CritCF

dataframe for which the number of cluster should be estimated.

data

string, clustering algorithm to use. Available values are
"<code>kmeans</code>", "<code>hc</code>" (for <code>hclust()</code>) or "<code>mclust</code>".

method

string, distance between the observations (either "euclidean"
or "manhattan").

distance

If not <code>null</code>, passed to <code>set.seed()</code> before generating
the partition.

Seed

Computes <code>CritCF</code> index for the selected number of clusters and
clustering algorithm.

internal

Algorithms for (i) unsupervised learning for dataset with
missing data and/or left-censored data, using multiple imputation and
consensus clustering ; (ii) semi-supervised learning with a survival
endpoint (right-censored) for complete or incomplete datasets, using
multiple imputation and consensus clustering in the latter case. The
methods are described in Faucheux et al. (2021)
<doi:10.1002/bimj.201900366> and Faucheux et al. (2021)
<doi:10.1002/bimj.202000365>, respectively.

CritCF: `CritCF` index

Description

Usage

Arguments

Value