robustify_mvnorm {beastt} | R Documentation |
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)