imp4p: impute_mle – R documentation

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impute_mle

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). The function is based on the imp.norm function of the R package norm.

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 mle algorithm
dat.mle=impute.mle(tab=res.sim$dat.obs,conditions=res.sim$condition);

imp4p

Imputation for Proteomics

v1.1

GPL-3

Authors

Quentin Giai Gianetto

Initial release