impute.FIA-proFIAset-method: Fill missing values using the provided method.
In adelabriere/proFIA: Preprocessing of FIA-HRMS data

Description Usage Arguments Examples

Impute the missing values in an FIA experiment using a Weighted K-Nearest Neighbours on Truncated Distribution implemented in impute.KNN_TN or by random forest using the impute.randomForest function.

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## S4 method for signature 'proFIAset'
impute.FIA(object, method = c("KNN_TN", "randomForest"),
  ...)

`object`	A proFIAset object.
`method`	The method to be used for missing value imputation.
`...`	Arguments furnished to the imputation method. No argument is required for `impute.randomForest` and the number of neighbours should be furnished using the `impute.KNN_TN` function.

if(require(plasFIA)){
    data(plasSet)

    ###Reinitializing the data matrix an using KNN
    plasSet<-makeDataMatrix(plasSet,maxo=FALSE)
    plasSet<-impute.FIA(plasSet,method="KNN_TN",k=2)
    
    ###Reinitializing the data matrix and using randomForest
    plasSet<-makeDataMatrix(plasSet,maxo=FALSE)
    plasSet<-impute.FIA(plasSet,method="randomForest")
}