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Diagnostic plots for power-scaling sensitivity

powerscale_plots.Rd

Various diagnostic plots for power-scaling sensitivity. See Plot Descriptions below for details.

Usage

powerscale_plot_dens(x, ...)

powerscale_plot_ecdf(x, ...)

# S3 method for class 'powerscaled_sequence'
powerscale_plot_ecdf(
  x,
  variable = NULL,
  resample = FALSE,
  length = 3,
  facet_rows = "component",
  help_text = getOption("priorsense.plot_help_text", TRUE),
  colors = NULL,
  ...
)

powerscale_plot_quantities(x, ...)

# S3 method for class 'powerscaled_sequence'
powerscale_plot_quantities(
  x,
  variable = NULL,
  quantity = c("mean", "sd"),
  div_measure = "cjs_dist",
  resample = FALSE,
  measure_args = NULL,
  mcse = TRUE,
  quantity_args = NULL,
  help_text = getOption("priorsense.plot_help_text", TRUE),
  colors = NULL,
  ...
)

Arguments

x

An object of class powerscaled_sequence or an object for which powerscale_sequence will first be run on.

...

Arguments passed to powerscale_sequence if x is not of class powerscaled_sequence.

variable

A character vector of variable names. If NULL (the default) all variables will be plotted.

resample

Logical; Indicate whether or not draws should be resampled based on calculated importance weights.

length

Numeric specifying how many alpha values should be used. Ignored of the object is of class powerscaled_sequence.

facet_rows

Character defining the rows of the plot facets, either "variable" or "component". Default is "variable".

help_text

Logical indicating whether title and subtitle with explanatory description should be included in the plot. Default is TRUE. Can be set via option "priorsense.show_help_text".

colors

Character vector of colors to be used for plots. Either length 3 for powerscale_plot_ecdf and powerscale_plot_dens with order lowest, base, highest; or length 2 for powerscale_plot_quantities with order low Pareto k, high Pareto k. If NULL the defaults will be used.

quantity

A character vector specifying one or several quantities to plot. Options are "mean", "median", "sd", "mad", "quantile".

div_measure

The divergence measure to use. The following methods are implemented:

  • "cjs_dist": Cumulative Jensen-Shannon distance. Default method. See function cjs_dist for more details.

  • "js_dist": Jensen-Shannon distance.

  • "js_div": Jensen-Shannon divergence.

  • "hellinger_dist": Hellinger distance.

  • "kl_dist": Kullback-Leibler distance.

  • "kl_div": Kullback-Leibler divergence.

  • "ks_dist": Kolmogorov-Smirnov distance.

  • "hellinger_dist": Hellinger distance.

  • "ws_dist": Wassterstein distance (pass measure_args = list(p = N)) for a different order, where N is the order.

measure_args

Named list of further arguments passed to divergence measure functions.

mcse

Boolean; If TRUE will plot +/- 2 * Monte Carlo standard error of the base quantity on the quantities plot.

quantity_args

Named list of further arguments passed to quantity functions. Passed as .args to [posterior::summarise_draws].

Value

A ggplot object that can be further customized using the ggplot2 package.

Plot Descriptions

powerscale_plot_dens()

Kernel density plot of power-scaled posterior draws with respect to power-scaling.

powerscale_plot_ecdf()

Empirical cumulative distribution function plot of power-scaled posterior draws with respect to power-scaling.

powerscale_plot_quantities()

Plot of posterior quantities with respect to power-scaling.

Examples

ex <- example_powerscale_model()

powerscale_plot_dens(ex$draws)