robustify_mvnorm: Robustify Multivariate Normal Distributions

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Robustify Multivariate Normal Distributions

Description

Adds vague normal component, where the level of vagueness is controlled by the n parameter

Usage

robustify_mvnorm(prior, n, weights = c(0.5, 0.5))

Arguments

prior	Multivariate Normal distributional object
n	Number of theoretical participants (or events, for time-to-event data)
weights	Vector of weights, where the first number corresponds to the informative component and the second is the vague

Details

In cases with a time-to-event endpoint, a robust mixture prior can be created by adding a vague multivariate normal component to any multivariate normal prior with mean vector \boldsymbol{\mu} and covariance matrix \boldsymbol{\Sigma}. The vague component is calculated to have the same mean vector \boldsymbol{\mu} and covariance matrix equal to \boldsymbol{\Sigma} \times n, where n is the specified number of theoretical events.

Value

mixture distribution

Examples

library(distributional)
robustify_mvnorm(
      dist_multivariate_normal(mu = list(c(1, 0)), sigma = list(c(10, 5))),
       n = 15)

beastt documentation built on June 8, 2025, 11:42 a.m.