Scores {adoptr}

Scores

Description

In adoptr scores are used to assess the performance of a design. This can be done either conditionally on the observed stage-one outcome or unconditionally. Consequently, score objects are either of class ConditionalScore or UnconditionalScore.

Usage

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, ...)

Arguments

s

Score object

data_distribution

DataDistribution object

prior

a Prior object

...

further optional arguments

label

object label (string)

design

object

optimization

logical, if TRUE uses a relaxation to real parameters of the underlying design; used for smooth optimization.

subdivisions

maximal number of subdivisions when evaluating an integral score using adaptive quadrature (optimization = FALSE)

Details

All scores can be evaluated on a design using the evaluate method. Note that evaluate requires a third argument x1 for conditional scores (observed stage-one outcome). Any ConditionalScore can be converted to a UnconditionalScore by forming its expected value using expected. The returned unconditional score is of class IntegralScore.

Value

No return value. Generic description of class Score.

See Also

ConditionalPower, ConditionalSampleSize, composite

Examples

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 non-adaptive quadrature