R: Imputing missing values using a maximum likelihood estimation...

impute.mle {imp4p} | R Documentation

## Imputing missing values using a maximum likelihood estimation (MLE).

### Description

Imputing missing values using the EM algorithm proposed in section 5.4.1 of Schafer (1997).

### Usage

    impute.mle(tab, conditions)

### 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.

### Details

See section 5.4.1 of Schafer (1997) for the theory. It is built from functions proposed in the R package norm.

### Value

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

### Author(s)

Quentin Giai Gianetto <quentin2g@yahoo.fr>

### References

Schafer, J. L. (1997). Analysis of incomplete multivariate data. Chapman and Hall/CRC.

### Examples

    #Simulating data
    res.sim=sim.data(nb.pept=2000,nb.miss=600,nb.cond=2);

    #Imputation of missing values with the slsa algorithm
    dat.mle=impute.mle(tab=res.sim$dat.obs,conditions=res.sim$condition);

[Package imp4p version 0.7 Index]