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#' Clustering with the optimal parameters estimated by these tools
#'
#' @param data A dataframe, where columns are features and rows are data points
#' @param clusters Number of clusters to be generated by this clustering
#' @param algorithm The clustering algorithm to be used
#'
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#' @return An object of class "optimalClustering" containing a dataframe with
#' the memberships of the samples found in the input data, the optimal
#' stability score and parameter used
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#'
#' @export
#'
#' @examples
#' optimalClustering(toy_genes, 2,"kmeans")
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#'
#' @importFrom fpc speccCBI hclustCBI kmeansCBI
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optimalClustering <- function(data, clusters, algorithm) {
if (algorithm == "spectral") {
spectral.kernels <- c("rbfdot", "polydot", "vanilladot", "tanhdot",
"laplacedot", "anovadot", "splinedot")
optimal.stability <- 0
optimal.memberships <- seq(0,0,length.out=dim(data)[1])
for (par in spectral.kernels) {
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sc.boot <- fpc::clusterboot(data,
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B = 25,
bootmethod = "boot",
clustermethod = speccCBI,
k = clusters,
kernel = par,
seed = 28588,
showplots = FALSE,
count=FALSE)
if (mean(sc.boot$bootmean) > optimal.stability) {
optimal.stability <- mean(sc.boot$bootmean)
optimal.memberships <- sc.boot$partition
optimal.parameter <- par
}
}
} else if (algorithm == "hierarchical") {
hierarchical.methods <- c("average", "ward.D", "ward.D2", "single",
"complete", "mcquitty", "median", "centroid")
optimal.stability <- 0
optimal.memberships <- seq(0,0,length.out=dim(data)[1])
for (par in hierarchical.methods) {
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sc.boot <- fpc::clusterboot(data,
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B = 25,
bootmethod = "boot",
clustermethod = hclustCBI,
k = clusters,
method = par,
seed = 28588,
showplots = FALSE,
count=FALSE)
if (mean(sc.boot$bootmean) > optimal.stability) {
optimal.stability <- mean(sc.boot$bootmean)
optimal.memberships <- sc.boot$partition
optimal.parameter <- par
}
}
} else {
kmeans.kernels <- c("Hartigan-Wong", "Lloyd", "Forgy",
"MacQueen")
optimal.stability <- 0
optimal.memberships <- seq(0,0,length.out=dim(data)[1])
for (par in kmeans.kernels) {
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sc.boot <- fpc::clusterboot(data,
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B = 25,
bootmethod = "boot",
clustermethod = kmeansCBI,
k = clusters,
algorithm = par,
seed = 28588,
showplots = FALSE,
count=FALSE)
if (mean(sc.boot$bootmean) > optimal.stability) {
optimal.stability <- mean(sc.boot$bootmean)
optimal.memberships <- sc.boot$partition
optimal.parameter <- par
}
}
}
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memberships <- data.frame(id = rownames(data),
memberships = optimal.memberships)
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optimalClustering <-
function(optimal.memberships = memberships,
optimal.stability.score = optimal.stability,
optimal.parameter.used = optimal.parameter){
oc <- list(optimal.memberships = optimal.memberships,
optimal.stability.score = optimal.stability.score,
optimal.parameter.used = optimal.parameter.used)
## Set the name for the class
class(oc) <- "optimalClustering"
return(oc)
}
optimal.clustering <- optimalClustering()
return(optimal.clustering)
}
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