WH.MultiplePCA: Principal components analysis of a set of histogram variable...

In HistDAWass: Histogram-Valued Data Analysis

Principal components analysis of a set of histogram variable based on Wasserstein distance

Description

(Beta version) The function implements a Principal components analysis of a set of histogram variables based on Wasserstein distance. It performs a centered (not standardized) PCA on a set of quantiles of a variable. Being a distribution a multivalued description, the analysis performs a dimensional reduction and a visualization of distributions. It is a 1d (one dimension) becuse it is considered just one histogram variable.

Usage

WH.MultiplePCA(data, list.of.vars, quantiles = 10, outl = 0)

Arguments

data	A MatH object (a matrix of distributionH).
list.of.vars	A list of integers, the active variables.
quantiles	An integer, it is the number of quantiles used in the analysis. Default=10.
outl	a number between 0 (default) and 0.5. For each distribution, is the amount of mass removed from the tails of the distribution. For example, if 0.1, from each distribution is cut away a left tail and a right one each containing the 0.1 of mass.

Details

It is an extension of WH.1d.PCA to the multiple case.

Value

a list with the results of the PCA in the MFA format of package FactoMineR for function MFA

HistDAWass documentation built on Sept. 26, 2022, 5:06 p.m.