Difference between revisions of "Cluster.R"
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(bugs) |
(# {{R}}) |
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| − | clusterPlot <- function(x, dmethod = "euclidian", hmethod="complete", ...) { | + | # {{R}} |
| + | clusterPlot <- function(x, rowNames = NULL, dmethod = "euclidian", hmethod="complete", ...) { | ||
oldpar <- par(mfrow=c(2,1)) | oldpar <- par(mfrow=c(2,1)) | ||
| + | |||
| + | if(is.character(rowNames) & (length(rowNames) == nrow(x))) { | ||
| + | rownames(x) <- rowNames | ||
| + | } | ||
xDist <- dist(x, method=dmethod) | xDist <- dist(x, method=dmethod) | ||
# 1) Tree dendrogram | # 1) Tree dendrogram | ||
xClust <- hclust(xDist, method=hmethod) | xClust <- hclust(xDist, method=hmethod) | ||
| − | plclust(xClust) | + | plclust(xClust, sub="") |
# 2) Image plot... | # 2) Image plot... | ||
| − | image(x = 1:nrow(x), y = 1:ncol(x), z = x[xClust$order, ]) | + | image(x = 1:nrow(x), y = 1:ncol(x), z = x[xClust$order, ], col=heat.colors(nrow(x)), ...) |
par(oldpar) | par(oldpar) | ||
invisible() | invisible() | ||
} | } | ||
| − | # | + | # Euclidian distance |
| + | X <- matrix(1:9, nc=3) | ||
| + | X1 <- X[1,] | ||
| + | X2 <- X[2,] | ||
| + | |||
| + | dist(X) | ||
| + | sqrt(sum((X1-X2)^2)) | ||
| + | |||
| + | # Example | ||
x <- matrix(rnorm(40), nc=4) | x <- matrix(rnorm(40), nc=4) | ||
| + | colnames(x) <- LETTERS[1:4] | ||
| + | rownames(x) <- letters[1:10] | ||
clusterPlot(x) | clusterPlot(x) | ||
| + | clusterPlot(x, rowNames = LETTERS[1:10]) | ||
Latest revision as of 19:46, 4 November 2007
Code snipits and programs written in R, S or S-PLUS clusterPlot <- function(x, rowNames = NULL, dmethod = "euclidian", hmethod="complete", ...) {
oldpar <- par(mfrow=c(2,1))
if(is.character(rowNames) & (length(rowNames) == nrow(x))) {
rownames(x) <- rowNames
}
xDist <- dist(x, method=dmethod)
- 1) Tree dendrogram
xClust <- hclust(xDist, method=hmethod) plclust(xClust, sub="")
- 2) Image plot...
image(x = 1:nrow(x), y = 1:ncol(x), z = x[xClust$order, ], col=heat.colors(nrow(x)), ...) par(oldpar) invisible()
}
- Euclidian distance
X <- matrix(1:9, nc=3) X1 <- X[1,] X2 <- X[2,]
dist(X) sqrt(sum((X1-X2)^2))
- Example
x <- matrix(rnorm(40), nc=4) colnames(x) <- LETTERS[1:4] rownames(x) <- letters[1:10] clusterPlot(x) clusterPlot(x, rowNames = LETTERS[1:10])
