Mutual Forest Impact shortcut function (recommended).

This method corrects the mean adjusted agreement by a permutation approach and generates the relation parameter mutual forest impact. Subsequently p-values are determined and related variables are selected.

Usage

MFI(variables, candidates, num.threads = 1, ...)

Arguments

variables

Vector of variable names for **which related variables should be searched**.

candidates

Vector of variable names that **are candidates to be related to the variables**.

num.threads

Number of threads to parallelize with. (Default: 1)

...

Arguments passed on to RandomForestSurrogates

x,y

Predictor data and dependent variables.

s.pct,s

Number of surrogate splits. This can be defined either by setting `s.pct` to a number between 0 and 1, or providing an exact value for `s`. - `s.pct`: Percentage of variables to use for `s`. (Default: 0.01) - `s`: Number of surrogate splits. (Default: Number of variables multiplied by `s.pct`, which defaults to 0.01; If `s.pct` is less than or equal to zero, or greater than 1: 0.01 is used instead.)

mtry

Number of variables to possibly split at in each node. Default is the (rounded down) number of variables to the power of three quarters (Ishwaran, 2011). Alternatively, a single argument function returning an integer, given the number of independent variables.

type

The type of random forest to create with ranger. One of `"regression"` (Default), `"classification"` or `"survival"`.

status

If `type = "regression"`: Survival forest status variable. Use 1 for event and 0 for censoring. Length must match `y`.

min.node.size

Minimal node size to split at. (Default: 1)

permutate

Enable to permutate `x` for [MutualForestImpact()] (Default: FALSE).

seed

RNG seed. It is strongly recommended that you set this value.

preschedule.threads

(Default: TRUE) Passed as `mc.preschedule` to [parallel::mclapply()] in [addSurrogates()].

num.trees

Number of trees.

Value

A [MutualForestImpact()] list object. * `REL`: The [MeanAdjustedAgreement()] object. * `PERM`: The permutated [MeanAdjustedAgreement()] object. * `relations`: Matrix of determined relations (rows: investigated variables, columns: candidate variables).

See also

[MutualForestImpact()]

Examples

# \donttest{
data("SMD_example_data")
mfi <- MFI(
  x = SMD_example_data[, -1], y = SMD_example_data[, 1],
  s = 10, num.trees = 50, num.threads = 1,
  variables = c("X7", "X1"), candidates = colnames(SMD_example_data)[2:101]
)
#> Warning: `seed` was not set. Your results may not be reproducible.
#> Warning: `seed` was not set. Your results may not be reproducible.
# }