DataDistribution-class: Data distributions

In kkmann/adoptr: Adaptive Optimal Two-Stage Designs in R

Data distributions

Description

DataDistribution is an abstract class used to represent the distribution of a sufficient statistic x given a sample size n and a single parameter value theta.

Arguments

x	outcome
n	sample size
theta	distribution parameter
...	further optional arguments

Details

This abstraction layer allows the representation of t-distributions (unknown variance), normal distribution (known variance), and normal approximation of a binary endpoint. Currently, the two implemented versions are Normal-class and Binomial-class.

The logical option two_armed allows to decide whether a one-arm or a two-arm (the default) design should be computed. In the case of a two-arm design all sample sizes are per group.

Slots

two_armed

Logical that indicates if a two-arm design is assumed.

Examples

normaldist   <- Normal(two_armed = FALSE)
binomialdist <- Binomial(rate_control = .25, two_armed = TRUE)

kkmann/adoptr documentation built on Feb. 3, 2024, 6:55 p.m.