R2sample (version 4.0.1)

twosample_test: This function runs a number of two sample tests using Rcpp and parallel computing.

Description

This function runs a number of two sample tests using Rcpp and parallel computing.

Usage

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

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

=5000, 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.

maxProcessor

maximum number of cores to use. If missing (the default) no parallel processing is used.

UseLargeSample

should p values be found via large sample theory if n,m>10000?

samplingmethod

="independence" or "MCMC" for discrete data

rnull

a function that generates data from a model, possibly with parameter estimation.

SuppressMessages

= FALSE print informative messages?

doMethods

="all" Which methods should be included? If missing all methods are used.

Examples

 R2sample::twosample_test(rnorm(1000), rt(1000, 4), B=1000)
 myTS=function(x,y) {z=c(mean(x)-mean(y),sd(x)-sd(y));names(z)=c("M","S");z}
 R2sample::twosample_test(rnorm(1000), rt(1000, 4), TS=myTS, B=1000)
 vals=1:5
 x=table(sample(vals, size=100, replace=TRUE))
 y=table(sample(vals, size=100, replace=TRUE, prob=c(1,1,3,1,1)))
 R2sample::twosample_test(x, y, vals)