plot(seconds~words.,data=it)
colnames(it)
colnames(it)[1]='excerpt'
colnames(it)[4]
colnames(it)[4]='words'
colnames(it)[5]
colnames(it)[5]='genre'
colnames(it)
options(width=60)
colnames(it)
write.table(it,file='~/meetings_workshops/Rmodeling/data/NatDataCorr.csv',sep='\t',quote=FALSE,row.names=FALSE)
fitit=lm(PctCorrect~genre+seconds,data=it)
coef(it)
coef(fitit)
data.frame(coef(fitit))
unique(it$genre)
data.frame(coef(fitit))
ls(fitit)
fitit=lm(PctCorrect~words+seconds,data=it)
data.frame(coef(fitit))
summary(lm(seconds~words.,data=it))
summary(fitit)
head(it)
success=data.frame(right=it$words,wrong=round(it$words(1-it$PctCorrect),0)
)
success=data.frame(right=it$words,wrong=round(it$words*(1-it$PctCorrect),0)
)
success=data.frame(right=it$words,wrong=round(it$words*(1-it$PctCorrect),0))
dim(success)
head(success)
head(it)
fitit=glm(success~it$seconds,family=poisson(link='identity'))
success=as.matrix(data.frame(right=it$words,wrong=round(it$words*(1-it$PctCorrect),0)))
fitit=glm(success~it$seconds,family=poisson(link='identity'))
dim(success)
dim(it)
head(success)
good=!is.na(success$wrong)
good=!is.na(success[,2])
fitit=glm(success[good,]~it$seconds[good],family=poisson(link='identity'))
table(good)
head(it)
summary(it)
it=subset(it,PctCorrect>=0)
dim(it)
write.table(it,file='~/meetings_workshops/Rmodeling/data/NatDataCorr.csv',sep='\t',quote=FALSE,row.names=FALSE)
success=as.matrix(data.frame(right=it$words,wrong=round(it$words*(1-it$PctCorrect),0)))
dim(success)
table(success[,1],success[,2])
options(width=80)
table(success[,1],success[,2])
fitit=glm(success[good,]~it$seconds[good],family=poisson(link='identity'))
fitit=glm(success~it$seconds,family=poisson(link='identity'))
fitit=glm(success~it$seconds,family=binomial(link='logit'))
summary(fitit)
fitit=glm(success~it$seconds+it$genre,family=binomial(link='logit'))
summary(fitit)
?predict.glm
emptydb=data.frame(seconds=1,genre='Rap')
predict.glm(fitit,newdata=emptydb)
summary(fitit)
emptydb
?predict.glm
predict.glm(fitit,newdata=emptydb,type='response')
?predict.glm
predict.lm(fitit,newdata=emptydb,type='response')
?predict.glm
predict(fitit,newdata=emptydb,type='response')
fitit=glm(success~it$seconds+it$genre,family=binomial(link='logit'))

emptydb
predict.glm(fitit,newdata=emptydb,type='response')
?predict.glm
pred=predict.glm(fitit,type='response')
str(pred)
pred=cbind(it,predict.glm(fitit,type='response'))
head(pred)
pred=cbind(it,pred=predict.glm(fitit,type='response'))
head(pred)
plot(pred~seconds,data=pred)
plot(pred~seconds+genre,data=pred)
plot(pred~seconds,data=pred)
allgen=unique(it$genre)
ord=order(it$seconds)
for(i in 1:length(allgen)) lines(pred~seconds,data=subset(it,genre==allgen[i])[ord,])
for(i in 1:length(allgen)) lines(pred~seconds,data=subset(pred,genre==allgen[i])[ord,])
for(i in 1:length(allgen)) lines(pred~seconds,data=subset(pred,genre==allgen[i])[ord,],col=i)
?legend
legend(x='top',legend=allgen,col=1:length(allgen))
legend(x='top',legend=allgen,col=1:length(allgen),lty='solid')
legend(x='top',legend=allgen,col=1:length(allgen),lty='solid',rows=1)
?legend
legend(x='top',legend=allgen,col=1:length(allgen),lty='solid',ncol=length(allgen))
