R: Imputing missing values using Principal Components Analysis.

impute.PCA {imp4p} | R Documentation

## 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).

### Details

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

### Value

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

### Author(s)

Quentin Giai Gianetto <quentin2g@yahoo.fr>

### 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

    #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);

[Package imp4p version 1.2 Index]