Prior/posterior contraction plot. — plot

The rationale for this plot and its interpretation 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
)

# S3 method for data.frame
plot_contraction(
  x,
  prior_sd,
  variables = NULL,
  scale = "sd",
  alpha = 0.8,
  show_hidden = FALSE,
  parameters = NULL
)

Arguments

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.
show_hidden: Show variables marked with hidden_var_attribute() (by default, those are not shown, available only when x is a data.frame)

Value

a ggplot2 plot object