PomaRandForest {POMA} | R Documentation

## Classification Random Forest for Mass Spectrometry Data

### Description

PomaRandForest() allows users to perform a classification Random Forest with a MS data matrix using the classical `randomForest` R package.

### Usage

    PomaRandForest(
      data,
      ntest = 20,
      ntree = 500,
      mtry = floor(sqrt(ncol(t(Biobase::exprs(data))))),
      nodesize = 1,
      nvar = 20
    )

### Arguments

`data` | A MSnSet object. First `pData` column must be the subject group/type.
`ntest` | Numeric indicating the percentage of observations that will be used as test set. Default is 20% of observations.
`ntree` | Number of trees to grow.
`mtry` | Number of variables randomly sampled as candidates at each split. This value is set sqrt(p) (where p is number of variables in data) by default.
`nodesize` | Minimum size of terminal nodes. By default is equal to 1.
`nvar` | Number of variables to show in the Gini plot.

### Value

A list with all results for Random Forest including plots and data frames.

### Author(s)

Pol Castellano-Escuder

### References

A. Liaw and M. Wiener (2002). Classification and Regression by randomForest. R News 2(3), 18–22.

### Examples

    data("st000336")

    st000336 %>%
      PomaImpute() %>%
      PomaRandForest()

[Package POMA version 1.0.0 Index]