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.
# }