R: Imputation using a decision rule under an assumption of a...

impute.mix {imp4p}

R Documentation

Imputation using a decision rule under an assumption of a mixture of MCAR and MNAR values.

Description

This function allows imputing data sets with a MCAR-devoted algorithm and a MNAR-devoted algorithm using probabilities that missing values are MCAR. If such a probability is superior to 0.5, then the MCAR-devoted algorithm is used, otherwise it is the MNAR-devoted algorithm.

Usage

impute.mix(tab, prob.MCAR, conditions, repbio=NULL, reptech=NULL, method="slsa", nknn=15,
weight=1, selec="all", ind.comp=1, progress.bar=TRUE, q=0.95)

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.
`prob.MCAR`	A matrix of probabilities that each missing value is MCAR. For instance such a matrix can be obtained from the function `prob.mcar.tab` of this package.
`conditions`	A vector of factors indicating the biological condition to which each column (experimental sample) belongs.
`repbio`	A vector of factors indicating the biological replicate to which each column belongs. Default is NULL (no experimental design is considered).
`reptech`	A vector of factors indicating the technical replicate to which each column belongs. Default is NULL (no experimental design is considered).
`method`	The method used for imputing MCAR data. If `methodi="slsa"` (default), then the SLSA algorithm is used, else the MLE algorithm is used.
`nknn`	The number of nearest neighbours used in the SLSA algorithm (see `impute.slsa`).
`weight`	The way of weighting in the algorithm (see `impute.slsa`).
`selec`	A parameter to select a part of the dataset to find nearest neighbours between rows. This can be useful for big data sets (see `impute.slsa`).
`ind.comp`	If `ind.comp=1`, only nearest neighbours without missing values are selected to fit linear models (see `impute.slsa`). Else, they can contain missing values.
`progress.bar`	If `TRUE`, a progress bar is displayed.
`q`	A quantile value (see `impute.igcda`).

Details

The missing values for which prob.MCAR is superior to 0.5 are imputed with either the function impute.slsa or the MLE algorithm (function impute.wrapper.MLE of the R package imputeLCMD). The other missing values are considered MNAR and imputed with impute.igcda.

Value

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

Author(s)

Quentin Giai Gianetto <quentin2g@yahoo.fr>

Examples


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

#Fast imputation of missing values with the impute.rand algorithm
dat.rand=impute.rand(tab=res.sim$dat.obs,conditions=res.sim$condition);

#Estimation of the mixture model
res=estim.mix(tab=res.sim$dat.obs, tab.imp=dat.rand, conditions=res.sim$condition);

#Computing probabilities to be MCAR
born=estim.bound(tab=res.sim$dat.obs,conditions=res.sim$condition);
proba=prob.mcar.tab(born$tab.lower,born$tab.upper,res);

#Imputation under the assumption of MCAR and MNAR values
tabi=impute.mix(tab=res.sim$dat.obs, prob.MCAR=proba, conditions=res.sim$conditions,
repbio=res.sim$repbio, method="slsa", nknn=15, weight=1, selec="all", ind.comp=1,
progress.bar=TRUE);

[Package imp4p version 0.7 Index]