In two-sided fixed design two-sample \(z\)-tests with composite alternative prior assumed on the difference between standardized effect sizes \((\mu_2 - \mu_1)/\sigma_0\) under the alternative, this function calculates the expected log(Hajnal's ratio) at a prefixed standardized effect size for a varied range of sample sizes.
fixedHajnal.twoz_es(es = 0, es1 = 0.3, n1min = 20, n2min = 20,
n1max = 5000, n2max = 5000, sigma0 = 1,
batch1.size.increment, batch2.size.increment,
nReplicate = 50000)
Numeric. Difference between standardized effect sizes where the expected weights of evidence is desired. Default: 0
.
Positive numeric. \(\delta\) as above. Default: \(0.3\). For this, the prior on \((\mu_2 - \mu_1)/\sigma_0\) takes values \(0.3\) and \(-0.3\) each with equal probability 1/2.
Positive integer. Minimum sample size from Grpup-1 to be considered. Default: 20.
Positive integer. Minimum sample size from Grpup-2 to be considered. Default: 20.
Positive integer. Maximum sample size from Grpup-1 to be considered. Default: 5000.
Positive integer. Maximum sample size from Grpup-2 to be considered. Default: 5000.
Positive numeric. Known common standard deviation of the populations. Default: 1.
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.
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.
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 log(Hajnal's ratios) at those values.
$BF
is a matrix of dimension number of sample sizes considered
by nReplicate
. Each row contains the Hajnal's ratios at the corresponding sample size in nReplicate
replicated studies.
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].
# NOT RUN {
out = fixedHajnal.twoz_es(n1max = 100, n2max = 100)
# }
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