impute.KNN_TN-proFIAset-method: Fill missing values in the peak table using K-nearest...
In proFIA: Preprocessing of FIA-HRMS data

Description Usage Arguments Value References Examples

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

1 2	## S4 method for signature 'proFIAset' impute.KNN_TN(object, k = 0.6, classes = c("split", "unique"))

`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.

A proFIAset object with the missing values imputated.

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.

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

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