Prior/likelihood power-scaling perturbation — powerscale-overview • priorsense

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)
#> # A draws_df: 1000 iterations, 4 chains, and 2 variables
#>     mu sigma
#> 1  9.4  1.05
#> 2  9.5  1.06
#> 3  9.3  0.90
#> 4  9.4  1.06
#> 5  9.1  1.18
#> 6  9.2  1.06
#> 7  9.5  1.04
#> 8  9.7  0.60
#> 9  9.7  0.65
#> 10 9.5  0.77
#> # ... with 3990 more draws
#> # ... hidden reserved variables {'.log_weight', '.chain', '.iteration', '.draw'}
#> 
#> power-scaling
#>  alpha: 0.5 
#>  scaled component: prior 
#>  pareto-k: 0.2 
#>  pareto-k threshold: 0.72 
#>  resampled: FALSE 
#>  transform: identity 

powerscale_sequence(ex$draws)
#> base draws:
#> # A draws_df: 1000 iterations, 4 chains, and 2 variables
#>     mu sigma
#> 1  9.4  1.05
#> 2  9.5  1.06
#> 3  9.3  0.90
#> 4  9.4  1.06
#> 5  9.1  1.18
#> 6  9.2  1.06
#> 7  9.5  1.04
#> 8  9.7  0.60
#> 9  9.7  0.65
#> 10 9.5  0.77
#> # ... with 3990 more draws
#> # ... hidden reserved variables {'.chain', '.iteration', '.draw'}
#> 
#> power-scaling
#>  alpha range: [0.8, 1.25]
#>  length of sequence: 2 
#>  scaled component: likelihood 
#>  scaled component: prior 
#>  transform: identity