Scores

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.

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, ...)

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.

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)
#> E[Pr[x2>=c2(x1)|x1]]<Normal<two-armed>;PointMass<0.30>> 
evaluate(cp, design, x1 = .5)
#> [1] 0.3227581

# unconditional
power <- Power(Normal(), prior)
evaluate(power, design)
#> [1] 0.3269562
evaluate(power, design, optimization = TRUE) # use non-adaptive quadrature
#> [1] 0.3269562