estim_ncpPCA: Estimate the number of dimensions for the Principal Component...

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

Description

Estimate the number of dimensions for the Principal Component Analysis by cross-validation

Usage

estim_ncpPCA(X, ncp.min = 0, ncp.max = 5, method = "Regularized", scale = TRUE, method.cv = "loo", nbsim = 100, pNA = 0.05, threshold=1e-4)

Arguments

X	a data.frame with continuous 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"
scale	boolean. By default TRUE leading to a same weight for each variable
method.cv	string with the values "loo" for leave-one-out or "Kfold" cross-validation
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"
threshold	the threshold for assessing convergence

Details

For leave-one-out (loo) cross-validation, each value is alternatively removed and predicted with a PCA model using ncp.min to ncp.max dimensions. The number of components which leads to the smallest MSEP is retained. Each cell is predicted using the imputePCA function, it means using the regularized iterative PCA algorithm or the iterative PCA (EM cross-validation).
For the Kfold cross-validation, pNA percentage of missing values is removed and predicted with a PCA model using ncp.min to ncp.max dimensions. This process is repeated nbsim times. The leave-one-out method is time-consuming (method.cv="loo") when the number of cells is important in the data.frame.
The regularized version is more appropriate when there are many missing values in the dataset (to avoid overfitting).

Value

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

Author(s)

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

References

Bro, R., Kjeldahl, K. Smilde, A. K. and Kiers, H. A. L. (2008) Cross-validation of component models: A critical look at current methods. Analytical and Bioanalytical Chemistry, 5, 1241-1251.
J. Josse, F. Husson et J. Pag<e8>s (2009) Gestion des donn<e9>es manquantes en Analyse en Composantes Principales. Journal de la SFdS. 150 (2), pp. 28-51.

Examples

## Not run: 
data(orange)
nb <- estim_ncpPCA(orange,ncp.min=0,ncp.max=4) ## Time consuming, nb = 2

## End(Not run)

Example output

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