Formulating Constraints

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 Constraint,TwoStageDesign
evaluate(s, design, optimization = FALSE, ...)

# S4 method for ConditionalScore,numeric
<=(e1, e2)

# S4 method for ConditionalScore,numeric
>=(e1, e2)

# S4 method for numeric,ConditionalScore
<=(e1, e2)

# S4 method for numeric,ConditionalScore
>=(e1, e2)

# S4 method for ConditionalScore,ConditionalScore
<=(e1, e2)

# S4 method for ConditionalScore,ConditionalScore
>=(e1, e2)

# S4 method for UnconditionalScore,numeric
<=(e1, e2)

# S4 method for UnconditionalScore,numeric
>=(e1, e2)

# S4 method for numeric,UnconditionalScore
<=(e1, e2)

# S4 method for numeric,UnconditionalScore
>=(e1, e2)

# S4 method for UnconditionalScore,UnconditionalScore
<=(e1, e2)

# S4 method for UnconditionalScore,UnconditionalScore
>=(e1, e2)

Arguments

s

Score object

design

object

optimization

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

...

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

minimize

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)
#> [1] 0.2840466

# conditional power constraint
constraint2 <- cp  >= 0.7
evaluate(constraint2, design, .5)
#> [1] 0.7
constraint3 <- 0.7 <= cp # same as constraint2
evaluate(constraint3, design, .5)
#> [1] 0.7