MIclust_mpool: 'MultiCons' wrapper for imputed datasets

In LilithF/doMIsaul: Do Multiple Imputation-Based Semi-Supervised and Unsupervised Learning

Description

Performs MultiCons() from a list of partitions

Usage

MIclust_mpool(list.part, comb.cons, mcons.JAC.sel = 0, plot.MIclust = FALSE)

Arguments

list.part	list of partitions, where one element of the list corresponds to the clustering results for one imputed dataset. If more than one clustering algorithm were used, each element if the list is a dataframe, as obtained by partition_generation().
comb.cons	Boolean, use TRUE to perform an additional consensus from all partitions (ie. one consensus per clustering algorithm used, plus one consensus of all partitions: mixing all clustering algorithms used). This parameter is forced to FALSE if length(algo)<2.
mcons.JAC.sel	Numeric (in (0,1)) passed to internal function my_jack(). Minimum Jaccard index value between partitions to keep them for the consensus.
plot.MIclust	Boolean, should MultiCons() tree be plotted?

Value

a data frame with ncol() = number of algorithms (+1 if comb.cons == T), containing the consensus partitions.

LilithF/doMIsaul documentation built on Dec. 17, 2021, 12:08 a.m.