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).
Usage
sensitivity_to_prior(model, ...)
# S3 method for class 'stanreg'
sensitivity_to_prior(model, index = "Median", magnitude = 10, ...)Arguments
- model
A Bayesian model (
stanregorbrmsfit).- ...
Arguments passed to or from other methods.
- index
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').- magnitude
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 wil be updated with a prior located at 10 standard deviations from its original location.
Examples
# \donttest{
library(bayestestR)
# rstanarm models
# -----------------------------------------------
model <- rstanarm::stan_glm(mpg ~ wt, data = mtcars)
#>
#> SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).
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#> Chain 1: Gradient evaluation took 2e-05 seconds
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#> SAMPLING FOR MODEL 'continuous' NOW (CHAIN 2).
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#> Chain 2: Gradient evaluation took 8e-06 seconds
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#>
#> SAMPLING FOR MODEL 'continuous' NOW (CHAIN 3).
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#> Chain 3: Gradient evaluation took 9e-06 seconds
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#> Chain 3: 0.055 seconds (Total)
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#>
#> SAMPLING FOR MODEL 'continuous' NOW (CHAIN 4).
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#> Chain 4: Gradient evaluation took 9e-06 seconds
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sensitivity_to_prior(model)
#> Parameter Sensitivity_Median
#> 1 wt 0.04105146
model <- rstanarm::stan_glm(mpg ~ wt + cyl, data = mtcars)
#>
#> SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).
#> Chain 1:
#> Chain 1: Gradient evaluation took 2.1e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.21 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
# }