randomForest {MetNet} | R Documentation

## Create a adjacency matrix based on random forest

### Description

`randomForest` infers an adjacency matrix using random forest using the `rfPermute` function from the `rfPermute` package. `randomForest` extracts the p-values by the function `rp.importance` and writes the presence/absence based on the significance value (α ≤q 0.05) of this connection to a matrix. The adjacency matrix is returned.

### Usage

    randomForest(x, parallel=FALSE, randomForest_adjust="none", ...)

### Arguments

`x` | matrix, where columns are the samples and the rows are features (metabolites), cell entries are intensity values
`parallel` | logical, should computation be parallelized? If `parallel=TRUE` the `bplapply` will be applied if `parallel=FALSE` the `lapply` function will be applied.
`randomForest_adjust` | character, correction method for p-values from `rp.importance`, `randomForest_adjust` will be passed to the `p.adjust` function and should be one of "holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", "none"
`...` | parameters passed to `rfPermute.default`

### Details

For use of the parameters used in the `rfPermute` function, refer to ?rfPermute::rfPermute.default.

### Value

matrix, matrix with edges inferred from random forest algorithm `rfPermute` and `rp.importance`

### Author(s)

Thomas Naake, thomasnaake@googlemail.com

### Examples

    data("x_test", package="MetNet")
    x <- x_test[, 3:dim(x_test)[2]]
    x <- as.matrix(x)
    ## Not run: randomForest(x)

[Package MetNet version 1.2.0 Index]