Sensitivity to Prior

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 (stanreg or brmsfit).
...: 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).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 2.1e-05 seconds
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#> 
#> SAMPLING FOR MODEL 'continuous' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 1.1e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.11 seconds.
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#> 
#> SAMPLING FOR MODEL 'continuous' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 1.1e-05 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.11 seconds.
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#> Chain 3:                0.055 seconds (Total)
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#> 
#> SAMPLING FOR MODEL 'continuous' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 1.1e-05 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.11 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 2.2e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.22 seconds.
#> Chain 1: Adjust your expectations accordingly!
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#> SAMPLING FOR MODEL 'continuous' NOW (CHAIN 2).
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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
# }

Usage

Arguments

See also

Examples