Constraints {adoptr} | R Documentation |
Formulating Constraints
Description
Conceptually, constraints work very similar to scores (any score can be put in a constraint). Currently, constraints of the form 'score <=/>= x', 'x <=/>= score' and 'score <=/>= score' are admissible.
Usage
## S4 method for signature 'Constraint,TwoStageDesign'
evaluate(s, design, optimization = FALSE, ...)
## S4 method for signature 'ConditionalScore,numeric'
e1 <= e2
## S4 method for signature 'ConditionalScore,numeric'
e1 >= e2
## S4 method for signature 'numeric,ConditionalScore'
e1 <= e2
## S4 method for signature 'numeric,ConditionalScore'
e1 >= e2
## S4 method for signature 'ConditionalScore,ConditionalScore'
e1 <= e2
## S4 method for signature 'ConditionalScore,ConditionalScore'
e1 >= e2
## S4 method for signature 'UnconditionalScore,numeric'
e1 <= e2
## S4 method for signature 'UnconditionalScore,numeric'
e1 >= e2
## S4 method for signature 'numeric,UnconditionalScore'
e1 <= e2
## S4 method for signature 'numeric,UnconditionalScore'
e1 >= e2
## S4 method for signature 'UnconditionalScore,UnconditionalScore'
e1 <= e2
## S4 method for signature 'UnconditionalScore,UnconditionalScore'
e1 >= e2
Arguments
s |
|
design |
object |
optimization |
logical, if |
... |
further optional arguments |
e1 |
left hand side (score or numeric) |
e2 |
right hand side (score or numeric) |
Value
an object of class Constraint
See Also
Examples
design <- OneStageDesign(50, 1.96)
cp <- ConditionalPower(Normal(), PointMassPrior(0.4, 1))
pow <- Power(Normal(), PointMassPrior(0.4, 1))
# unconditional power constraint
constraint1 <- pow >= 0.8
evaluate(constraint1, design)
# conditional power constraint
constraint2 <- cp >= 0.7
evaluate(constraint2, design, .5)
constraint3 <- 0.7 <= cp # same as constraint2
evaluate(constraint3, design, .5)
[Package adoptr version 1.0.1 Index]