estim_ncp: Estimate the number of components in Principal Component...

In FactoMineR: Multivariate Exploratory Data Analysis and Data Mining

Estimate the number of components in Principal Component Analysis

Description

Estimate the number of components in PCA .

Usage

estim_ncp(X, ncp.min=0, ncp.max=NULL, scale=TRUE, method="GCV")

Arguments

X	a data frame with continuous variables
ncp.min	minimum number of dimensions to interpret, by default 0
ncp.max	maximum number of dimensions to interpret, by default NULL which corresponds to the number of columns minus 2
scale	a boolean, if TRUE (value set by default) then data are scaled to unit variance
method	method used to estimate the number of components, "GCV" for the generalized cross-validation approximation or "Smooth" for the smoothing method (by default "GCV")

Value

Returns ncp the best number of dimensions to use (find the minimum or the first local minimum) and the mean error for each dimension tested

Author(s)

Francois Husson [e-mail], Julie [e-mail]

References

Josse, J. and Husson, F. (2012). Selecting the number of components in PCA using cross-validation approximations. Computational Statistics and Data Analysis, 56, 1869-1879.

See Also

PCA

Examples

data(decathlon)
nb.dim <- estim_ncp(decathlon[,1:10],scale=TRUE)

FactoMineR documentation built on May 29, 2024, 3:36 a.m.