Power-scale gradients — powerscale-gradients • priorsense

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, ...)

# 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,
  ...
)

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)
#> Loading required namespace: testthat
#> $multivariate_divergence
#> $multivariate_divergence$prior
#> NULL
#> 
#> $multivariate_divergence$likelihood
#> NULL
#> 
#> 
#> $divergence
#> $divergence$prior
#> # A tibble: 2 × 2
#>   variable cjs_dist
#>   <chr>       <dbl>
#> 1 mu          0.433
#> 2 sigma       0.358
#> 
#> $divergence$likelihood
#> # A tibble: 2 × 2
#>   variable cjs_dist
#>   <chr>       <dbl>
#> 1 mu          0.641
#> 2 sigma       0.671
#> 
#> 
#> $quantities
#> $quantities$prior
#> # A tibble: 2 × 7
#>   variable   mean median    sd    mad      q5    q95
#>   <chr>     <dbl>  <dbl> <dbl>  <dbl>   <dbl>  <dbl>
#> 1 mu       -0.312 -0.228 0.165 0.0916 -0.391  -0.212
#> 2 sigma     0.145  0.143 0.141 0.0483  0.0297  0.271
#> 
#> $quantities$likelihood
#> # A tibble: 2 × 7
#>   variable   mean median     sd    mad       q5    q95
#>   <chr>     <dbl>  <dbl>  <dbl>  <dbl>    <dbl>  <dbl>
#> 1 mu        0.396  0.299 -0.315 -0.167  0.644    0.173
#> 2 sigma    -0.246 -0.210 -0.288 -0.147 -0.00776 -0.451
#> 
#> 
#> $loadings
#> NULL
#>