impute_mle: Imputing missing values using a maximum likelihood estimation...
In imp4p: Imputation for Proteomics

Description Usage Arguments Details Value Author(s) References Examples

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.

1	impute.mle(tab, conditions)

`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.

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

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

Quentin Giai Gianetto <quentin2g@yahoo.fr>

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

#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);