powerscale-gradients {priorsense}R Documentation

Power-scale gradients

Description

Calculate the numerical derivative of posterior quantities/divergence with respect to power-scaling the specified component (prior or likelihood). This is done using importance sampling (and optionally moment matching).

Usage

powerscale_gradients(x, ...)

## Default S3 method:
powerscale_gradients(x, ...)

## S3 method for class 'priorsense_data'
powerscale_gradients(
  x,
  variable = NULL,
  component = c("prior", "likelihood"),
  type = c("quantities", "divergence"),
  lower_alpha = 0.99,
  upper_alpha = 1.01,
  div_measure = "cjs_dist",
  measure_args = list(),
  moment_match = FALSE,
  k_threshold = 0.5,
  resample = FALSE,
  transform = NULL,
  prediction = NULL,
  scale = FALSE,
  prior_selection = NULL,
  likelihood_selection = NULL,
  ...
)

Arguments

x

Model fit or draws object.

...

Further arguments passed to functions.

variable

Variables to compute sensitivity of. If NULL (default) sensitivity is computed for all variables.

component

Component to power-scale (prior or likelihood).

type

type of sensitivity to measure ("distance", "quantity"). Multiple options can be specified at the same time.

lower_alpha

lower power to scale component by, should be < 1 (default is 0.9).

upper_alpha

upper power to scale component by, should be > 1 (default is 1.1).

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.

moment_match

Logical; Indicate whether or not moment matching should be performed. Can only be TRUE if is_method is "psis".

k_threshold

Threshold value for Pareto k values above which the moment matching algorithm is used. Default is 0.5.

resample

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

transform

Indicate a transformation of posterior draws to perform before sensitivity analysis. Either "scale" or "whiten".

prediction

Function taking the model fit and returning a draws_df of predictions to be appended to the posterior draws

scale

logical scale quantity gradients by base posterior standard deviation.

prior_selection

Numeric vector specifying which priors to consider.

likelihood_selection

Numeric vector specifying which likelihoods to consider.

Value

Maximum of the absolute derivatives above and below alpha = 1.

Examples

ex <- example_powerscale_model()
drw <- ex$draws

powerscale_gradients(drw)

[Package priorsense version 1.0.2 Index]