Robustify Multivariate Normal Distributions

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

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

Value

mixture distribution

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.

Examples

library(distributional)
robustify_mvnorm(
      dist_multivariate_normal(mu = list(c(1, 0)), sigma = list(c(10, 5))),
       n = 15)
#> <distribution[1]>
#> [1] mixture(0.5*MVN[2], 0.5*MVN[2])