The rationale for this plot and its interpretaion is explained in Mike Betancourt's Towards A Principled Bayesian Workflow.

```
plot_contraction(
x,
prior_sd,
variables = NULL,
scale = "sd",
alpha = 0.8,
parameters = NULL
)
```

- x
object containing results (a data.frame or

`SBC_results()`

object).- prior_sd
a named vector of prior standard deviations for your variables. Either pass in analytically obtained values or use

`calculate_prior_sd()`

to get an empirical estimate from an`SBC_datasets`

object.- variables
variables to show in the plot or

`NULL`

to show all must correspond a field already computed in the results (most likely`"mean"`

and`"median"`

).- scale
which scale of variability you want to see - either

`"sd"`

for standard deviation or`"var"`

for variance.- alpha
the alpha for the points

- parameters
DEPRECATED, use

`variables`

instead.

a ggplot2 plot object