Computes the sensitivity to priors specification. This represents the proportion of change in some indices when the model is fitted with an antagonistic prior (a prior of same shape located on the opposite of the effect).
sensitivity_to_prior(model, ...)
# S3 method for class 'stanreg'
sensitivity_to_prior(model, index = "Median", magnitude = 10, ...)A Bayesian model (stanreg or brmsfit).
Arguments passed to or from other methods.
The indices from which to compute the sensitivity. Can be one or
multiple names of the columns returned by describe_posterior. The case is
important here (e.g., write 'Median' instead of 'median').
This represent the magnitude by which to shift the antagonistic prior (to test the sensitivity). For instance, a magnitude of 10 (default) means that the mode will be updated with a prior located at 10 standard deviations from its original location.
DescTools
# \donttest{
library(bayestestR)
# rstanarm models
# -----------------------------------------------
model <- rstanarm::stan_glm(mpg ~ wt, data = mtcars)
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#> SAMPLING FOR MODEL 'continuous' NOW (CHAIN 4).
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sensitivity_to_prior(model)
#> Parameter Sensitivity_Median
#> 1 wt 0.03699622
model <- rstanarm::stan_glm(mpg ~ wt + cyl, data = mtcars)
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#> SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).
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#> Chain 1: Gradient evaluation took 7e-05 seconds
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sensitivity_to_prior(model, index = c("Median", "MAP"))
#> Parameter Sensitivity_Median Sensitivity_MAP
#> 1 wt 0.03611038 0.03354017
#> 2 cyl 0.02489034 0.05805163
# }