ClustPlus: A clustering function

In MSeidelFed/RandodiStats_package: Random distributions and their univariate and multivariate statistics

A clustering function

Description

This function allows you to rationalize the clustering method selected.

Usage

ClustPlus(
  mat = distribution_test_mat(nrow_x = 50, n_random_distributions = 100, class_method =
    "discovery"),
  autoscale_mat = T,
  center_fun = colMeans,
  scale_fun = colSds,
  color_clust_Nr = 5,
  nboots = 100,
  AU_p_value = 0.95,
  out_PDF = T,
  out_PDF_name = "test"
)

Arguments

mat	Defaults to a randomly generated matrix from the distribution_test_mat function.
autoscale_mat	Defaults to True, allows to center and scale the input matrix.
center_fun	Defaults to colMeans, allows to select any function that can be applied to columns as condition to center the matrix.
scale_fun	Defaults to colSds, allows to select any function that can be applied to columns as condition to scale the matrix.
color_clust_Nr	Defaults to 5, is the number of colored-partitions that the decision dendrograms will have.
nboots	Defaults to 100, is the number of bootstrapps using the package pvclust.
AU_p_value	Defaults to 0.95 or in other words the classic P value 0.05 treshold to define a significant cluster.
out_PDF	Defaults to TRUE, if TRUE generates the respective bootstrapped-dendrogram plots.
out_PDF_name	Defaults to test, name of the PDF file that will contain the plots.

Examples

test <- ClustPlus()

MSeidelFed/RandodiStats_package documentation built on July 31, 2022, 3 a.m.