ttest_filter: t-test filter

In nestedcv: Nested Cross-Validation with 'glmnet' and 'caret'

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"),
  keep_factors = TRUE,
  ...
)

Arguments

y	Response vector
x	Matrix or dataframe 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.
keep_factors	Logical affecting factors with 3 or more levels. Dataframes are coerced to a matrix using data.matrix. Binary factors are converted to numeric values 0/1 and analysed as such. If keep_factors is TRUE (the default), factors with 3 or more levels are not filtered and are retained. If keep_factors is FALSE, they are removed.
...	optional arguments, e.g. rsq_method: see collinear().

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 Oct. 26, 2023, 5:08 p.m.