dimRestrict: Significant dimensions identification

In FactoInvestigate: Automatic Description of Factorial Analysis

Significant dimensions identification

Description

Evaluate the number of significant dimensions in the data.

Usage

dimRestrict(res, file = "", rand = NULL)

Arguments

res	an object of class PCA, CA or MCA.
file	the file path where to write the function execution in Rmarkdown language. If not specified, the description is written in the console.
rand	an optional vector of eigenvalues to compare the observation with. If NULL, use the result of the eigenRef function for comparison.

Value

ncp	the number of significant dimensions.

Author(s)

Simon Thuleau and Francois Husson

See Also

eigenRef, inertiaDistrib

Examples

## Not run: 
require(FactoMineR)
data(decathlon)
res.pca = PCA(decathlon, quanti.sup = c(11:12), quali.sup = c(13), graph = FALSE)
dimRestrict(res.pca, file = "PCA.Rmd")

## End(Not run)

FactoInvestigate documentation built on Nov. 28, 2023, 1:10 a.m.