par9 <- 'cases'
par8 <- 'ATTLES connected'
par7 <- 'all'
par6 <- 'all'
par5 <- 'all'
par4 <- 'FALSE'
par3 <- 'FALSE'
par2 <- '10'
par1 <- 'ward'
x <- as.data.frame(read.table(file='https://automated.biganalytics.eu/download/utaut.csv',sep=',',header=T))
x$U25 <- 6-x$U25
if(par5 == 'female') x <- x[x$Gender==0,]
if(par5 == 'male') x <- x[x$Gender==1,]
if(par6 == 'prep') x <- x[x$Pop==1,]
if(par6 == 'bachelor') x <- x[x$Pop==0,]
if(par7 != 'all') {
x <- x[x$Year==as.numeric(par7),]
}
cAc <- with(x,cbind( A1, A2, A3, A4, A5, A6, A7, A8, A9,A10))
cAs <- with(x,cbind(A11,A12,A13,A14,A15,A16,A17,A18,A19,A20))
cA <- cbind(cAc,cAs)
cCa <- with(x,cbind(C1,C3,C5,C7, C9,C11,C13,C15,C17,C19,C21,C23,C25,C27,C29,C31,C33,C35,C37,C39,C41,C43,C45,C47))
cCp <- with(x,cbind(C2,C4,C6,C8,C10,C12,C14,C16,C18,C20,C22,C24,C26,C28,C30,C32,C34,C36,C38,C40,C42,C44,C46,C48))
cC <- cbind(cCa,cCp)
cU <- with(x,cbind(U1,U2,U3,U4,U5,U6,U7,U8,U9,U10,U11,U12,U13,U14,U15,U16,U17,U18,U19,U20,U21,U22,U23,U24,U25,U26,U27,U28,U29,U30,U31,U32,U33))
cE <- with(x,cbind(BC,NNZFG,MRT,AFL,LPM,LPC,W,WPA))
cX <- with(x,cbind(X1,X2,X3,X4,X5,X6,X7,X8,X9,X10,X11,X12,X13,X14,X15,X16,X17,X18))
if (par8=='ATTLES connected') x <- cAc
if (par8=='ATTLES separate') x <- cAs
if (par8=='ATTLES all') x <- cA
if (par8=='COLLES actuals') x <- cCa
if (par8=='COLLES preferred') x <- cCp
if (par8=='COLLES all') x <- cC
if (par8=='CSUQ') x <- cU
if (par8=='Learning Activities') x <- cE
if (par8=='Exam Items') x <- cX
ncol <- length(x[1,])
for (jjj in 1:ncol) {
x <- x[!is.na(x[,jjj]),]
}
par3 <- as.logical(par3)
par4 <- as.logical(par4)
if (par3 == TRUE){
dum = xlab
xlab = ylab
ylab = dum
}
if (par9=='variables') {
x <- t(x)
} else {
ncol <- length(x[1,])
colnames(x) <- 1:ncol
}
hc <- hclust(dist(x),method=par1)
d <- as.dendrogram(hc)
str(d)
mysub <- paste('Method: ',par1)
bitmap(file='test1.png')
if (par4 == TRUE){
plot(d,main=main,ylab=ylab,xlab=xlab,horiz=par3, nodePar=list(pch = c(1,NA), cex=0.8, lab.cex = 0.8),type='t',center=T, sub=mysub)
} else {
plot(d,main=main,ylab=ylab,xlab=xlab,horiz=par3, nodePar=list(pch = c(1,NA), cex=0.8, lab.cex = 0.8), sub=mysub)
}
dev.off()
if (par2 != 'ALL'){
if (par3 == TRUE){
ylab = 'cluster'
} else {
xlab = 'cluster'
}
par2 <- as.numeric(par2)
memb <- cutree(hc, k = par2)
cent <- NULL
for(k in 1:par2){
cent <- rbind(cent, colMeans(x[memb == k, , drop = FALSE]))
}
hc1 <- hclust(dist(cent),method=par1, members = table(memb))
de <- as.dendrogram(hc1)
bitmap(file='test2.png')
if (par4 == TRUE){
plot(de,main=main,ylab=ylab,xlab=xlab,horiz=par3, nodePar=list(pch = c(1,NA), cex=0.8, lab.cex = 0.8),type='t',center=T, sub=mysub)
} else {
plot(de,main=main,ylab=ylab,xlab=xlab,horiz=par3, nodePar=list(pch = c(1,NA), cex=0.8, lab.cex = 0.8), sub=mysub)
}
dev.off()
str(de)
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Summary of Dendrogram',2,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Label',header=TRUE)
a<-table.element(a,'Height',header=TRUE)
a<-table.row.end(a)
num <- length(x[,1])-1
for (i in 1:num)
{
a<-table.row.start(a)
a<-table.element(a,hc$labels[i])
a<-table.element(a,hc$height[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable1.tab')
if (par2 != 'ALL'){
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Summary of Cut Dendrogram',2,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Label',header=TRUE)
a<-table.element(a,'Height',header=TRUE)
a<-table.row.end(a)
num <- par2-1
for (i in 1:num)
{
a<-table.row.start(a)
a<-table.element(a,i)
a<-table.element(a,hc1$height[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
}
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