Prior and likelihood sensitivity of a Bayesian calibration model

Power-scaling sensitivity analysis with 'priorsense' (Kallioinen et al. 2024, doi:10.1007/s11222-023-10366-5 ): how much the posterior changes when the prior or the likelihood is up- or down-weighted. A prior-data conflict or a strongly informative prior shows up as sensitivity to both.

Usage

calibration_sensitivity(fit, variable = NULL, ...)

Arguments

fit

Model returned by fit_calibration() with engine = "brms".

variable

Parameters to analyse. NULL takes the population-level effects and the standard deviations and correlations of the random effects (b_, sd_, cor_), leaving out the individual random effects.

...

Further arguments for priorsense::powerscale_sensitivity().

Value

The table of priorsense::powerscale_sensitivity().