#' @title Use different algorithms for cluster analysis
#' @param dat_wide Data.frame in wide format: Row = case; column = values; multiple columns = e.g. values for each time step
#' @param k integer. Number of groups
#' @param ...
#' @export
clust <- function (dat_wide,
k = 3,
procedure = "kmeans",
algorithm = "Hartigan-Wong",
iter.max = 500,
nstart = 1,
start = "random",
measure4diss = "EUCL",
method4agglo = "single",
seed = 42,
...) {
outputFunProc(R)
set.seed(seed)
if (procedure == "kmeans")
res <- clust.kmeans(dat_wide, k, iter.max, nstart, algorithm, ...)
if (procedure == "kmeanspp")
res <- clust.kmeanspp(dat_wide, k, start, iter.max, nstart, algorithm, ...)
if (procedure == "hclust")
res <- clust.hclust(dat_wide, measure4diss, method4agglo, k, ...)
## No "Done!" needes, as outputs comes from other function
return (res)
}
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