imputeLCMD (version 2.0)

impute.wrapper.SVD: SVD-based imputation.

Description

This is a wrapper function that performs SVD-based imputation of missing data. The wrapper is built around the pca function from the pcaMethods package.

Usage

impute.wrapper.SVD(dataSet.mvs, K)

Arguments

dataSet.mvs

A data matrix containing left-censored missing data.

K

The number of PCs used.

Value

A complete expression data matrix with missing values imputed.

References

See package pcaMethods

See Also

pca, impute.wrapper.KNN, impute.wrapper.MLE

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]]

# perform missing data imputation
exprsData.imputed = impute.wrapper.SVD(exprsData.MD,2)

# }
# NOT RUN {
hist(exprsData[,1])
hist(exprsData.MD[,1])
hist(exprsData.imputed[,1])
# }
# NOT RUN {
## The function is currently defined as
function (dataSet.mvs, K) 
{
    resultSVD = pca(dataSet.mvs, method = "svdImpute", nPcs = K)
    dataSet.imputed = resultSVD@completeObs
    return(dataSet.imputed)
  }
# }