fixedNAP.onez_es {NAP} | R Documentation |
Fixed-design one-sample z
-tests with NAP for varied sample sizes
Description
In two-sided fixed design one-sample z
-tests with normal moment prior assumed on the standardized effect size \mu/\sigma_0
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.
Usage
fixedNAP.onez_es(es = 0, nmin = 20, nmax = 5000,
tau.NAP = 0.3/sqrt(2), sigma0 = 1,
batch.size.increment, nReplicate = 50000)
Arguments
es |
Numeric. Standardized effect size where the expected weights of evidence is desired. Default: |
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: |
sigma0 |
Positive numeric. Known standard deviation in the population. Default: 1. |
batch.size.increment |
function. Increment in sample size. The sequence of sample size thus considered for the fixed design test is from |
nReplicate |
Positve integer. Number of replicated studies based on which the expected weights of evidence is calculated. Default: 50,000. |
Value
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.
Author(s)
Sandipan Pramanik and Valen E. Johnson
References
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]
Examples
out = fixedNAP.onez_es(nmax = 100)