imputeLCMD (version 2.0)

impute.MAR: Generic function for the imputation of MAR/MCAR missing data

Description

Performs the imputation of missing data under the randomness assumption (either MAR or MCAR). The function allows treating the missing values using one of the following MAR/MCAR specific imputation methods: MLE, SVD, KNN.

Usage

impute.MAR(dataSet.mvs, model.selector, method = "MLE")

Arguments

dataSet.mvs

A data matrix containing missing values.

model.selector

Binary vector; "1" indicates rows that should be treated using MAR/MCAR specific methods, while "0" indicates rows treated using MNAR specific methods (the missing values corresponding to these rows are assumed to be left-censored).

method

The method employed for the imputation of MAR/MCAR missing data.

Value

A data matrix containing only MNAR (assumed to be left-censored) missing data.

See Also

model.Selector, imp.norm, pca, impute.knn

Examples

# NOT RUN {
# generate expression data matrix
exprsDataObj = generate.ExpressionData(nSamples1 = 6, nSamples2 = 6,
                          meanSamples = 0, sdSamples = 0.2,
                          nFeatures = 1000, nFeaturesUp = 50, nFeaturesDown = 50,
                          meanDynRange = 20, sdDynRange = 1,
                          meanDiffAbund = 1, sdDiffAbund = 0.2)
exprsData = exprsDataObj[[1]]
  
# insert 15% missing data with 100% missing not at random
m.THR = quantile(exprsData, probs = 0.15)
sd.THR = 0.1
MNAR.rate = 100
exprsData.MD.obj = insertMVs(exprsData,m.THR,sd.THR,MNAR.rate)
exprsData.MD = exprsData.MD.obj[[2]]

# run model.Selector
m.s = model.Selector(exprsData.MD)

# perform MAR/MCAR imputation
exprsData.MAR.imputed = impute.MAR (exprsData.MD, m.s, method = "MLE")

## The function is currently defined as
function (dataSet.mvs, model.selector, method = "MLE") 
{
    if (length(which(model.selector[[1]] == 1)) == 0) {
        dataSet.imputed = dataSet.mvs
    }
    else {
        dataSet.MCAR = dataSet.mvs[which(model.selector[[1]] == 
            1), ]
        switch(method, MLE = {
            dataSet.MCAR.imputed = impute.wrapper.MLE(dataSet.MCAR)
        }, SVD = {
            dataSet.MCAR.imputed = impute.wrapper.SVD(dataSet.MCAR, 
                K = 2)
        }, KNN = {
            dataSet.MCAR.imputed = impute.wrapper.KNN(dataSet.MCAR, 
                K = 15)
        })
        dataSet.imputed = dataSet.mvs
        dataSet.imputed[which(model.selector[[1]] == 1), ] = dataSet.MCAR.imputed
    }
    return(dataSet.imputed)
  }
# }