impute_RF: Imputing missing values using Random Forest.
In imp4p: Imputation for Proteomics

Description Usage Arguments Details Value Author(s) References Examples

Imputing missing values using the algorithm proposed by Stekhoven and Buehlmann (2012). The function is based on the missForest function of the R package missForest.

impute.RF(tab, conditions,
                    maxiter = 10, ntree = 100, variablewise = FALSE,
                    decreasing = FALSE, verbose = FALSE,
                    mtry = floor(sqrt(ncol(tab))), replace = TRUE,
                    classwt = NULL, cutoff = NULL, strata = NULL,
                    sampsize = NULL, nodesize = NULL, maxnodes = NULL,
                    xtrue = NA, parallelize = c('no', 'variables', 'forests'))

`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.
`maxiter`	parameter of the `missForest` function (missForest R package).
`ntree`	parameter of the `missForest` function (missForest R package).
`variablewise`	parameter of the `missForest` function (missForest R package).
`decreasing`	parameter of the `missForest` function (missForest R package).
`verbose`	parameter of the `missForest` function (missForest R package).
`mtry`	parameter of the `missForest` function (missForest R package).
`replace`	parameter of the `missForest` function (missForest R package).
`classwt`	parameter of the `missForest` function (missForest R package).
`cutoff`	parameter of the `missForest` function (missForest R package).
`strata`	parameter of the `missForest` function (missForest R package).
`sampsize`	parameter of the `missForest` function (missForest R package).
`nodesize`	parameter of the `missForest` function (missForest R package).
`maxnodes`	parameter of the `missForest` function (missForest R package).
`xtrue`	parameter of the `missForest` function (missForest R package).
`parallelize`	parameter of the `missForest` function (missForest R package).

See Stekhoven and Buehlmann (2012) for the theory. It is built from functions proposed in the R package missForest.

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

Quentin Giai Gianetto <quentin2g@yahoo.fr>

Stekhoven, D.J. and Buehlmann, P. (2012), 'MissForest - nonparametric missing value imputation for mixed-type data', Bioinformatics, 28(1) 2012, 112-118, doi: 10.1093/bioinformatics/btr597

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

#Imputation of missing values with Random Forest
dat.rf=impute.RF(tab=res.sim$dat.obs,conditions=res.sim$condition);