dimRestrict {FactoInvestigate}

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)