geom_borrow.fujikawa: Plot a Fujikawa basket trial's posterior distribution after...

In basksim: Simulation-Based Calculation of Basket Trial Operating Characteristics

Plot a Fujikawa basket trial's posterior distribution after borrowing

Description

Plot a Fujikawa basket trial's posterior distribution after borrowing

Usage

## S3 method for class 'fujikawa'
geom_borrow(design, n, r, epsilon, tau, logbase, ...)

Arguments

design	An object created with one of the setup functions.
n	The sample sizes of the baskets. A vector must be used for varying sample sizes.
r	Vector of responses.
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.
...	Further arguments to be passed to 'geom_function'.

Value

A list of ggplot layers of type 'geom_function'.

Examples

# Example for a basket trial with Fujikawa's Design
design <- setup_fujikawa(k = 3, p0 = 0.2)
n <- 20
r <- c(4, 5, 2)
epsilon <- 2
tau <- 0.5
# One facet per basket
library(ggplot2)
ggplot() +
    geom_borrow(design, n, r, epsilon, tau, logbase = exp(1)) +
    facet_wrap(vars(basket))
# Colour different baskets
ggplot() +
    geom_borrow(design, n, r, epsilon, tau,
                logbase = exp(1), aes(colour = basket))

basksim documentation built on May 12, 2026, 9:08 a.m.