imp4p (version 1.2)

impute.PCA: Imputing missing values using Principal Components Analysis.

Description

Imputing missing values using the algorithm proposed by Josse and Husson (2013). The function is based on the imputePCA function of the R package missMDA.

Usage

impute.PCA(tab, conditions, ncp.max=5)

Arguments

tab

A data matrix containing numeric and missing values. Each column of this matrix is assumed to correspond to an experimental sample, and each row to an identified peptide.

conditions

A vector of factors indicating the biological condition to which each sample belongs.

ncp.max

integer corresponding to the maximum number of components to test (used in the estim_ncpPCA function of R package missMDA).

Value

The input matrix tab with imputed values instead of missing values.

Details

See Josse and Husson (2013) for the theory. It is built from functions proposed in the R package missMDA.

References

Josse, J & Husson, F. (2013). Handling missing values in exploratory multivariate data analysis methods. Journal de la SFdS. 153 (2), pp. 79-99.

Examples

# NOT RUN {
#Simulating data
res.sim=sim.data(nb.pept=2000,nb.miss=600,nb.cond=2);

#Imputation of missing values with PCA
dat.pca=impute.PCA(tab=res.sim$dat.obs,conditions=res.sim$condition);
# }