Adds vague normal component, where the level of vagueness is controlled by
the n parameter
robustify_norm(prior, n, weights = c(0.5, 0.5))Normal distributional object
Number of theoretical participants
Vector of weights, where the first number corresponds to the informative component and the second is the vague
mixture distribution
In cases with a normal endpoint, a robust mixture prior can be created by
adding a vague normal component to any normal prior with mean \(\theta\)
and variance \(\sigma^2\).The vague component is calculated to have the
same mean \(\theta\) and variance equal to \(\sigma^2 \times n\), where
n is the specified number of theoretical participants. If robustifying a normal
power prior that was calculated from external control data and n is defined as
the number of external control participants, and the vague component would
then correspond to one external control participant's worth of data.
library(distributional)
robustify_norm(dist_normal(0,1), n = 15)
#> <distribution[1]>
#> [1] mixture(n=2)