fixedNAP.twoz_es: Fixed-design two-sample z-tests with NAP for varied sample...
In NAP: Non-Local Alternative Priors in Psychology

Description Usage Arguments Details Value Author(s) References Examples

In two-sided fixed design two-sample z-tests with normal moment prior assumed on the difference between standardized effect sizes (μ_2 - μ_1)/σ_0 under the alternative, this function calculates the expected weights of evidence (that is, expected log(Bayes Factor)) of the test at a prefixed differences between standardized effect size for a varied range of sample sizes.

fixedNAP.twoz_es(es = 0, n1min = 20, n2min = 20, 
                 n1max = 5000, n2max = 5000, 
                 tau.NAP = 0.3/sqrt(2), sigma0 = 1, 
                 batch1.size.increment, batch2.size.increment, 
                 nReplicate = 50000)

`es`	Numeric. Difference between standardized effect sizes where the expected weights of evidence is desired. Default: `0`.
`n1min`	Positive integer. Minimum sample size from Grpup-1 to be considered. Default: 20.
`n2min`	Positive integer. Minimum sample size from Grpup-2 to be considered. Default: 20.
`n1max`	Positive integer. Maximum sample size from Grpup-1 to be considered. Default: 5000.
`n2max`	Positive integer. Maximum sample size from Grpup-2 to be considered. Default: 5000.
`tau.NAP`	Positive numeric. Parameter in the moment prior. Default: 0.3/√2. This places the prior modes of (μ_2 - μ_1)/σ_0 at 0.3 and -0.3.
`sigma0`	Positive numeric. Known common standard deviation of the populations. Default: 1.
`batch1.size.increment`	Positive numeric. Increment in sample size from Group-1. The sequence of sample size thus considered from Group-1 for the fixed design test is from `n1min` to `n1max` with an increment of `batch1.size.increment`. Default: `function(narg){20}`. This means an increment of 20 samples from Group-1 at each step.
`batch2.size.increment`	Positive numeric. Increment in sample size from Group-2. The sequence of sample size thus considered from Group-2 for the fixed design test is from `n2min` to `n2max` with an increment of `batch2.size.increment`. Default: `function(narg){20}`. This means an increment of 20 samples from Group-2 at each step.
`nReplicate`	Positve integer. Number of replicated studies based on which the expected weights of evidence is calculated. Default: 50,000.

n1min, n1max, batch1.size.increment, and n2min, n2max, batch2.size.increment should be chosen such that the length of sample sizes considered from Group 1 and 2 are equal.

A list with two components named summary and BF.

$summary is a data frame with columns n1 containing the sample sizes from Group-1, n2 containing the sample sizes from Group-2, and avg.logBF containing the expected weight of evidence values at those values.

$BF is a matrix of dimension number of sample sizes considered by nReplicate. Each row contains the Bayes factor values at the corresponding sample size in nReplicate replicated studies.

Sandipan Pramanik and Valen E. Johnson

Pramanik, S. and Johnson, V. (2022). Efficient Alternatives for Bayesian Hypothesis Tests in Psychology. Psychological Methods. Just accepted.

Johnson, V. and Rossell, R. (2010). On the use of non-local prior densities in Bayesian hypothesis tests. Journal of the Royal Statistical Society: Series B, 72:143-170. [Article]