Bootstrap Flexclust Algorithms
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. This information is stored and can be
obtained using
getOption("flexclust")$have_multicore
. Set to FALSE
for debugging and more sensible error messages in case something
goes wrong.
Friedrich Leisch
## Not run: ## data uniform on unit square x <- matrix(runif(400), ncol=2) cl <- FALSE ## to run bootstrap replications on a workstation cluster do the following: library("parallel") cl <- makeCluster(2, type = "PSOCK") clusterCall(cl, function() require("flexclust")) ## 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) ## End(Not run)
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