ttest_filter: t-test filter

View source: R/filters.R

ttest_filterR Documentation

t-test filter

Description

Simple univariate filter using t-test using the Rfast package for speed. Can be applied to all or a subset of predictors.

Usage

ttest_filter(
  y,
  x,
  force_vars = NULL,
  nfilter = NULL,
  p_cutoff = 0.05,
  rsq_cutoff = NULL,
  type = c("index", "names", "full")
)

Arguments

y

Response vector

x

Matrix of predictors

force_vars

Vector of column names within x which are always retained in the model (i.e. not filtered). Default NULL means all predictors will be passed to filterFUN.

nfilter

Number of predictors to return. If NULL all predictors with p-values < p_cutoff are returned.

p_cutoff

p value cut-off

rsq_cutoff

r^2 cutoff for removing predictors due to collinearity. Default NULL means no collinearity filtering. Predictors are ranked based on t-test. If 2 or more predictors are collinear, the first ranked predictor by t-test is retained, while the other collinear predictors are removed. See collinear().

type

Type of vector returned. Default "index" returns indices, "names" returns predictor names, "full" returns a matrix of p values.

Value

Integer vector of indices of filtered parameters (type = "index") or character vector of names (type = "names") of filtered parameters in order of t-test p-value. If type is "full" full output from Rfast::ttests is returned.

Examples

## sigmoid function
sigmoid <- function(x) {1 / (1 + exp(-x))}

## load iris dataset and simulate a binary outcome
data(iris)
dt <- iris[, 1:4]
colnames(dt) <- c("marker1", "marker2", "marker3", "marker4")
dt <- as.data.frame(apply(dt, 2, scale))
y2 <- sigmoid(0.5 * dt$marker1 + 2 * dt$marker2) > runif(nrow(dt))
y2 <- factor(y2, labels = c("C1", "C2"))

ttest_filter(y2, dt)  # returns index of filtered predictors
ttest_filter(y2, dt, type = "name")  # shows names of predictors
ttest_filter(y2, dt, type = "full")  # full results table


nestedcv documentation built on Dec. 5, 2022, 5:25 p.m.