opt_design {baskexact}  R Documentation 
Optimize a Basket Design
Description
Finds the optimal tuning parameters using grid search.
Usage
opt_design(design, ...)
## S4 method for signature 'OneStageBasket'
opt_design(
design,
n,
alpha,
weight_fun,
weight_params = list(),
globalweight_fun = NULL,
globalweight_params = list(),
scenarios,
prec_digits,
...
)
## S4 method for signature 'TwoStageBasket'
opt_design(
design,
n,
n1,
alpha,
interim_fun,
interim_params = list(),
weight_fun,
weight_params = list(),
globalweight_fun = NULL,
globalweight_params = list(),
scenarios,
prec_digits,
...
)
Arguments
design 
An object of class 
... 
Further arguments. 
n 
The sample size per basket. 
alpha 
The onesided signifance level. 
weight_fun 
Which function should be used to calculate the pairwise weights. 
weight_params 
A list of tuning parameters specific to

globalweight_fun 
Which function should be used to calculate the global weights. 
globalweight_params 
A list of tuning parameters specific to

scenarios 
A matrix of response rate scenarios. Each column corresponds
to a scenario and each row corresponds to a basket. A default scenario
matrix can be created with 
prec_digits 
Number of decimal places that are considered when adjusting lambda. 
n1 
The sample size per basket for the interim analysis in case of a twostage design. 
interim_fun 
Which type of interim analysis should be conducted in case of a twostage design. 
interim_params 
A list of tuning parameters specific to

Details
opt_design
finds the optimal combination of tuning parameter
values from a the set of tuning paramters that is passed to the function.
The objective function for the optimization is the mean of the expected
number of correct decisions (ECD) under the passed scenarios, with the
constraint that the type 1 error under the global null hypothesis must be
below alpha
.
Value
A matrix with the ECDs under all scenarios and the mean ECD for all combinations of tuning parameter values. The matrix is sorted decreasingly by the mean ECD.
Methods (by class)

opt_design(OneStageBasket)
: Optimize a singlestage basket design. 
opt_design(TwoStageBasket)
: Optimize a twostage basket design.
Examples
design < setupOneStageBasket(k = 3, p0 = 0.2)
opt_design(design = design, n = 10, alpha = 0.05,
weight_fun = weights_fujikawa, weight_params = list(epsilon = c(1, 2),
tau = c(0, 0.5)), scenarios = get_scenarios(design, 0.5), prec_digits = 3)