gof_power_cont {Rgof}

Find the power of various gof tests for continuous data.

Description

Find the power of various gof tests for continuous data.

Usage

gof_power_cont(
  pnull,
  rnull,
  qnull,
  ralt,
  param_alt,
  phat,
  TS,
  alpha = 0.05,
  Range = c(-Inf, Inf),
  B = c(1000, 1000),
  nbins = c(100, 10),
  rate = 0,
  maxProcessors,
  minexpcount = 2
)

Arguments

pnull

function to find cdf under null hypothesis

rnull

function to generate data under null hypothesis

qnull

quantile function (inverse cdf). If missing Wasserstein test can not be done.

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

Range

=c(-Inf, Inf) limits of possible observations, if any

B

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

nbins

=c(100,10), number of bins for chi square 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 the standard normal distribution but 
# true data comes from a normal distribution with mean different from 0.
pnull = function(x) pnorm(x)
qnull = function(x) qnorm(x)
rnull = function()  rnorm(50)
ralt = function(mu)  rnorm(50, mu)
gof_power_cont(pnull, rnull, qnull, ralt, c(0.25, 0.5), B=c(500, 500))
# Power of tests when null hypothesis specifies normal distribution and 
# mean and standard deviation are estimated from the data. 
# Example is not run because it takes several minutes.
# true data comes from a normal distribution with mean different from 0.
pnull = function(x, p=c(0, 1)) pnorm(x, p[1], ifelse(p[2]>0.001, p[2], 0.001))
qnull = function(x, p=c(0, 1)) qnorm(x, p[1],  ifelse(p[2]>0.001, p[2], 0.001))
rnull = function(p=c(0, 1))  rnorm(50, p[1], ifelse(p[2]>0.001, p[2], 0.001))
phat = function(x) c(mean(x), sd(x))
gof_power_cont(pnull, rnull, qnull, ralt, c(0, 1), phat, B=c(200, 200), maxProcessor=2)