didec (version 0.1.0)

mfoci: Multivariate feature ordering by conditional independence.

Description

A variable selection algorithm based on the directed dependence coefficient (didec).

Usage

mfoci(
  X,
  Y,
  pre.selected = NULL,
  perm = FALSE,
  perm.method = c("decreasing"),
  autostop = TRUE
)

Value

A data.frame listing the selected variables.

Arguments

X

A numeric matrix or data.frame/data.table. Contains the predictor vector X.

Y

A numeric matrix or data.frame/data.table. Contains the response vector Y.

pre.selected

An integer vector for indexing pre-selected predictor variables from X.

perm

A logical. If True a version of didec is computed that takes into account the permutations (specified by perm.method) of the response variables.

perm.method

An optional character string specifying a method in didec for permuting the response variables. This must be one of the strings "sample", "increasing", "decreasing" (default) or "full". The version "full" is invariant with respect to permutations of the response variables.

autostop

A logical. If True the algorithm stops at the first non-increasing value of didec.

Author

Sebastian Fuchs, Jonathan Ansari, Yuping Wang

Details

mfoci is a forward feature selection algorithm for multiple-outcome data that employs the directed dependence coefficient (didec) at each step. mfoci is proved to be consistent in the sense that the subset of predictor variables selected via mfoci is sufficient with high probability.

If autostop == TRUE the algorithm stops at the first non-increasing value of didec, thereby selecting a subset of variables. Otherwise, all predictor variables are ordered according to their predictive strength measured by didec.

References

J. Ansari, S. Fuchs, A simple extension of Azadkia & Chatterjee's rank correlation to multi-response vectors, Available at https://arxiv.org/abs/2212.01621, 2024.

Examples

library(didec)
data("bioclimatic")
X <- bioclimatic[, c(9:12)]
Y <- bioclimatic[, c(1,8)]
mfoci(X, Y, pre.selected = c(1, 3))