impute.randomForest-proFIAset-method: Fill missing values in the peak table using random forest.
In proFIA: Preprocessing of FIA-HRMS data

Description Usage Arguments Value References Examples

Impute the missing values in an FIA experiment using a random forest implemented in the missForest package.

1 2	## S4 method for signature 'proFIAset' impute.randomForest(object, parallel = FALSE, ...)

`object`	A proFIAset object.
`parallel`	Shall parallelism be used.
`...`	supplementary arguements to be passed to missForest function.

A proFIAset object with the missing values imputated.

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

if(require(plasFIA)){
    data(plasSet)
    ###Reinitializing the data matrix
    plasSet<-makeDataMatrix(plasSet,maxo=FALSE)
    plasSet<-impute.randomForest(plasSet)
}