MS Metabolomics Data Processing Utilities

Description Usage Arguments Value References See Also Examples

Apply random forest imputation to a matrix or poplin object. This is an interface to the missForest function from the missForest package. Since the random forest is a tree-based method, it is invariant to monotonic transformations.

## S4 method for signature 'matrix'
impute_randomforest(x, ...)

## S4 method for signature 'poplin'
impute_randomforest(x, xin, xout, ...)

`x`	A matrix or poplin object.
`...`	Additional arguments passed to missForest.
`xin`	Character specifying the name of data to retrieve from `x` when `x` is a poplin object.
`xout`	Character specifying the name of data to store in `x` when `x` is a poplin object.

A matrix or poplin object of the same dimension as x containing the imputed intensities.

Daniel J. Stekhoven (2013). missForest: Nonparametric Missing Value Imputation using Random Forest. R package version 1.4.

Stekhoven D. J., & Buehlmann, P. (2012). MissForest - non-parametric missing value imputation for mixed-type data. Bioinformatics, 28(1), 112-118.

Other imputation methods: impute_knn(), impute_pca(), impute_univariate(), poplin_impute()

data(faahko_poplin)

if (requireNamespace("missForest", quietly = TRUE)) {
  ## poplin object
  impute_randomforest(faahko_poplin, xin = "raw", xout = "rf")

  ## matrix
  m <- poplin_raw(faahko_poplin, xin = "raw")
  impute_randomforest(m)
}