legend(x=6,y=1,legend=allgen,col=1:length(allgen),lty='solid',ncol=length(allgen))
plot(pred~seconds,data=pred,xlim=c(0,35))
for(i in 1:length(allgen)) lines(pred~seconds,data=subset(pred,genre==allgen[i])[ord,],col=i)
legend(x='right',legend=allgen,col=1:length(allgen),lty='solid')
emptydb=data.frame(seconds=1:15,genre=rep('Rap',15))
allgen=unique(it$genre)
predict.glm(fitit,newdata=emptydb,type='response')
str(fitit)
ls(fitit)
fitit$family
predict(fitit,newdata=emptydb,type='response')
predict(fitit,emptydb,type='response')
emptydb
ls(fitit)
str(fitit$data)
ls(fitit$data)
names(fitit$data)
names(fitit@data)
str(fitit$data)
str(fitit)
str(fitit$model)
emptydb
str(emptydb)
emptydb=data.frame(seconds=1:15,genre=I(rep('Rap',15)))
str(emptydb)
predict.glm(fitit,newdata=emptydb,type='response')
head(it)
it=read.delim(NatIntelligibilityFile)
NatIntelligibilityFile='~/meetings_workshops/Rmodeling/data/NatDataCorr.csv'
it=read.delim(NatIntelligibilityFile)
head(it)
names(fit)
it=toSingleColumn(infile=OrigIntelligibilityFile,basecol=1:6,responsecol=7:36,resultname='PctCorrect')
OrigIntelligibilityFile='~/meetings_workshops/Rmodeling/data/NatDataOrig.csv'
it=toSingleColumn(infile=OrigIntelligibilityFile,basecol=1:6,responsecol=7:36,resultname='PctCorrect')
dim(it)
head(it)
dim(it)
table(is.na(it$Pct))
str(it)
table(it$Pct)
head(it)
table(it$GEN)
it=toSingleColumn(infile=OrigIntelligibilityFile,basecol=1:6,responsecol=7:36,resultname='PctCorrect')
dim(it)
table(it$GEN)
it=toSingleColumn(infile=OrigIntelligibilityFile,basecol=1:6,responsecol=7:36,resultname='PctCorrect')
dim(it)
table(it$GEN)
str(it)
table(it$subject)
table(is.na(it$Pct))
it=subset(it,PctCorrect>=0)
dim(it)
write.table(it,file='~/meetings_workshops/Rmodeling/data/NatDataCorr.csv',sep='\t',quote=FALSE,row.names=FALSE)
it=toSingleColumn(infile=OrigIntelligibilityFile,basecol=1:6,responsecol=7:36,resultname='PctCorrect')
head(it)
it=toSingleColumn(infile=OrigIntelligibilityFile,basecol=1:6,responsecol=7:36,resultname='PctCorrect')
head(it)
subset(it,genre='Classical')
subset(it,genre=='Classical')
subset(it,genre=='Classical')[,1:4]
subset(it,genre=='Classical')[,1:3]
subset(it,genre=='Classical')[,1:3]
subset(it,genre=='Classical')[,1:3]
head(subset(it,genre=='Classical')[,1:3])
head(subset(it,genre=='Classical')[,c(1,3,4,5)])
(subset(it,genre=='Classical')[,c(1,3,4,5)])
(subset(it,genre=='Classical')[,-2])
(subset(it,genre=='Classical')[,2])
it=subset(it,PctCorrect>=0)
write.table(it,file='~/meetings_workshops/Rmodeling/data/NatDataCorr.csv',sep='\t',quote=FALSE,row.names=FALSE)
plot(fit)
fit2=lm(Preference~Meter+Experience,data=x)
head(study3)
fit2=lm(Authority~Gender+Age,data=study3)
summary(fit)$coef
summary(fit2)$coef
fit2=lm(Authority~Gender+Age,data=study3)
summary(fit2)$coef
plot(Authority~Age,data=study3)
graphics.off()
plot(Authority~Age,data=study3)
fit=lm(Authority~Age,data=study3)
abline(fit)
summary(fit)$coef
abline(1.25,.055,col='red')
summary(fit2)$coef
abline(1.07,.0516,col='pink')
abline(1.61,.0516,col='blue')
predict.lm(fit2,newdata=data.frame(Gender=c('M','F'),Age=c(20,20))
)
predict.lm(fit2,newdata=data.frame(Gender=c('M','F'),Age=c(20,20)))
