The optimization method minimize is based on the package
nloptr. This requires upper and lower boundaries for optimization.
Such boundaries can be computed via lower_boundary_design
respectively upper_boundary_design.
They are implemented by default in minimize.
Note that minimize allows the user to define its own
boundary designs, too.
get_lower_boundary_design(initial_design, ...)
get_upper_boundary_design(initial_design, ...)
# S4 method for OneStageDesign
get_lower_boundary_design(initial_design, n1 = 1, c1_buffer = 2, ...)
# S4 method for GroupSequentialDesign
get_lower_boundary_design(
initial_design,
n1 = 1,
n2_pivots = 1,
c1_buffer = 2,
c2_buffer = 2,
...
)
# S4 method for TwoStageDesign
get_lower_boundary_design(
initial_design,
n1 = 1,
n2_pivots = 1,
c1_buffer = 2,
c2_buffer = 2,
...
)
# S4 method for OneStageDesign
get_upper_boundary_design(
initial_design,
n1 = 5 * initial_design@n1,
c1_buffer = 2,
...
)
# S4 method for GroupSequentialDesign
get_upper_boundary_design(
initial_design,
n1 = 5 * initial_design@n1,
n2_pivots = 5 * initial_design@n2_pivots,
c1_buffer = 2,
c2_buffer = 2,
...
)
# S4 method for TwoStageDesign
get_upper_boundary_design(
initial_design,
n1 = 5 * initial_design@n1,
n2_pivots = 5 * initial_design@n2_pivots,
c1_buffer = 2,
c2_buffer = 2,
...
)The initial design
optional arguments
The values c1f and c1e from the initial design are shifted
to c1f - c1_buffer and c1e - c1_buffer in
get_lower_boundary_design, respectively, to c1f + c1_buffer and c1e + c1_buffer in
get_upper_boundary_design.
This is handled analogously with c2_pivots and c2_buffer.
bound for the first-stage sample size n1
shift of the early-stopping boundaries from the initial ones
bound for the second-stage sample size n2
shift of the final decision boundary from the initial one
initial_design <- TwoStageDesign(
n1 = 25,
c1f = 0,
c1e = 2.5,
n2 = 50,
c2 = 1.96,
order = 7L
)
get_lower_boundary_design(initial_design)
#> TwoStageDesign<n1=1;-2.0<=x1<=0.5;n2=1>