p_ks.test: p-value from Kolmogorov-Smirnov one- or two-sample simulation

In Spower: Power Analyses using Monte Carlo Simulations

p-value from Kolmogorov-Smirnov one- or two-sample simulation

Description

Generates one or two sets of continuous data group-level data and returns a p-value under the null that the groups were drawn from the same distribution (two sample) or from a theoretically known distribution (one sample).

Usage

p_ks.test(n, p1, p2, n2_n1 = 1, two.tailed = TRUE, parent = NULL, ...)

Arguments

n	sample size per group, assumed equal across groups
p1	a function indicating how the data were generated for group 1
p2	(optional) a function indicating how the data were generated for group 2. If omitted a one-sample test will be evaluated provided that parent is also specified
n2_n1	sample size ratio. Default uses equal sample sizes
two.tailed	logical; should a two-tailed or one-tailed test be used?
parent	the cumulative distribution function to use (e.g., pnorm). Specifying this input will construct a one-sample test setup
...	additional arguments to be passed to the parent distribution function from ks.test, as well as any other relevant parameter to ks.test (e.g., exact = TRUE)

Value

a single p-value

Author(s)

Phil Chalmers [e-mail]

See Also

gen_t.test

Examples

# two-sample test from two Gaussian distributions with different locations
p1 <- function(n) rnorm(n)
p2 <- function(n) rnorm(n, mean=-.5)
p_ks.test(n=100, p1, p2)

# one-sample data from chi-squared distribution tested
#   against a standard normal distribution
pc <- function(n, df=15) (rchisq(n, df=df) - df) / sqrt(2*df)
p_ks.test(n=100, p1=pc, parent=pnorm, mean=0, sd=1)


  # empirical power estimates
  p_ks.test(n=100, p1, p2) |> Spower()
  p_ks.test(n=100, p1=pc, parent=pnorm, mean=0, sd=1) |> Spower()

Spower documentation built on April 4, 2025, 5:11 a.m.