CLUSTERGRAM

Prepared by Volkan OBAN
CLUSTERGRAM
SOURCE:
 https://gist.github.com/hadley/439761 (hadley/clustergram-had.r)
 http://www.r-statistics.com/tag/large-data/
CODES:
> clustergram.kmeans <- function(Data, k, ...)
+ {
+ # this is the type of function that the clustergram
+ # function takes for the clustering.
+ # using similar structure will allow implementation of differe
nt clustering algorithms
+
+ # It returns a list with two elements:
+ # cluster = a vector of length of n (the number of subjects/items)
+ # indicating to which cluster each item belong
s.
+ # centers = a k dimensional vector. Each element is 1 number that re
present that cluster
+ # In our case, we are using the weighted mean
of the cluster dimensions by
+ # Using the first component (loading) of the P
CA of the Data.
+
+ cl <- kmeans(Data, k,...)
+
+ cluster <- cl$cluster
+ centers <- cl$centers %*% princomp(Data)$loadings[,1] # 1 number per
center
+ # here we are using the weighted mean for each
+
+ return(list(
+ cluster = cluster,
+ centers = centers
+ ))
+ }
>
> clustergram.plot.matlines <- function(X,Y, k.range,
+ x.range, y.range , COL,
+ add.center.points , centers.points)
+ {
+ plot(0,0, col = "white", xlim = x.range, ylim = y.range,
+ axes = F,
+ xlab = "Number of clusters (k)", ylab = "PCA weighted Mean of th
e clusters", main = c("Clustergram of the PCA-weighted Mean of" ,"the clust
ers k-mean clusters vs number of clusters (k)"))
+ axis(side =1, at = k.range)
+ axis(side =2)

+ abline(v = k.range, col = "grey")
+
+ matlines(t(X), t(Y), pch = 19, col = COL, lty = 1, lwd = 1.5)
+
+ if(add.center.points)
+ {
+ require(plyr)
+
+ xx <- ldply(centers.points, rbind)
+ points(xx$y~xx$x, pch = 19, col = "red", cex = 1.3)
+
+ # add points
+ # temp <- l_ply(centers.points, function(xx) {
+ # with(xx,points(y~x, pch = 19, col = "red", cex = 1.3))
+ # points(xx$y~xx$x, pch = 19, col = "red", cex = 1.3)
+ # return(1)
+ # })
+ # We assign the lapply to a variable (temp) only to suppress the
lapply "NULL" output
+ }
+ }
>
>
>
> clustergram <- function(Data, k.range = 2:10 ,
+ clustering.function = clustergram.kmeans,
+ clustergram.plot = clustergram.plot.matlines,
+ line.width = .004, add.center.points = T)
+ {
+ # Data - should be a scales matrix. Where each column belongs to a d
ifferent dimension of the observations
+ # k.range - is a vector with the number of clusters to plot the clust
ergram for
+ # clustering.function - this is not really used, but offers a bases t
o later extend the function to other algorithms
+ # Although that would more work on the code
+ # line.width - is the amount to lift each line in the plot so they wo
n't superimpose eachother
+ # add.center.points - just assures that we want to plot points of the
cluster means
+
+ n <- dim(Data)[1]
+
+ PCA.1 <- Data %*% princomp(Data)$loadings[,1] # first principal comp
onent of our data
+
+ if(require(colorspace)) {
+ COL <- heat_hcl(n)[order(PCA.1)] # line colors
+ } else {
+ COL <- rainbow(n)[order(PCA.1)] # line colors
+ warning('Please consider installing the package "colorspace" for
prittier colors')
+ }
+
+ line.width <- rep(line.width, n)
+
+ Y <- NULL # Y matrix
+ X <- NULL # X matrix
+
+ centers.points <- list()
+
+ for(k in k.range)

+ {
+ k.clusters <- clustering.function(Data, k)
+
+ clusters.vec <- k.clusters$cluster
+ # the.centers <- apply(cl$centers,1, mean)
+ the.centers <- k.clusters$centers
+
+ noise <- unlist(tapply(line.width, clusters.vec, cumsum))[order(s
eq_along(clusters.vec)[order(clusters.vec)])]
+ # noise <- noise - mean(range(noise))
+ y <- the.centers[clusters.vec] + noise
+ Y <- cbind(Y, y)
+ x <- rep(k, length(y))
+ X <- cbind(X, x)
+
+ centers.points[[k]] <- data.frame(y = the.centers , x = rep(k , k
))
+ # points(the.centers ~ rep(k , k), pch = 19, col = "red", cex
= 1.5)
+ }
+
+
+ x.range <- range(k.range)
+ y.range <- range(PCA.1)
+
+ clustergram.plot(X,Y, k.range,
+ x.range, y.range , COL,
+ add.center.points , centers.points)
+
+
+ }
>
>
>
>
> if(F) {
+
+ #Examples:
+
+ png("d:clustergram_plots_%03d.png",650,650, pointsize = 15)
+
+ data(iris)
+ set.seed(250)
+ par(cex.lab = 1.5, cex.main = 1.2)
+ Data <- scale(iris[,-5]) # notice I am scaling the vectors)
+ clustergram(Data, k.range = 2:8, line.width = 0.004) # notice how I a
m using line.width. Play with it on your problem, according to the scale o
f Y.
+
+ set.seed(500)
+ Data <- scale(iris[,-5]) # notice I am scaling the vectors)
+ par(cex.lab = 1.2, cex.main = .7)
+ par(mfrow = c(3,2))
+ for(i in 1:6) clustergram(Data, k.range = 2:8 , line.width = .004, ad
d.center.points = T)
+ par(mfrow = c(1,1))
+
+ set.seed(250)
+ Data <- rbind(
+ cbind(rnorm(100,0, sd = 0.3),rnorm(100,0, sd = 0.3),rnorm(100,0,
sd = 0.3)),

sd = 0.3)),
sd = 0.3))
+ )
+ clustergram(Data, k.range = 2:5 , line.width = .004, add.center.point
s = T)
+
+ set.seed(250)
+ Data <- rbind(
sd = 0.3),rnorm(100,0, sd = 0.3)),
sd = 0.3),rnorm(100,0, sd = 0.3)),
sd = 0.3),rnorm(100,0, sd = 0.3)),
sd = 0.3),rnorm(100,1, sd = 0.3))
+ )
+ clustergram(Data, k.range = 2:8 , line.width = .004, add.center.point
s = T)
+
+ dev.off()
+ }
>source("http://www.r-statistics.com/wp-content/uploads/2012/01/source_http
s.r.txt") # Making sure we can source code from github
>source_https("https://raw.github.com/talgalili/R-code-snippets/master/clus
tergram.r")
> data(iris)
> set.seed(250)
> par(cex.lab = 1.5, cex.main = 1.2)
> Data <- scale(iris[,-5]) # notice I am scaling the vectors)
> clustergram(Data, k.range = 2:8, line.width = 0.004)

source("http://www.r-statistics.com/wp-
content/uploads/2012/01/source_https.r.txt") #
Making sure we can source code from github
source_https("https://raw.github.com/talgalili/
R-code-snippets/master/clustergram.r")
set.seed(500)
Data <- scale(iris[,-5]) # notice I am scaling
the vectors)
par(cex.lab = 1.2, cex.main = .7)
par(mfrow = c(3,2))
for(i in 1:6) clustergram(Data, k.range = 2:8 ,
line.width = .004, add.center.points = T)

CLUSTERGRAM

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CLUSTERGRAM