predict.lm(fit2,newdata=data.frame(Gender=c('M','F'),Age=c(40,40)))
dev.open()
x11()
plot(Authority~Age,data=study3)
abline(fit)
dev.set(2)
plot(Authority~Age,data=study3)
abline(1.61,.0516,col='blue')
abline(1.07,.0516,col='pink')
par(mfcol=c(2,1))
plot(Authority~Age,data=study3)
plot(Authority~Age,data=study3)
plot(Authority~Age,data=study3)
abline(fit)
plot(Authority~Age,data=study3)
abline(1.07,.0516,col='pink')
abline(1.61,.0516,col='blue')
fitM=lm(Authority~Age,data=subset(study3,Gender=='M')
)
fitM=lm(Authority~Age,data=subset(study3,Gender=='M'))
plot(Authority~Age,data=study3)
abline(1.61,.0516,col='blue')
abline(1.07,.0516,col='pink')
x11()
dev.set(2)
plot(Authority~Age,data=study3)
abline(fit)
fitM=lm(Authority~Age,data=subset(study3,Gender=='M'))
fitF=lm(Authority~Age,data=subset(study3,Gender=='F'))
x<-3
x
abline(fitM,col='blue',lty='dashed')
abline(fitF,col='red',lty='dashed')
dev.set(3)
abline(fitM,col='blue',lty='dashed')
abline(fitF,col='red',lty='dashed')
summary(fitM)$coef
summary(fitF)$coef)
summary(fitF)$coef
summary(fitM)$coef
points(Authority~Age,data=subset(study3,Gender=='M'),col='red',pch=16,cex=.8)
points(Authority~Age,data=subset(study3,Gender=='M'),col='blue',pch=1,cex=1.3)
points(Authority~Age,data=subset(study3,Gender=='F'),col='red',pch=16,cex=.8)
it=read.delim(NatIntelligibilityFile)
NatIntelligibilityFile='~/meetings_workshops/Rmodeling/data/NatDataCorr.csv'
it=read.delim(NatIntelligibilityFile)
head(it)
str(it)
table(it$subject)
NatIntelligibilityFile='~/meetings_workshops/Rmodeling/data/NatDataCorr.csv'
it=read.delim(NatIntelligibilityFile)
table(it$subject)
table(it$genre)
table(it$genre,it$subject)
dim(it)
?read.table
head(it)
it$genre[1]
it$genre[1]=='Avant-garde'
it$genre[1]==1
str(it)
dim(it)
it=read.delim('~/Desktop/NatDataCorr.csv')
dim(it)
tail(it)
it=read.delim('~/Desktop/NatDataCorr.csv')
dim(it)
table(it$subject)
str(it)
it=toSingleColumn(infile=OrigIntelligibilityFile,basecol=1:6,responsecol=7:36,resultname='PctCorrect')
dim(it)
it=subset(it,PctCorrect>=0)
head(it)
colnames(it)
dim(it)
table(it$subject)
itcorr=read.delim('~/Desktop/NatDataCorr.csv')
dim(itcorr)
missing=which(!it$excerpt %in% itcorr$excerpt)
table(missing)
it[197,]
it[197,-2]
length(unique(it$excerpt))
length(unique(itcorr$excerpt))
plot(it$Pct[1:top],itcorr$Pct[1:top])
top=50
plot(it$Pct[1:top],itcorr$Pct[1:top])
top=500
plot(it$Pct[1:top],itcorr$Pct[1:top])
top=100
plot(it$Pct[1:top],itcorr$Pct[1:top])
top=300
plot(it$Pct[1:top],itcorr$Pct[1:top])
top=200
plot(it$Pct[1:top],itcorr$Pct[1:top])
top=180
plot(it$Pct[1:top],itcorr$Pct[1:top])
top=150
plot(it$Pct[1:top],itcorr$Pct[1:top])
top=165
plot(it$Pct[1:top],itcorr$Pct[1:top])
top=155
plot(it$Pct[1:top],itcorr$Pct[1:top])
it[150:160,-2]
itcorr[150:160,-2]
write.table(it,file='~/meetings_workshops/Rmodeling/data/NatDataCorr.csv',sep='\t',quote=FALSE,row.names=FALSE)
itagain=read.delim(NatIntelligibilityFile)
dim(itagain)
dim(it)
write.table(it[,-2],file='~/meetings_workshops/Rmodeling/data/NatDataCorr.csv',sep='\t',quote=FALSE,row.names=FALSE)
itagain=read.delim(NatIntelligibilityFile)
dim(it)
table(it$LYR)
length(table(it$LYR))
itagain=read.delim(NatIntelligibilityFile,quote="'")
dim(itagain)
