add_funs: Helper functions for adding components to an 'Experiment'.

In Yu-Group/simChef: Intensive Computational Experiments Made Easy

Helper functions for adding components to an Experiment.

Description

Helper functions for adding DGPs, Methods, Evaluators, and Visualizers to an Experiment.

Usage

add_dgp(experiment, dgp, name = NULL, ...)

add_method(experiment, method, name = NULL, ...)

add_evaluator(experiment, evaluator, name = NULL, ...)

add_visualizer(experiment, visualizer, name = NULL, ...)

Arguments

experiment	An Experiment object.
dgp	A DGP object.
name	A name to identify the object to be added.
...	Not used.
method	A Method object.
evaluator	An Evaluator object.
visualizer	A Visualizer object.

Value

The original Experiment object passed to ⁠add_*⁠.

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)
print(experiment)

# this is equivalent to
experiment <- create_experiment(
  name = "Experiment Name",
  dgp_list = list(`Normal DGP` = normal_dgp, `Bernoulli DGP` = bernoulli_dgp),
  method_list = list(`Mean(x)` = mean_method),
  evaluator_list = list(`SD of Mean(x)` = sd_mean_eval),
  visualizer_list = list(`SD of Mean(x) Plot` = sd_mean_plot)
)

Yu-Group/simChef documentation built on March 25, 2024, 3:22 a.m.