powerscale-overview: Prior/likelihood power-scaling perturbation

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

Prior/likelihood power-scaling perturbation

Description

Estimate posterior draws based on power-scaling perturbations of prior or likelihood using importance sampling (and optionally moment matching).

Usage

powerscale(x, ...)

## Default S3 method:
powerscale(
  x,
  component,
  alpha,
  moment_match = FALSE,
  k_threshold = NULL,
  resample = FALSE,
  transform = NULL,
  prediction = NULL,
  variable = NULL,
  selection = NULL,
  ...
)

## S3 method for class 'priorsense_data'
powerscale(
  x,
  component,
  alpha,
  moment_match = FALSE,
  k_threshold = NULL,
  resample = FALSE,
  transform = NULL,
  prediction = NULL,
  variable = NULL,
  selection = NULL,
  ...
)

powerscale_sequence(x, ...)

## Default S3 method:
powerscale_sequence(
  x,
  lower_alpha = 0.8,
  upper_alpha = 1/lower_alpha,
  length = 3,
  variable = NULL,
  component = c("prior", "likelihood"),
  moment_match = FALSE,
  k_threshold = 0.5,
  resample = FALSE,
  transform = NULL,
  prediction = NULL,
  auto_alpha_range = FALSE,
  symmetric = TRUE,
  prior_selection = NULL,
  likelihood_selection = NULL,
  ...
)

## S3 method for class 'priorsense_data'
powerscale_sequence(
  x,
  lower_alpha = 0.8,
  upper_alpha = 1/lower_alpha,
  length = 3,
  variable = NULL,
  component = c("prior", "likelihood"),
  moment_match = FALSE,
  k_threshold = 0.5,
  resample = FALSE,
  transform = NULL,
  prediction = NULL,
  auto_alpha_range = FALSE,
  symmetric = TRUE,
  prior_selection = NULL,
  likelihood_selection = NULL,
  ...
)

Arguments

x	A fitted model object.
...	Further arguments passed to internal functions.
component	Component to be power-scaled (either "prior" or "likelihood"). For powerscale_sequence, this can be both "prior" and "likelihood".
alpha	Value by which to power-scale specified component. (likelihood/prior).
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
variable	Vector of variable names to return estimated posterior draws for. If NULL all variables will be included.
selection	Numeric vector specifying partitions of component to be included in power-scaling. Default is NULL, which takes all partitions.
lower_alpha	Lower power-scaling alpha value in sequence.
upper_alpha	Upper power-scaling alpha value in sequence.
length	Length of alpha sequence.
auto_alpha_range	Boolean. Restrict range to ensure Pareto-k values below threshold?
symmetric	Boolean. Should the alpha range be symmetrical around alpha = 1, on log-space?
prior_selection	Numeric vector of prior partitions to include in power-scaling. Default is NULL, which takes all partitions.
likelihood_selection	Numeric vector of likelihood partitions to include in power-scaling. Default is NULL, which takes all partitions.

Value

A powerscaled_draws or powerscaled_sequence object, which contains the estimated posterior draws resulting from the power-scaling perturbations and details of the perturbation and estimation methods.

References

Kallioinen, N., Paananen, T., Bürkner, P-C., Vehtari, A. (2023). Detecting and diagnosing prior and likelihood sensitivity with power-scaling perturbations. Statistics and Computing. 34(57). doi:10.1007/s11222-023-10366-5

Vehtari, A., Simpson, D., Gelman, A., Yao, Y., and Gabry, J. (2024). Pareto smoothed importance sampling. Journal of Machine Learning Research. 25(72). https://jmlr.org/papers/v25/19-556.html

Paananen, T., Piironen, J., Bürkner, P-C., Vehtari, A. (2021). Implicitly adaptive importance sampling. Statistics and Computing. 31(16). doi:10.1007/s11222-020-09982-2

Examples

ex <- example_powerscale_model()

powerscale(ex$draws, component = "prior", alpha = 0.5)

powerscale_sequence(ex$draws)

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