impute.KNN_TN,proFIAset-method {proFIA} | R Documentation

## Fill missing values in the peak table using K-nearest Neighbour for tuncated distributions.

### 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

    ## 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

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

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

[Package proFIA version 1.10.0 Index]