kat = read.table("Psychol.txt",dec=",",header = T)
kat
head(kat)
d<-dist(kat,method="binary")
cl<-hclust(d)
plot(cl,labels=kat$Jmeno,main="Shluky psychologů")      # ,names=kat$Jmeno



sc<-cmdscale(d,eig=TRUE, k=2) # k is the number of dim
sc # view results
x<-sc$points[,1]
y<-sc$points[,2]
plot(x, y, xlab="Dim 1", ylab="Dim 2", main="MDS", type="n")
text(x, y, labels = kat$Jmeno, cex=.7) 


names(kat)


# Nonmetric MDS
# N rows (objects) x p columns (variables)
# each row identified by a unique row name

library(MASS)
d <- dist(mydata) # euclidean distances between the rows
fit <- isoMDS(d, k=2) # k is the number of dim
fit # view results

# plot solution
x <- fit$points[,1]
y <- fit$points[,2]
plot(x, y, xlab="Coordinate 1", ylab="Coordinate 2",
  main="Nonmetric MDS", type="n")
text(x, y, labels = row.names(kat), cex=.7)