ConditionalSampleSize-class | R Documentation |
This score simply evaluates n(d, x1)
for a design d
and the
first-stage outcome x1
.
The data distribution and prior are only relevant when it is integrated.
ConditionalSampleSize(label = "n(x1)")
ExpectedSampleSize(dist, prior, label = "E[n(x1)]")
## S4 method for signature 'ConditionalSampleSize,TwoStageDesign'
evaluate(s, design, x1, optimization = FALSE, ...)
label |
object label (string) |
dist |
a univariate |
prior |
a |
s |
|
design |
object |
x1 |
stage-one test statistic |
optimization |
logical, if |
... |
further optional arguments |
Scores
design <- TwoStageDesign(50, .0, 2.0, 50, 2.0, order = 5L)
prior <- PointMassPrior(.4, 1)
css <- ConditionalSampleSize()
evaluate(css, design, c(0, .5, 3))
ess <- ExpectedSampleSize(Normal(), prior)
# those two are equivalent
evaluate(ess, design)
evaluate(expected(css, Normal(), prior), design)
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