?read.delim
grep("'",itagain$LYR)
grep("'",itagain$LYR,fixed=TRUE)
itagain=read.delim(NatIntelligibilityFile,quote="")
dim(itagain)
head(itagain)
itagain=read.delim(NatIntelligibilityFile)
head(itagain)
dim(itagain)
it=toSingleColumn(infile=OrigIntelligibilityFile,basecol=1:6,responsecol=7:36,resultname='PctCorrect')
head(it)
dim(it)
it=subset(it,PctCorrect>=0)
dim(it)
grep("'",it$LYR,fixed=TRUE)
grep("'",it$LYR)
length(grep("'",it$LYR))
length(grep('"',it$LYR))
subset(it,logical.grep('"',it$LYR))
subset(it,logical.grep('"',it$LYR))[2]
subset(it,logical.grep("'",it$LYR))[2]
unique(subset(it,logical.grep("'",it$LYR))[2])
length(unique(subset(it,logical.grep("'",it$LYR))[2]))
length(unique(subset(it,logical.grep("'",it$LYR))[,2]))
(unique(subset(it,logical.grep("'",it$LYR))[,2]))
dim(it)
mod=lmer(PctCorrect~seconds+genre+(seconds+genre|subject),verbose=TRUE)
library(lme4)
mod=lmer(PctCorrect~seconds+genre+(seconds+genre|subject),verbose=TRUE)
mod=lmer(PctCorrect~seconds+genre+(seconds+genre|subject),verbose=TRUE,data=it)
head(x)
dim(x)
x=read.delim(study3457file)
dim(x)
head(x)
table(x$Part)
mod=lmer(Preference~Meter+(Meter|Participant),data=x,verbose=TRUE)
display(mod)
library(arm)
display(mod)
coef(mod)
table(it$genre)
modit1=lmer(PctCorrect~genre+(genre|subject),verbose=TRUE,data=it)
modit1=lmer(PctCorrect~seconds+(seconds|genre),verbose=TRUE,data=it)
display(modit)
library(arm)
display(modit)
display(modit1)
coef(modit1)
display(modit1,digits=5)
mod=lmer(Preference~Meter+(Meter|Participant),data=x,verbose=TRUE)
mod=lmer(Preference~Meter+(Meter|Participant),data=x)
coef(mod)
fixef(mod)
modlm=lm(Preference~Meter+Participant,data=x)
coef(modlm)
str(x)
x=$Participant=as.factor(x$Participant)
x$Participant=as.factor(x$Participant)
str(x)
modlm=lm(Preference~Meter+Participant,data=x)
coef(modlm)
fitit=lm(PctSuccess~seconds+genre,data=it)
fitit=lm(PctCorrect~seconds+genre,data=it)
colnames(it)
coef(fitit)
coef(fitit)
options(width=60)
coef(fitit)
allgenre=unique(it$genre)
allgenre
strsplit('abc,defgh,i3',split=',')
strsplit(c('abc,defgh,i3','123,45',split=',')
)
strsplit(c('abc,defgh,i3','123,45'),split=',')
lst=strsplit(c('abc,defgh,i3','123,45'),split=',')
lst
lst=strsplit(c('abc,defgh,i3','123,45'),split='')
lst
allgenre
plot(PctCorrect~seconds+genre,data=it)
graphics.off()
plot(PctCorrect~seconds+genre,data=it)
plot(PctCorrect~seconds+genre,data=it)
plot(PctCorrect~seconds,data=it)
?axis
plot(PctCorrect~seconds,data=it,axes=FALSE)
axis(1,hadj=90)
axis(1,hadj=0)
axis(1,padj=0)
axis(1,padj=90)
axis(1,padj=1)
?par
axis(1,las=3)
plot(PctCorrect~seconds,data=it,axes=FALSE)
axis(1,las=3)
plot(PctCorrect~seconds,data=it,las=3)
data.frame(coef(fitit))
rep(1:2,c(2,15))
c(rep(1,15),rep(20,15))
rep(c(1,20),c(15,15))
fitit=lm(PctCorrect~seconds+genre,data=it)
fitit=lm(PctCorrect~seconds+genre,data=it)
strsplit('abc,defgh,i3',split=',')
it=read.delim(NatIntelligibilityFile)
plot(PctCorrect~seconds,data=it,las=3)
allgenre=unique(it$genre)
emptydf=data.frame(genre=allgenre,seconds=rep(c(1,20),c(13,13)))
emptydf=data.frame(genre=allgenre,seconds=rep(1,13))
pred1=predict.lm(fitit,newdata=emptydf)
emptydf=data.frame(genre=allgenre,seconds=rep(15,13))
