subj!=67)
head(data)
head(questions)
(fm0.logistic <- lmer(response~pron+rc+inter+(1|subj)+(1|item),
family=binomial(),questions))
exp(1.8342)/(1+exp(1.8342))
q()
