plot_pri_post_distributions: Plot prior-posterior distributions

In W-Mohammed/calibrater: Calibration of Decision-Analytic Models

Plot prior-posterior distributions

Description

Plot prior-posterior distributions

Usage

plot_pri_post_distributions(
  .engine_ = "ggplot2",
  .l_PSA_samples_ = self$PSA_samples,
  .l_params_ = self$calibration_parameters,
  .l_calibration_results_ = self$calibration_results,
  .t_prior_samples_ = self$prior_samples[["LHS"]],
  .transform_ = self$transform_parameters,
  .bins_ = 100,
  .legend_pos_ = "none",
  .log_scaled_ = FALSE
)

Arguments

.engine_	String specifying the plotting engine. Currently supports "ggplot2". Also, some of the code is written for the "triliscope" engine.
.l_PSA_samples_	List containing PSA samples - from which the function uses Bayesian calibration results.
.l_params_	List containing information about calibration parameters, including parameters' names, distributions, and boundaries.
.l_calibration_results_	List containing the results from the calibration methods.
.t_prior_samples_	Dataset or tibble containing prior samples.
.transform_	Logical for whether the model is set to handle parameters on a transformed scale.
.bins_	Numeric specifying the number of bins in the histograms.
.legend_pos_	String defining the location of the legend position default (bottom).
.log_scaled_	Logical for whether to present the x-axis using the log scale.

Examples

## Not run: 
pri_post_plots <- plot_pri_post_distributions(
  .engine_ = "ggplot2",
  .l_PSA_samples_ = CR_CRS_2P2T$PSA_samples,
  .l_params_ = CR_CRS_2P2T$calibration_parameters,
  .l_calibration_results_ = CR_CRS_2P2T$calibration_results,
  .t_prior_samples_ = CR_CRS_2P2T$prior_samples[["LHS"]],
  .transform_ = CR_CRS_2P2T$transform_parameters,
  .bins_ = 20,
  .legend_pos_ = "none",
  .log_scaled_ = FALSE)

## End(Not run)

W-Mohammed/calibrater documentation built on Oct. 14, 2023, 1:57 a.m.