bootFlexclust {flexclust} | R Documentation |
Runs clustering algorithms repeatedly for different numbers of clusters on bootstrap replica of the original data and returns corresponding cluster assignments, centroids and Rand indices comparing pairs of partitions.
bootFlexclust(x, k, nboot=100, correct=TRUE, seed=NULL, multicore=TRUE, verbose=FALSE, ...) ## S4 method for signature 'bootFlexclust' summary(object) ## S4 method for signature 'bootFlexclust,missing' plot(x, y, ...) ## S4 method for signature 'bootFlexclust' boxplot(x, ...) ## S4 method for signature 'bootFlexclust' densityplot(x, data, ...)
x, k, ... |
Passed to |
nboot |
Number of bootstrap pairs of partitions. |
correct |
Logical, correct the index for agreement by chance? |
seed |
If not |
multicore |
If |
verbose |
If |
y, data |
Not used. |
object |
An object of class |
Availability of multicore is checked
when flexclust is loaded and stored in
getOption("flexclust")$have_multicore
. Set to FALSE
for debugging and more sensible error messages in case something
goes wrong.
Friedrich Leisch
## data uniform on unit square x <- matrix(runif(400), ncol=2) cl <- FALSE ## Not run: ## to run bootstrap replications on snow cluster do the following: library("snow") cl <- makeCluster(2, type = "SOCK") clusterCall(cl, function() require("flexclust")) ## End(Not run) ## 50 bootstrap replicates for speed in example, ## use more for real applications bcl <- bootFlexclust(x, k=2:7, nboot=50, FUN=cclust, multicore=cl) bcl summary(bcl) ## splitting the square into four quadrants should be the most stable ## solution (increase nboot if not) plot(bcl) densityplot(bcl, from=0)