fixedNAP.onet_es: Fixed-design one-sample t-tests with NAP for varied sample...
In NAP: Non-Local Alternative Priors in Psychology

Description Usage Arguments Value Author(s) References Examples

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

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fixedNAP.onet_es(es = 0, nmin = 20, nmax = 5000, 
                 tau.NAP = 0.3/sqrt(2),
                 batch.size.increment, nReplicate = 50000)

`es`	Numeric. Standardized effect size where the expected weights of evidence is desired. Default: `0`.
`nmin`	Positive integer. Minimum sample size to be considered. Default: 20.
`nmax`	Positive integer. Maximum sample size to be considered. Default: 5000.
`tau.NAP`	Positive numeric. Parameter in the moment prior. Default: 0.3/√2. This places the prior modes of the standardized effect size μ/σ at 0.3 and -0.3.
`batch.size.increment`	Positive numeric. Increment in sample size. The sequence of sample size thus considered for the fixed design test is from `nmin` to `nmax` with an increment of `batch.size.increment`. Default: `function(narg){20}`. This means an increment of 20 samples at each step.
`nReplicate`	Positve integer. Number of replicated studies based on which the expected weights of evidence is calculated. Default: 50,000.

A list with two components named summary and BF.

$summary is a data frame with columns n containing the values of sample sizes 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]