ptest: Correlation Permutation Test

In meantrix/corrP: Compute Correlations Type Analysis in Parallel

Correlation Permutation Test

Description

Execute a one-sample permutation test on two numeric vectors. One vector is kept constant while the other is "shuffled" by resampling. This approximates the null hypothesis — that there is no dependency or difference between the variables.

Usage

ptest(
  x,
  y,
  FUN,
  rk = FALSE,
  alternative = c("greater", "less", "two.sided"),
  num.s = 250
)

Arguments

x	[numeric(1)] A numeric vector.
y	[numeric(1)] A numeric vector.
FUN	[function(1)] The function to be applied.
rk	[logical(1)] If TRUE, transform x and y numeric vectors with sample ranks.
alternative	[character(1)] A character string specifying the alternative hypothesis. Must be one of "greater" (default), "less", or "two.sided". You can specify just the initial letter.
num.s	[numeric(1)] The number of samples with replacement created from the y numeric vector.

Value

[numeric(1)]
The p.value statistic.

Examples

x <- iris[[1]]
y <- iris[[2]]
ptest(x, y, FUN = function(x, y) cor(x, y), alternative = "t")

meantrix/corrP documentation built on June 12, 2025, 5:33 p.m.