# 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]`

.

## 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)
```