twosample_power {R2sample}R Documentation

Find the power of various two sample tests using Rcpp and parallel computing.

Description

Find the power of various two sample tests using Rcpp and parallel computing.

Usage

twosample_power(
  f,
  ...,
  TS,
  TSextra,
  alpha = 0.05,
  B = c(1000, 1000),
  nbins = c(50, 10),
  minexpcount = 5,
  UseLargeSample,
  samplingmethod = "independence",
  maxProcessor = 10
)

Arguments

f

function to generate a list with data sets x, y and (optional) vals, weights

...

additional arguments passed to f, up to 2

TS

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

TSextra

additional info passed to TS, if necessary

alpha

=0.05, the level of the hypothesis test

B

=c(1000, 2000), number of simulation runs for power and permutation test.

nbins

=c(50,10), number of bins for chi large and chi small.

minexpcount

=5 minimum required count for chi square tests

UseLargeSample

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

samplingmethod

=independence or MCMC in discrete data case

maxProcessor

=10, maximum number of cores to use. If maxProcessor=1 no parallel computing is used.

Value

A numeric vector of power values.

Examples

 f=function(mu) list(x=rnorm(25), y=rnorm(25, mu))
 twosample_power(f, mu=c(0,2), B=c(100, 100), maxProcessor = 1)
 f=function(n, p) list(x=table(sample(1:5, size=1000, replace=TRUE)), 
       y=table(sample(1:5, size=n, replace=TRUE, 
       prob=c(1, 1, 1, 1, p))), vals=1:5)
 twosample_power(f, n=c(1000, 2000), p=c(1, 1.5), B=c(100, 100), maxProcessor = 1)

[Package R2sample version 2.2.0 Index]