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,
  log_prior_name = "lprior",
  log_lik_name = "log_lik",
  ...
)

## 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.
log_prior_name	Character (case sensitive) specifying name of the variable storing the log prior evaluations
log_lik_name	Character (case sensitive) specifying name of the variable storing the log likelihood evaluations
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	Character (case sensitive) specifying 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 April 17, 2025, 8:38 p.m.