impute.KNN_TN-proFIAset-method: Fill missing values in the peak table using K-nearest...

Description Usage Arguments Value References Examples

Description

Impute the missing values in an FIA experiment using a Weighted K-Nearest Neighbours on Truncated Distribution described by Jasmit S. Shah et al.

Usage

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## S4 method for signature 'proFIAset'
impute.KNN_TN(object, k = 0.6, classes = c("split", "unique"))

Arguments

object

A proFIAset object.

k

The number of neighbors considered, can be a fraction then in this case the k will be taken for each class as the frac of the effective of the class. 3 at minima because comparison is based on correlation.

classes

how to handle imputation for different classes, if 'split', the classes are taken separately, if 'unique', the imputation is done on the full data matrix.

Value

A proFIAset object with the missing values imputated.

References

Distribution based nearest neighbor imputation for truncated high dimensional data with applications to pre-clinical and clinical metabolomics studies, J.S Shah 2017, BMC Bioinformatics.

Examples

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

    ###Reinitializing the data matrix
    plasSet<-makeDataMatrix(plasSet,maxo=FALSE)
    plasSet<-impute.KNN_TN(plasSet,2)
}

proFIA documentation built on March 20, 2021, 6 p.m.