screen.FSelector.correlation: Correlation screening algorithm

In saraemoore/SLScreenExtra: A Collection of Additional Feature Selection Algorithms and Utilities for SuperLearner

Correlation screening algorithm

Description

Features are ranked and selected based on the strength of their correlation with the outcome Y. Implemented for gaussian() family only.

Usage

screen.FSelector.correlation(
  Y,
  X,
  family,
  filter = c("linear.correlation", "rank.correlation"),
  selector = c("cutoff.biggest.diff", "cutoff.k", "cutoff.k.percent"),
  k = switch(selector, cutoff.k = ceiling(0.5 * ncol(X)), cutoff.k.percent = 0.5, NULL),
  verbose = FALSE,
  ...
)

Arguments

Y	Outcome (numeric vector). See SuperLearner for specifics.
X	Predictor variable(s) (data.frame or matrix). See SuperLearner for specifics.
family	Error distribution to be used in the model: gaussian or binomial. Currently unused. See SuperLearner for specifics.
filter	Character string. One of "linear.correlation" (Pearson) or "rank.correlation" (Spearman).
selector	A string corresponding to a subset selecting function implemented in the FSelector package. One of: cutoff.biggest.diff, cutoff.k, cutoff.k.percent, or "all". Note that "all" is a not a function but indicates pass-thru should be performed in the case of a filter which selects rather than ranks features. Default: "cutoff.biggest.diff".
k	Passed through to the selector in the case where selector is cutoff.k or cutoff.k.percent. Otherwise, should remain NULL (the default). For cutoff.k, this is an integer indicating the number of features to keep from X. For cutoff.k.percent, this is instead the proportion of features to keep.
verbose	Should debugging messages be printed? Default: FALSE.
...	Currently unused.

Value

A logical vector with length equal to ncol(X).

Examples

data(mtcars)
Y <- mtcars$mpg
X <- mtcars[,-which(colnames(mtcars)=="mpg")]
screen.FSelector.correlation(Y, X, gaussian(), filter = "rank.correlation")

# based on examples in SuperLearner package
set.seed(1)
n <- 100
p <- 20
X <- matrix(rnorm(n*p), nrow = n, ncol = p)
X <- data.frame(X)
Y <- X[, 1] + sqrt(abs(X[, 2] * X[, 3])) + X[, 2] - X[, 3] + rnorm(n)

library(SuperLearner)
sl = SuperLearner(Y, X, family = gaussian(), cvControl = list(V = 2),
                  SL.library = list(c("SL.glm", "All"),
                                    c("SL.glm", "screen.FSelector.correlation")))
sl
sl$whichScreen

saraemoore/SLScreenExtra documentation built on Nov. 4, 2023, 9:31 p.m.