## 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 Basket created by setupOneStageBasket or setupTwoStageBasket. ... Further arguments. p1 Probabilities under the alternative hypothesis. If length(p1) == 1, then this is a common probability for all baskets. 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 weight_fun. globalweight_fun Which function should be used to calculate the global weights. globalweight_params A list of tuning parameters specific to globalweight_fun. n1 The sample size per basket for the interim analysis in case of a two-stage design. interim_fun Which type of interim analysis should be conducted in case of a two-stage design. interim_params A list of tuning parameters specific to interim_fun.

### 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 single-stage basket design.

• estim(TwoStageBasket): Posterior mean and mean squared error for a two-stage 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)