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.- ...
Additional arguments.
Examples
x <- iris[[1]]
y <- iris[[2]]
ptest(x, y, FUN = function(x, y) cor(x, y), alternative = "t")
#> [1] 0.16