dat<-data.frame(x1=c(50,69,93,76,88,43,56,38,21,25),
                x2=c(15.5,18.4,26.4,22.9,18.6,16.9,21.6,12.2,16.0,10.5), 
                cl=c(rep("h",5),rep("d",5)))
library(MASS)
dat.lda<-lda(cl~.,data=dat)
 2, col='skyblue')

dat<-read.csv("http://matsuka.info/data_folder/tdkDA01.csv", header=T)
dat.lda<-lda(class~.,dat)


dat<-read.csv("http://matsuka.info/data_folder/tdkDA02.csv",header=T)
dat.lda=lda(class~.,data=dat)
lda.pred<-predict(dat.lda,dat)

plot(dat.lda, dimen=2, col=as.numeric(dat$class), cex=3)
plot(dat.lda, dimen=5, col=as.numeric(lda.pred$class),cex=2)


dat.km<-kmeans(dat[,1:6],5)

dat<-read.csv("http://www.matsuka.info/data_folder/tdkCFA.csv")
dat1<-subset(dat,dat$popularity<5)
dat2<-subset(dat,dat$popularity>4 & dat$popularity<6)
dat3<-subset(dat,dat$popularity>6)
datT=rbind(dat1,dat2,dat3)
datT.lda<-lda(popularity~.,datT)
datT.pred<-predict(datT.lda,datT)
table(datT.pred$class,datT$popularity)   

plot(datT.lda,col=as.numeric(datT.pred$class)+2,cex=1)
colors=rep("black",nrow(datT))
miss.idx = which(datT.pred$class!= datT$popularity)
colors[miss.idx]='red'
points(datT.pred$x,pch=20,col=colors)
legend("bottomright",c("correct pred.", "missed"), pch=20,col=(1:2))


dat<-read.csv("http://matsuka.info/data_folder/tdkDA01.csv", header=T)
dat.lda<-lda(class~.,dat)
dat.glm<-glm(class~.,family=binomial,data=dat)

#MDS
dat<-data.frame(p1=c(4,1,5,1,5),p2=c(1,5,4,3,1))
rownames(dat)<-c('a','b','c','d','e')
dat.mds<-cmdscale(dist(dat),2)
plot(dat.mds[,1],dat.mds[,2], type='n')
text(dat.mds[,1],dat.mds[,2],labels=row.names(dat))
dat<-read.csv("http://matsuka.info/data_folder/tdkMDS02.csv", row.name=1)
dat.mds<-cmdscale(dist(t(dat)),2,eig=T)

plot(dat.mds$points[,1],dat.mds$points[,2], type='n')
text(dat.mds$points[,1],dat.mds$points[,2],labels=colnames(dat), cex=2)

dat<-read.csv("http://www.matsuka.info/data_folder/tdkCFA.csv")