setwd("C:/Courses/STA5703/2009-Fall/R/")

# ---------------------------------------
#  PLOTTING CLUSTERING SOLUTIONS WITH MDS
# ---------------------------------------

data(swiss)
dat0 <- na.omit(swiss)  
mydata <- scale(dat0)

# K-Means Clustering with 3 clusters
fit <- kmeans(mydata, 3)

# Cluster Plot against 1st 2 principal components
# vary parameters for most readable graph
library(cluster) 
?clusplot
clusplot(mydata, clus=fit$cluster, color=TRUE, shade=TRUE, 
   labels=2, lines=0)


# .......................................................
# Centroid Plot against 1st 2 discriminant functions
library(fpc)
plotcluster(mydata, fit$cluster) 
# .......................................................



# Classical/Metric MDS
# --------------------

# You can perform a classical MDS using the cmdscale( ) function. 

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

d <- dist(mydata) # euclidean distances between the rows
fit <- cmdscale(d,eig=TRUE, 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="Metric MDS", type="n")
text(x, y, labels = row.names(mydata), cex=.7) 


# Nonmetric MDS 
# --------------

# Nonmetric MDS is performed using the isoMDS( ) function in the MASS package.

# 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(mydata), cex=.7)