ConditionalPower-class: (Conditional) Power of a Design

In adoptr: Adaptive Optimal Two-Stage Designs in R

Description

This score evaluates P[X₂ > c2(design, X₁) | X₁ = x₁]. Note that the distribution of X₂ is the posterior predictive after observing X₁ = x₁.

Usage

ConditionalPower(dist, prior, label = "Pr[x2>=c2(x1)|x1]")

Power(dist, prior, label = "Pr[x2>=c2(x1)]")

## S4 method for signature 'ConditionalPower,TwoStageDesign'
evaluate(s, design, x1, optimization = FALSE, ...)

Arguments

dist	a univariate distribution object
prior	a Prior object
label	object label (string)
s	Score object
design	object
x1	stage-one test statistic
optimization	logical, if TRUE uses a relaxation to real parameters of the underlying design; used for smooth optimization.
...	further optional arguments

See Also

Scores

Examples

prior <- PointMassPrior(.4, 1)
cp <- ConditionalPower(Normal(), prior)
evaluate(
   cp,
   TwoStageDesign(50, .0, 2.0, 50, 2.0, order = 5L),
   x1 = 1
)
# these two are equivalent:
expected(cp, Normal(), prior)
Power(Normal(), prior)

adoptr documentation built on June 28, 2021, 5:11 p.m.