mickeypash

x <- data.frame(SubjID=1:20
				Student=factor(rep(c("Freshmen","Sophomores"
					"Junior","Seniors"),each=5)),
				Y=c(32,30,28,30,25,27,27,30,30,21,27,22,23,23,25,22,20,22,23,13))

mickeypash

# OS generated files 
######################
.DS_Store
.DS_Store?
._*
.Spotlight-V100
.Trashes
Icon?
ehthumbs.db
Thumbs.db

mickeypash

library(XML)

atime <- read.csv("audiotimings.csv")
atime$ItemID <- as.numeric(substr(atime$Wavfile, 2, 3))
options(stringsAsFactors=FALSE)

getfile <- function(x, ext=NULL) {
  return(paste(paste(mypath, x$base, sep="/"), ext, "txt", sep="."))
}

mickeypash

clippedaod <- data.frame(yr=c(2000,2001,2002,2003,2004,2005))

tst <- c()

for(i in 2000:2013){
  tst[i] <- (clippedaod$yr == i)
      print(i)
} 

mickeypash

load(file="preprocessed.RData")
load(file="alldat.RData")

colnames(alldat) <- sub("\\.x$", "", colnames(alldat))
rownames(alldat) <- NULL

mx <- as.matrix(xtabs(~AOI+Msec, alldat))
props <- mx / apply(mx, 2, sum)

# Use ggplot to make it cool?

mickeypash

## T-test

library(plyr)
load(file="preprocessed.RData")

# Read data
trials <- tinfo4
responses <- dat4

# Change column names

mickeypash

> shapiro.test(prop.mean$PComp)

        Shapiro-Wilk normality test

data:  prop.mean$PComp
W = 0.9662, p-value = 0.528

> shapiro.test(prop.mean$SComp)

        Shapiro-Wilk normality test

mickeypash

############ Shapiro test #################
########### normality #####################

> shapiro.test(prop.mean$PComp)

        Shapiro-Wilk normality test

data:  prop.mean$PComp
W = 0.9662, p-value = 0.528

mickeypash

b.means <-c (0.2356355, 0.2294562, 0.2197686)

barplot(b.means,ylim=c(0,1), main="Mean proportion for competitors", 
        names.arg= c("Phonological", "Semantic", "Distractor"), col = c("gray1", "gray45", "gray90"))

mickeypash

library(ggplot2)
gg.means <- data.frame(PComp=mean(prop.mean$PComp), SComp=mean(prop.mean$SComp), Unrl=mean(prop.mean$Unrl))
ggplot(melt(gg.means,id.var=c()), aes(x=,y=Value)) + geom_bar()