impute_PCA: Imputing missing values using Principal Components Analysis.
In imp4p: Imputation for Proteomics

Description Usage Arguments Details Value Author(s) References Examples

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.

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

`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).

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

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

Quentin Giai Gianetto <quentin2g@yahoo.fr>

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

#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);