R2sample (version 4.0.1)

twosample_test_adjusted_pvalue: This function runs a number of two sample tests using Rcpp and parallel computing and then finds the correct p value for the combined tests.

Description

This function runs a number of two sample tests using Rcpp and parallel computing and then finds the correct p value for the combined tests.

Usage

twosample_test_adjusted_pvalue(
  x,
  y,
  vals = NA,
  TS,
  TSextra,
  wx = rep(1, length(x)),
  wy = rep(1, length(y)),
  B = c(5000, 1000),
  nbins = c(50, 10),
  minexpcount = 5,
  samplingmethod = "independence",
  rnull,
  SuppressMessages = FALSE,
  doMethods
)

Value

A list of two numeric vectors, the test statistics and the p values.

Arguments

x

a vector of numbers if data is continuous or of counts if data is discrete, or a list with the data.

y

a vector of numbers if data is continuous or of counts if data is discrete.

vals

=NA, a vector of numbers, the values of a discrete random variable. NA if data is continuous data.

TS

routine to calculate test statistics for non-chi-square tests

TSextra

additional info passed to TS, if necessary

wx

A numeric vector of weights of x.

wy

A numeric vector of weights of y.

B

=c(5000, 1000), number of simulation runs for permutation test

nbins

=c(50,10), number of bins for chi square tests.

minexpcount

= 5, minimum required expected counts for chi-square tests

samplingmethod

="independence" or "MCMC" for discrete data

rnull

routine for parametric bootstrap

SuppressMessages

= FALSE print informative messages?

doMethods

Which methods should be included?

Examples

 x=rnorm(100)
 y=rt(200, 4)
 R2sample::twosample_test_adjusted_pvalue(x, y, B=c(500, 500))
 vals=1:5
 x=table(c(1:5, sample(1:5, size=100, replace=TRUE)))-1
 y=table(c(1:5, sample(1:5, size=100, replace=TRUE, prob=c(1,1,3,1,1))))-1
 R2sample::twosample_test_adjusted_pvalue(x, y, vals, B=c(500, 500))