estim_ncpMultilevel: Estimate the number of dimensions for the Multilevel PCA,...

In missMDA: Handling Missing Values with Multivariate Data Analysis

Estimate the number of dimensions for the Multilevel PCA, multlevel MCA or Multilevel FAMD by cross-validation

Description

Estimate the number of dimensions for Multilevel principal component (multilevel PCA, multilevel MCA ormultilevel Factorial Analysis of Mixed Data) by cross-validation

Usage

estim_ncpMultilevel(X,  ifac=1, ncpW.min = 1, ncpW.max = 5, ncpB.min = 1, 
    ncpB.max = 5, scale = TRUE, nbsim=100, pNA=0.05, threshold=1e-4, 
	nb.cores = NULL, verbose = TRUE)

Arguments

X	a data.frame with categorical variables; with missing entries or not
ifac	index of the group variable
ncpB.min	integer corresponding to the minimum number of components to test for the between matrix
ncpB.max	integer corresponding to the maximum number of components to test for the between matrix
ncpW.min	integer corresponding to the minimum number of components to test for the within matrix
ncpW.max	integer corresponding to the maximum number of components to test for the within matrix
scale	if all the variables are continuous, should they be standardized? Yes if true.
nbsim	number of simulations
pNA	percentage of missing values added in the data set, useful only if method.cv="Kfold"
threshold	the threshold for assessing convergence
nb.cores	Integer, number of core used. By default, NULL and the number of cores used are the number of cores of your computer minus 1
verbose	boolean. TRUE means that a progressbar is writtent

Details

pNA percentage of missing values is inserted at random in the data matrix and predicted with a multilevel model using ncpB.min to ncpB.max and ncpW.min to ncpW.max dimensions. This process is repeated nbsim times. The number of components which leads to the smallest MSEP is retained. More precisely, the missing entries are predicted using the imputeMultilevel function.

Value

ncp	the number of components retained for the FAMD
criterion	the criterion (the MSEP) calculated for each number of components

See Also

imputeMultilevel

Examples

## Not run: 
data(ozone)
result <- estim_ncpMultilevel(ozone, ifac=12)

## End(Not run)

missMDA documentation built on Nov. 17, 2023, 5:07 p.m.