estim_ncpMCA: Estimate the number of dimensions for the Multiple...

In missMDA: Handling Missing Values with Multivariate Data Analysis

Estimate the number of dimensions for the Multiple Correspondence Analysis by cross-validation

Description

Estimate the number of dimensions for the Multiple Correspondence Analysis by cross-validation

Usage

estim_ncpMCA(don, ncp.min=0, ncp.max=5,  method = c("Regularized","EM"), 
     method.cv = c("Kfold","loo"), nbsim=100, pNA=0.05, ind.sup=NULL,
	 quanti.sup=NULL, quali.sup=NULL, threshold=1e-4,verbose = TRUE)

Arguments

don	a data.frame with categorical variables; with missing entries or not
ncp.min	integer corresponding to the minimum number of components to test
ncp.max	integer corresponding to the maximum number of components to test
method	"Regularized" by default or "EM"
method.cv	"Kfold" for cross-validation or "loo" for leave-one-out
nbsim	number of simulations, useful only if method.cv="Kfold"
pNA	percentage of missing values added in the data set, useful only if method.cv="Kfold"
ind.sup	a vector indicating the indexes of the supplementary individuals
quanti.sup	a vector indicating the indexes of the quantitative supplementary variables
quali.sup	a vector indicating the indexes of the categorical supplementary variables
threshold	the threshold for assessing convergence
verbose	boolean. TRUE means that a progressbar is writtent

Details

For leave-one-out cross-validation (method.cv="loo"), each cell of the data matrix is alternatively removed and predicted with a MCA model using ncp.min to ncp.max dimensions. The number of components which leads to the smallest mean square error of prediction (MSEP) is retained. For the Kfold cross-validation (method.cv="Kfold"), pNA percentage of missing values is inserted at random in the data matrix and predicted with a MCA model using ncp.min to ncp.max dimensions. This process is repeated nbsim times. The number of components which leads to the smallest MSEP is retained. More precisely, for both cross-validation methods, the missing entries are predicted using the imputeMCA function, it means using it means using the regularized iterative MCA algorithm (method="Regularized") or the iterative MCA algorithm (method="EM"). The regularized version is more appropriate to avoid overfitting issues.

Value

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

Author(s)

Francois Husson [e-mail] and Julie Josse [e-mail]

References

Josse, J., Chavent, M., Liquet, B. and Husson, F. (2010). Handling missing values with Regularized Iterative Multiple Correspondence Analysis, Journal of Clcassification, 29 (1), pp. 91-116.

See Also

imputeMCA

Examples

## Not run: 
data(vnf)
result <- estim_ncpMCA(vnf,ncp.min=0, ncp.max=5)

## End(Not run)

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