Helper functions for getting components in an Experiment.

Helper functions for getting or retrieving DGPs, Methods, Evaluators, and Visualizers from an Experiment.

Usage

get_dgps(experiment, ...)

get_methods(experiment, ...)

get_evaluators(experiment, ...)

get_visualizers(experiment, ...)

Arguments

experiment

An Experiment object.

...

Not used.

Value

A named list of DGPs, Methods, Evaluators, or Visualizers

Examples

## create toy DGPs, Methods, Evaluators, and Visualizers

# generate data from normal distribution with n samples
normal_dgp <- create_dgp(
  .dgp_fun = function(n) rnorm(n), .name = "Normal DGP", n = 100
)
# generate data from binomial distribution with n samples
bernoulli_dgp <- create_dgp(
  .dgp_fun = function(n) rbinom(n, 1, 0.5), .name = "Bernoulli DGP", n = 100
)

# compute mean of data
mean_method <- create_method(
  .method_fun = function(x) list(mean = mean(x)), .name = "Mean(x)"
)

# evaluate SD of mean(x) across simulation replicates
sd_mean_eval <- create_evaluator(
  .eval_fun = function(fit_results, vary_params = NULL) {
    group_vars <- c(".dgp_name", ".method_name", vary_params)
    fit_results %>%
      dplyr::group_by(dplyr::across(tidyselect::all_of(group_vars))) %>%
      dplyr::summarise(sd = sd(mean), .groups = "keep")
  },
  .name = "SD of Mean(x)"
)
# plot SD of mean(x) across simulation replicates
sd_mean_plot <- create_visualizer(
  .viz_fun = function(fit_results, eval_results, vary_params = NULL,
                      eval_name = "SD of Mean(x)") {
    if (!is.null(vary_params)) {
      add_aes <- ggplot2::aes(
        x = .data[[unique(vary_params)]], y = sd, color = .dgp_name
      )
    } else {
      add_aes <- ggplot2::aes(x = .dgp_name, y = sd)
    }
    plt <- ggplot2::ggplot(eval_results[[eval_name]]) +
      add_aes +
      ggplot2::geom_point()
    if (!is.null(vary_params)) {
      plt <- plt + ggplot2::geom_line()
    }
    return(plt)
  },
  .name = "SD of Mean(x) Plot"
)

# initialize experiment with toy DGPs, Methods, Evaluators, and Visualizers
# using piping %>% and add_* functions
experiment <- create_experiment(name = "Experiment Name") %>%
  add_dgp(normal_dgp) %>%
  add_dgp(bernoulli_dgp) %>%
  add_method(mean_method) %>%
  add_evaluator(sd_mean_eval) %>%
  add_visualizer(sd_mean_plot)

# get DGPs, Methods, Evaluators, and Visualizers
get_dgps(experiment)
#> $`Normal DGP`
#> DGP Name: Normal DGP 
#>    Function: function (n)  
#>    Parameters: List of 1
#>      $ n: num 100
#> 
#> $`Bernoulli DGP`
#> DGP Name: Bernoulli DGP 
#>    Function: function (n)  
#>    Parameters: List of 1
#>      $ n: num 100
#> 
get_methods(experiment)
#> $`Mean(x)`
#> Method Name: Mean(x) 
#>    Function: function (x)  
#>    Parameters:  list()
#> 
get_evaluators(experiment)
#> $`SD of Mean(x)`
#> Evaluator Name: SD of Mean(x) 
#>    Function: function (fit_results, vary_params = NULL)  
#>    Parameters:  list()
#>    R Markdown Options: List of 3
#>      $ digits : num 2
#>      $ sigfig : logi FALSE
#>      $ options:List of 2
#>       ..$ scrollX       : logi TRUE
#>       ..$ scrollCollapse: logi TRUE
#>    Show in R Markdown: TRUE 
#> 
get_visualizers(experiment)
#> $`SD of Mean(x) Plot`
#> Visualizer Name: SD of Mean(x) Plot 
#>    Function: function (fit_results, eval_results, vary_params = NULL, eval_name = "SD of Mean(x)")  
#>    Parameters:  list()
#>    R Markdown Options: List of 2
#>      $ height: num 6
#>      $ width : num 10
#>    Show in R Markdown: TRUE 
#>