estim {baskexact}  R Documentation 
Posterior Mean and Mean Squared Error
Description
Computes the posterior mean and the mean squared error of a basket trial design.
Usage
estim(design, ...)
## S4 method for signature 'OneStageBasket'
estim(
design,
p1,
n,
lambda = NULL,
weight_fun,
weight_params = list(),
globalweight_fun = NULL,
globalweight_params = list(),
...
)
## S4 method for signature 'TwoStageBasket'
estim(
design,
p1,
n,
n1,
lambda = NULL,
interim_fun,
interim_params = list(),
weight_fun,
weight_params = list(),
globalweight_fun = NULL,
globalweight_params = list(),
...
)
Arguments
design 
An object of class 
... 
Further arguments. 
p1 
Probabilities under the alternative hypothesis. If

n 
The sample size per basket. 
lambda 
The posterior probability threshold. See details for more information. 
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

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

Value
A list containing means of the posterior distributions and the mean squared errors for all baskets.
Methods (by class)

estim(OneStageBasket)
: Posterior mean and mean squared error for a singlestage basket design. 
estim(TwoStageBasket)
: Posterior mean and mean squared error for a twostage basket design.
Examples
design < setupOneStageBasket(k = 3, p0 = 0.2)
estim(design = design, p1 = c(0.2, 0.2, 0.5), n = 15,
weight_fun = weights_fujikawa)