Description Usage Arguments Details See Also Examples
In adoptr
scores are used to assess the performance of a design.
This can be done either conditionally on the observed stageone outcome
or unconditionally.
Consequently, score objects are either of class ConditionalScore
or
UnconditionalScore
.
1 2 3 4 5 6 7 8 9  expected(s, data_distribution, prior, ...)
## S4 method for signature 'ConditionalScore'
expected(s, data_distribution, prior, label = NA_character_, ...)
evaluate(s, design, ...)
## S4 method for signature 'IntegralScore,TwoStageDesign'
evaluate(s, design, optimization = FALSE, subdivisions = 10000L, ...)

s 

data_distribution 

prior 
a 
... 
further optional arguments 
label 
object label (string) 
design 
object 
optimization 
logical, if 
subdivisions 
maximal number of subdivisions when evaluating an integral score using adaptive quadrature (optimization = FALSE) 
All scores can be evaluated on a design using the evaluate
method.
Note that evaluate
requires a third argument x1
for
conditional scores (observed stageone outcome).
Any ConditionalScore
can be converted to a UnconditionalScore
by forming its expected value using expected
.
The returned unconditional score is of class IntegralScore
.
ConditionalPower
, ConditionalSampleSize
,
composite
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19  design < TwoStageDesign(
n1 = 25,
c1f = 0,
c1e = 2.5,
n2 = 50,
c2 = 1.96,
order = 7L
)
prior < PointMassPrior(.3, 1)
# conditional
cp < ConditionalPower(Normal(), prior)
expected(cp, Normal(), prior)
evaluate(cp, design, x1 = .5)
# unconditional
power < Power(Normal(), prior)
evaluate(power, design)
evaluate(power, design, optimization = TRUE) # use nonadaptive quadrature

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