Scores: 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 ConditionalScore
expected(s, data_distribution, prior, label = NA_character_, ...)
evaluate(s, design, ...)
# S4 method for IntegralScore,TwoStageDesign
evaluate(s, design, optimization = FALSE, subdivisions = 10000L, ...)

Value

No return value. Generic description of class Score.

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.

Examples

Run this code

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