pred2=predict.lm(fitit,newdata=emptydf)
pred=data.frame(pred1,pred2)
rownames(pred)=allgenreemptydf=data.frame(genre=allgenre,seconds=rep(1,13))
pred1=predict.lm(fitit,newdata=emptydf)
emptydf=data.frame(genre=allgenre,seconds=rep(15,13))
pred2=predict.lm(fitit,newdata=emptydf)
pred=data.frame(pred1,pred2)
rownames(pred)=allgenre
emptydf=data.frame(genre=allgenre,seconds=rep(1,13))
emptydf
pred1=predict.lm(fitit,newdata=emptydf)
str(it)
fitit=lm(PctCorrect~seconds+genre,data=it)
str(emptydf)
plot(1:2,xlim=c(1,12),ylim=c(0,1))
plot(-1,xlim=c(0,12),ylim=c(0,1))
table(it$genre,it$PctCorrect)
t=table(it$genre,it$PctCorrect)
y=as.numeric(colnames(t))
length(y)
points(x=rep(1,length(y)),y=y,cex=t[1,])
points(x=rep(1,length(y)),y=y,cex=t[1,]/10)
plot(-1,xlim=c(0,12),ylim=c(0,1))
points(x=rep(1,length(y)),y=y,cex=t[1,]/10)
for(i in 1:12) points(x=rep(i,length(y)),y=y,cex=t[i,]/10)
rep(1,length(y))
plot(-1,xlim=c(0,12),ylim=c(0,1))
for(i in 1:12) points(x=rep(i,length(y)),y=y,cex=t[i,]/20)
plot(-1,xlim=c(0,12),ylim=c(0,1))
for(i in 1:12) points(x=rep(i,length(y)),y=y,cex=log(t[i,]+1))
plot(-1,xlim=c(0,12),ylim=c(0,1))
for(i in 1:12) points(x=rep(i,length(y)),y=y,cex=t[i,]/20)
for(i in 1:12) points(rep(i,length(y)),y,cex=t[i,]/20)
mod=lmer(PctCorrect~seconds,data=it)
mod=lmer(PctCorrect~seconds,data=it)
mod=lmer(PctCorrect~seconds+(seconds|genre),data=it)
coef(mod)
fit=lm(PctCorrect~seconds,data=it)
coef(fit)
plot(PctCorrect~seconds,data=it,las=3)
abline(fit)
cf=coef(mod)$genre
str(cf)
cf[1,]
cf=as.matrix(coef(mod)$genre)
coef(mod)$genre
for(i in 1:13) abline(cf[i,])
mod2=lmer(PctCorrect~1+(1|genre)+(1|subject),data=it)
coef(mod2)$genre
coef(mod2)$subject
mod3=lmer(PctCorrect~seconds+(seconds|genre)+(seconds|subject),data=it)
coef(mod3)$genre
coef(mod)$genre
coef(mod3)$subject
cf3=as.matrix(coef(mod3)$genre)
mod3=lmer(PctCorrect~seconds+(seconds|genre)+(seconds|subject),data=it)
for(i in 1:13) abline(cf3[i,],col=i)
for(i in 1:13) abline(cf[i,],col=i)
for(i in 1:13) abline(cf[i,],col=i,lty='dashed')
for(i in 1:13) abline(cf[i,],col=i)
for(i in 1:13) abline(cf3[i,],col=i,lty='dashed')
mod2=lmer(PctCorrect~1+(1|genre)+(1|subject)+(1|excerpt),data=it)
coef(mod2)$genre
mod4=lmer(PctCorrect~1+(1|genre)+(1|subject)+(1|excerpt),data=it)
mod2=lmer(PctCorrect~1+(1|genre)+(1|subject),data=it)
coef(mod2)$genre
coef(mod4)$genre
se.coef(mod4)$genre
se.coef(mod2)$genre
mod5=lmer(PctCorrect~genre+(genre|subject)+(genre|excerpt),data=it)
mod5=lmer(PctCorrect~genre+(genre|subject)+(genre|excerpt),data=it,verbose=TRUE)
hist(study3$Tessitura)
hist(study3$Tessitura,breaks=7)
hist(study3$Authority,breaks=7)
x=runif(100,min=1,max=7
)
x=runif(100,min=1,max=7)
y=2*x + run(100,min=(-3),max=3)
y=2*x + runif(100,min=(-3),max=3)
plot(x,y)
hist(x)
hist(y)
y=.2*x + runif(100,min=(-3),max=3)
hist(y)
lm(y~x)
y=.2*x + runif(100,min=(-3),max=3)
lm(y~x)
y=.2*x + runif(100,min=(-3),max=3)
lm(y~x)
y=.2*x + runif(100,min=(-3),max=3)
lm(y~x)
x=c(rep(1,50),rep(7,50))
y=.2*x + runif(100,min=(-3),max=3)
x=c(rep(1,50),rep(7,50))
y=.2*x + runif(100,min=(-3),max=3)
lm(y~x)
y=.2*x + runif(100,min=(-3),max=3)
lm(y~x)
savehistory('~/meetings_workshops/Rmodeling/OSU/history/hist10May.txt')