screen.FSelector.chi.squared: Chi-squared test screening algorithm

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

Chi-squared test screening algorithm

Description

Cramer's V, derived from Pearson's chi-squared statistic, is used to find columns of X which are associated with Y. Implemented for binomial() family only and designed to be used with binary or categorical X. Continuous X will be discretized by FSelector and Discretize using the MDL method (Fayyad & Irani, 1993).

Usage

screen.FSelector.chi.squared(
  Y,
  X,
  family,
  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.
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).

References

http://hdl.handle.net/2014/35171

Examples

data(iris)
Y <- as.numeric(iris$Species=="setosa")
X <- iris[,-which(colnames(iris)=="Species")]
screen.FSelector.chi.squared(Y, X, binomial(), selector = "cutoff.k.percent", k = 0.5)

# based on example in SuperLearner package
set.seed(1)
n <- 100
p <- 20
X <- matrix(rnorm(n*p), nrow = n, ncol = p)
X <- data.frame(X)
Y <- rbinom(n, 1, plogis(.2*X[, 1] + .1*X[, 2] - .2*X[, 3] + .1*X[, 3]*X[, 4] - .2*abs(X[, 4])))

library(SuperLearner)
sl = SuperLearner(Y, X, family = binomial(), cvControl = list(V = 2),
                  SL.library = list(c("SL.lm", "All"),
                                    c("SL.lm", "screen.FSelector.chi.squared")))
sl
sl$whichScreen

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