knn_impute {structToolbox} | R Documentation

## kNN missing value imputation

### Description

k-nearest neighbour missing value imputation replaces missing values in the data with the average of a predefined number of the most similar neighbours for which the value is present

### Usage

    knn_impute(
      neighbours = 5,
      sample_max = 50,
      feature_max = 50,
      by = "features",
      ...
    )

### Arguments

`neighbours` | (numeric) The number of neighbours (k) to use for imputation. The default is `5`.
`sample_max` | (numeric) The maximum percent missing values per sample. The default is `50`.
`feature_max` | (numeric) The maximum percent missing values per feature. The default is `50`.
`by` | (character) Impute using similar "samples" or "features". Default features. The default is `"features"`.
`...` | Additional slots and values passed to `struct_class`.

### Details

This object makes use of functionality from the following packages:

- `pmp`

### Value

A `knn_impute` object with the following `output` slots:

`imputed` | (DatasetExperiment) A DatasetExperiment object containing the data where missing values have been imputed.

### References

Jankevics A, Lloyd GR, Weber RJM (2021). pmp: Peak Matrix Processing and signal batch correction for metabolomics datasets. R package version 1.5.0.

### Examples

    M = knn_impute()

[Package structToolbox version 1.6.1 Index]