estim: Calculate the Posterior Mean and Mean Squared Error for a...

In baskwrap: Wrapper Package for Several Basket Trial R Packages

Calculate the Posterior Mean and Mean Squared Error for a Basket Trial Design

Description

Generic function for calculating the posterior mean and mean squared error of a basket trial design. It defaults to the function estim.default which does not rely on any baskwrap-specific function.

Usage

estim(design, ...)

## Default S3 method:
estim(design, ...)

## S3 method for class 'fujikawa_x'
estim(
  design,
  n,
  p1,
  lambda = NULL,
  epsilon,
  tau,
  logbase = 2,
  iter = 1000,
  weight_fun = weights_jsd,
  weight_params = list(epsilon = epsilon, tau = tau, logbase = logbase),
  globalweight_fun = NULL,
  globalweight_params = list(),
  ...
)

Arguments

design	An object created with one of the setup_ functions from the basksim package or the baskwrap package.
...	Further arguments.
n	The sample size per basket.
p1	Probabilities under the alternative hypothesis. If length(p1) == 1, then this is a common probability for all baskets.
lambda	The posterior probability threshold.
epsilon	Tuning parameter that determines the amount of borrowing. See setup_fujikawa).
tau	Tuning parameter that determines how similar the baskets have to be that information is shared. See setup_fujikawa).
logbase	Tuning parameter. The base of the logarithm that is used to calculate the Jensen-Shannon divergence.
iter	The number of iterations in the simulation. Is ignored if data is specified.
weight_fun	Which functions should be used to calculated the pairwise weights? Default is weights_jsd.
weight_params	A list of tuning parameters specific to weight_fun. By default, it takes the function arguments epsilon, tau and logbase.
globalweight_fun	Which functions should be used to calculated the global weights? Currently, this is only supported for the exact backend.
globalweight_params	A list of tuning parameters specific to globalweight_fun.

Details

estim.default is in fact just a wrapper of basksim::get_details() that select posterior mean and mean squared error.

Value

A list containing means of the posterior distribution and the mean squared errors for all baskets.

Examples

# Example for a basket trial with Fujikawa's Design
design <- setup_fujikawa_x(k = 3, p0 = 0.2)
estim(design = design, n = 20, p1 = c(0.2, 0.5, 0.5), lambda = 0.95,
     epsilon = 2, tau = 0, iter = 100)

baskwrap documentation built on March 19, 2026, 5:09 p.m.