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.

# 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)

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