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Last data update: 2014.03.03

R: Power calculation for unconditional exact test

power.exact.test

R Documentation

Power calculation for unconditional exact test

Description

Calculates the power of the design for known sample sizes and true probabilities.

Usage

power.exact.test(p1, p2, n1, n2, npNumbers = 100, alpha = 0.05, 
                 alternative = "two.sided", interval = FALSE, beta = 0.001, 
                 method = "Z-pooled", ref.pvalue = TRUE, simulation = FALSE, 
                 nsim = 100)

Arguments

`p1`	The probability of success given in group 1
`p2`	The probability of success given in group 2
`n1`	The sample size in group 1
`n2`	The sample size in group 2
`npNumbers`	Number: The number of nuisance parameters considered
`alpha`	Significance level
`alternative`	Indicates the alternative hypothesis: must be either "less", "two.sided", or "greater"
`interval`	Logical: Indicates if a confidence interval on the nuisance parameter should be computed
`beta`	Number: Confidence level for constructing the interval of nuisance parameters considered. Only used if interval=TRUE
`method`	Indicates the method for finding tables as or more extreme than the observed table: must be either "Z-pooled", "Z-unpooled", "Santner and Snell", "Boschloo", "CSM", "CSM modified", or "CSM approximate"
`ref.pvalue`	Logical: Indicates if p-value should be refined by maximizing the p-value function after the nuisance parameter is selected
`simulation`	Logical: Indicates if the power calculation is exact or estimated by simulation
`nsim`	Number of simulations run. Only used if simulation=TRUE

Details

The power calculations are for binomial models. The design must know the fixed sample sizes in advance. There are (n_1+1) \times (n_2+1) possible tables that could be produced. There are two ways to calculate the power: simulate the tables under two independent binomial distributions or consider all possible tables and calculate the exact power. The calculations can be done for any exact.test computation or using Fisher's exact test.

Value

The function returns the computed power.

Note

The code takes a very long time for the CSM test. Not refining the p-value often yields similar results and decreases the computation time.

Author(s)

Peter Calhoun

References

Berger, R. (1994) Power comparison of exact unconditional tests for comparing two binomial proportions. Institute of Statistics Mimeo Series No. 2266

Berger, R. (1996) More powerful tests from confidence interval p values. American Statistician, 50, 314-318

Boschloo, R. D. (1970), Raised Conditional Level of Significance for the 2x2-table when Testing the Equality of Two Probabilities. Statistica Neerlandica, 24, 1-35

Examples


power.exact.test(0.20,0.80,10,20)
power.exact.test(0.20,0.80,10,20,method="Fisher")
set.seed(218461)
power.exact.test(0.20,0.80,10,20,interval=TRUE,method="Boschloo",simulation=TRUE,nsim=100)

Results


R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(Exact)
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/Exact/power.exact.test.Rd_%03d_medium.png", width=480, height=480)
> ### Name: power.exact.test
> ### Title: Power calculation for unconditional exact test
> ### Aliases: power.exact.test
> ### Keywords: Power Barnard Boschloo Unconditional Exact
> 
> ### ** Examples
> 
> 
> power.exact.test(0.20,0.80,10,20)
$power
[1] 0.9131096

$alternative
[1] "two.sided"

$method
[1] "Z-pooled"

> power.exact.test(0.20,0.80,10,20,method="Fisher")
$power
[1] 0.87467

$alternative
[1] "two.sided"

$method
[1] "Fisher"

> set.seed(218461)
> power.exact.test(0.20,0.80,10,20,interval=TRUE,method="Boschloo",simulation=TRUE,nsim=100)
$power
[1] 0.91

$alternative
[1] "two.sided"

$method
[1] "Boschloo"

> 
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>