NAP (version 1.1)

fixedHajnal.onet_n: Fixed-design one-sample \(t\)-tests using Hajnal's ratio and a pre-fixed sample size

Description

In two-sided fixed design one-sample \(t\)-tests with composite alternative prior assumed on the standardized effect size \(\mu/\sigma\) under the alternative and a prefixed sample size, this function calculates the expected log(Hajnal's ratio) at a varied range of standardized effect sizes.

Usage

fixedHajnal.onet_n(es1 = 0.3, es = c(0, 0.2, 0.3, 0.5), 
                   n.fixed = 20, 
                   nReplicate = 50000, nCore)

Arguments

es1

Positive numeric. Default: \(0.3\). For this, the composite alternative prior on the standardized effect size \(\mu/\sigma\) takes values \(0.3\) and \(-0.3\) each with equal probability 1/2.

es

Numeric vector. Standardized effect sizes \(\mu/\sigma\) where the expected weights of evidence is desired. Default: c(0, 0.2, 0.3, 0.5).

n.fixed

Positive integer. Prefixed sample size. Default: 20.

nReplicate

Positve integer. Number of replicated studies based on which the expected weights of evidence is calculated. Default: 50,000.

nCore

Positive integer. Default: One less than the total number of available cores.

Value

A list with two components named summary and BF.

$summary is a data frame with columns effect.size containing the values in es and avg.logBF containing the expected log(Hajnal's ratios) at those values.

$BF is a matrix of dimension length(es) by nReplicate. Each row contains the Hajnal's ratios at the corresponding standardized effec size in nReplicate replicated studies.

References

Hajnal, J. (1961). A two-sample sequential t-test.Biometrika, 48:65-75, [Article].

Schnuerch, M. and Erdfelder, E. (2020). A two-sample sequential t-test.Biometrika, 48:65-75, [Article].

Examples

# NOT RUN {
out = fixedHajnal.onet_n(n.fixed = 20, es = c(0, 0.3), nCore = 1)
# }