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 2e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.2 seconds.
#> Chain 1: Adjust your expectations accordingly!
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#> Chain 1:
#> Chain 1: Elapsed Time: 0.033 seconds (Warm-up)
#> Chain 1: 0.029 seconds (Sampling)
#> Chain 1: 0.062 seconds (Total)
#> Chain 1:
#>
#> SAMPLING FOR MODEL 'continuous' NOW (CHAIN 2).
#> Chain 2:
#> Chain 2: Gradient evaluation took 9e-06 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.09 seconds.
#> Chain 2: Adjust your expectations accordingly!
#> Chain 2:
#> Chain 2:
#> Chain 2: Iteration: 1 / 2000 [ 0%] (Warmup)
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#> Chain 2: Iteration: 1800 / 2000 [ 90%] (Sampling)
#> Chain 2: Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 2:
#> Chain 2: Elapsed Time: 0.047 seconds (Warm-up)
#> Chain 2: 0.029 seconds (Sampling)
#> Chain 2: 0.076 seconds (Total)
#> Chain 2:
#>
#> SAMPLING FOR MODEL 'continuous' NOW (CHAIN 3).
#> Chain 3:
#> Chain 3: Gradient evaluation took 1e-05 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.1 seconds.
#> Chain 3: Adjust your expectations accordingly!
#> Chain 3:
#> Chain 3:
#> Chain 3: Iteration: 1 / 2000 [ 0%] (Warmup)
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#> Chain 3: Iteration: 1800 / 2000 [ 90%] (Sampling)
#> Chain 3: Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 3:
#> Chain 3: Elapsed Time: 0.029 seconds (Warm-up)
#> Chain 3: 0.03 seconds (Sampling)
#> Chain 3: 0.059 seconds (Total)
#> Chain 3:
#>
#> 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.
#> Chain 4: Adjust your expectations accordingly!
#> Chain 4:
#> Chain 4:
#> Chain 4: Iteration: 1 / 2000 [ 0%] (Warmup)
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#> Chain 4:
#> Chain 4: Elapsed Time: 0.031 seconds (Warm-up)
#> Chain 4: 0.033 seconds (Sampling)
#> Chain 4: 0.064 seconds (Total)
#> Chain 4:
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!
#> Chain 1:
#> Chain 1:
#> Chain 1: Iteration: 1 / 2000 [ 0%] (Warmup)
#> Chain 1: Iteration: 200 / 2000 [ 10%] (Warmup)
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#> Chain 1:
#> Chain 1: Elapsed Time: 0.047 seconds (Warm-up)
#> Chain 1: 0.047 seconds (Sampling)
#> Chain 1: 0.094 seconds (Total)
#> Chain 1:
#>
#> 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.
#> Chain 2: Adjust your expectations accordingly!
#> Chain 2:
#> Chain 2:
#> Chain 2: Iteration: 1 / 2000 [ 0%] (Warmup)
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#> Chain 2:
#> Chain 2: Elapsed Time: 0.048 seconds (Warm-up)
#> Chain 2: 0.044 seconds (Sampling)
#> Chain 2: 0.092 seconds (Total)
#> Chain 2:
#>
#> SAMPLING FOR MODEL 'continuous' NOW (CHAIN 3).
#> Chain 3:
#> Chain 3: Gradient evaluation took 9e-06 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.09 seconds.
#> Chain 3: Adjust your expectations accordingly!
#> Chain 3:
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#> Chain 3: Iteration: 1 / 2000 [ 0%] (Warmup)
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#> Chain 3:
#> Chain 3: Elapsed Time: 0.049 seconds (Warm-up)
#> Chain 3: 0.053 seconds (Sampling)
#> Chain 3: 0.102 seconds (Total)
#> Chain 3:
#>
#> SAMPLING FOR MODEL 'continuous' NOW (CHAIN 4).
#> Chain 4:
#> Chain 4: Gradient evaluation took 1e-05 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.1 seconds.
#> Chain 4: Adjust your expectations accordingly!
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#> Chain 4: Elapsed Time: 0.049 seconds (Warm-up)
#> Chain 4: 0.039 seconds (Sampling)
#> Chain 4: 0.088 seconds (Total)
#> Chain 4:
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
# }
```