| robustify_norm {beastt} | R Documentation |
Robustify Normal Distributions
Description
Adds vague normal component, where the level of vagueness is controlled by
the n parameter
Usage
robustify_norm(prior, n, weights = c(0.5, 0.5))
Arguments
prior |
Normal distributional object |
n |
Number of theoretical participants |
weights |
Vector of weights, where the first number corresponds to the informative component and the second is the vague |
Details
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.
Value
mixture distribution
Examples
library(distributional)
robustify_norm(dist_normal(0,1), n = 15)