screen.wgtd.corRank: Weighted correlation screening algorithm

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

Weighted correlation screening algorithm

Description

Performs feature selection according to the ranking of weighted correlation coefficient estimates. Implemented via weightedCorr.

Usage

screen.wgtd.corRank(
  Y,
  X,
  family,
  obsWeights,
  id,
  method = "pearson",
  k = 2,
  ...
)

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.
obsWeights	Optional numeric vector of observation weights. See SuperLearner for specifics.
id	Cluster identification variable. Currently unused.
method	Which correlation coefficient to compute. Currently accepts "pearson" or "spearman".
k	Minimum number of features to select.
...	Currently unused.

Value

A logical vector with length equal to ncol(X)

Examples

# 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)
obsWeights <- 1/runif(n)
screen.wgtd.corRank(Y, X, gaussian(), obsWeights, seq(n), k = 3)

screen.wgtd.corRank3 <- function(..., k = 3){
    screen.wgtd.corRank(..., k = k)
}

library(SuperLearner)
sl = SuperLearner(Y, X, family = gaussian(), cvControl = list(V = 2),
                  obsWeights = obsWeights,
                  SL.library = list(c("SL.glm", "All"),
                                    c("SL.glm.interaction", "screen.wgtd.corRank3")))
sl
sl$whichScreen

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