powerscale-gradients: Power-scale gradients

In n-kall/priorsense: Prior Diagnostics and Sensitivity Analysis

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)

n-kall/priorsense documentation built on Nov. 4, 2024, 10:30 p.m.