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

In missMDA: Handling missing values with/in multivariate data analysis (principal component methods)

Description

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

Usage

estim_ncpMCA(don, ncp.min=0, ncp.max=5, nbsim=100, pNA=0.05, threshold=1e-4)

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
nbsim	number of simulations
pNA	percentage of missing values added in the data set
threshold	the threshold for assessing convergence

Details

For the cross-validation, pNA percentage of missing values are removed at random 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. Each cell is predicted using the imputeMCA function, it means using the regularized iterative MCA algorithm.

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.

Examples

## Not run: 
data(vnf)
result <- estim_ncpMCA(vnf,ncp.min=0, ncp.max=3, nbsim=100)

## End(Not run)

missMDA documentation built on May 2, 2019, 5:46 p.m.