qtcatClust: Hierarchical clustering for big SNP data sets.
In QTCAT/qtcat: Quantitative Trait Cluster Association Test

Description Usage Arguments See Also Examples

A three step approximated hierarchical clustering of SNPs suitable to large data sets.

1 2	qtcatClust(snp, k, identicals = TRUE, maxNeigbours = 100, nLocal = 10, method = "complete", mc.cores = 1, trace = FALSE, ...)

`snp`	an object of class snpMatrix.
`k`	a positive integer specifying the number of clusters, less than the number of observations.
`identicals`	logical, if zero clustering.
`maxNeigbours`	a positive integer, specifying the maximum number of randomized searches.
`nLocal`	a positive integer, specifying the number of optimisation runs. Columns have to be similar to `snp`.
`method`	see hclust.
`mc.cores`	a number of cores for parallel computing. See `mclapply` in package parallel for details.
`trace`	logical, if `TRUE` it prints current status of the program.
`...`	additional argruments for `hclust`

clarans

# file containing example data for SNP data
gfile <- system.file("extdata/snpdata.csv", package = "qtcat")
snp <- read.snpData(gfile, sep = ",")

clust <- qtcatClust(snp)