gof_power_disc {Rgof}

Find the power of various gof tests for discrete data.

Description

Find the power of various gof tests for discrete data.

Usage

gof_power_disc(
  pnull,
  rnull,
  vals,
  ralt,
  param_alt,
  phat,
  TS,
  alpha = 0.05,
  B = c(1000, 1000),
  nbins = c(100, 10),
  rate = 0,
  maxProcessors,
  minexpcount = 2
)

Arguments

pnull

cumulative distribution function under the null hypothesis

rnull

a function to generate data under null hypothesis

vals

values of discrete rv.

ralt

function to generate data under alternative hypothesis

param_alt

vector of parameter values for distribution under alternative hypothesis

phat

function to estimate parameters from the data

TS

user supplied function to find test statistics

alpha

=0.05, the level of the hypothesis test

B

=c(1000, 1000), number of simulation runs to find power and null distribution

nbins

=c(100,10), number of bins for chisquare tests.

rate

=0 rate of Poisson if sample size is random, 0 if sample size is fixed

maxProcessors

maximum of number of processors to use, 1 if no parallel processing is needed or number of cores-1 if missing

minexpcount

=2 minimal expected bin count required

Value

A numeric matrix of power values.

Examples

# Power of tests when null hypothesis specifies a binomial N=10, p=0.5 distribution but 
# true data comes from a binomial distribution with success probability 0.55 or 0.6.
vals=0:10
pnull = function() pbinom(0:10, 10, 0.5)
rnull = function() table(c(0:10, rbinom(1000, 10, 0.5)))-1
ralt = function(p) table(c(0:10, rbinom(1000, 10, p)))-1
gof_power_disc(pnull, rnull, vals, ralt, c(0.515, 0.53), B=c(500, 500))
# Power of tests when null hypothesis specifies a binomial N=10 distribution and
# p is estimated from the data. 
pnull=function(p=0.5)  pbinom(0:10, 10, p)
rnull = function(p=0.5) table(c(0:10, rbinom(1000, 10, p)))-1
ralt = function(p=0.5) table(c(0:10, rbinom(1000, 10, p)))-1
phat = function(x) mean(rep(0:10, x))/10
gof_power_disc(pnull, rnull, vals, ralt, phat, param_alt=0.6, B=c(100, 100), maxProcessors = 2)