priorsense (version 1.1.0)

powerscale-gradients: 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, ...)
# S3 method for default
powerscale_gradients(
  x,
  log_prior_name = "lprior",
  log_lik_name = "log_lik",
  ...
)
# S3 method for 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,
  ...
)

Value

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

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.

Examples

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

powerscale_gradients(drw)