tidyMC (version 1.0.1)

plot.summary.mc: Plot the summarized results of a Monte Carlo Simulation

Description

Plot line plots of the path of the summarized output over all simulation repetitions of a Monte Carlo simulation run by future_mc() and summarized by summary.mc()

Usage

# S3 method for summary.mc
plot(
  x,
  join = NULL,
  which_setup = NULL,
  parameter_comb = NULL,
  plot = TRUE,
  ...
)

Value

A list whose components are named after the outputs of fun

and each component contains an object of class ggplot and gg which can be plotted and modified with the ggplot2::ggplot2 functions.

Arguments

x

An object of class summary.mc. For restrictions see details.

join

A character vector containing the nice_names for the different parameter combinations (returned by future_mc()), which should be plotted together. Default: Each parameter combination is plotted distinct.

which_setup

A character vector containing the nice_names for the different parameter combinations (returned by future_mc()), which should be plotted. Default: All parameter combinations are plotted.

parameter_comb

Alternative to which_setup. A named list whose components are named after (some of) the parameters in param_list in future_mc() and each component is a vector containing the values for the parameters to filter by. Default: All parameter combinations are plotted.

plot

Boolean that specifies whether the plots should be printed while calling the function or not. Default: TRUE

...

additional arguments passed to callies.

Details

Only one of the arguments join, which_setup, and paramter_comb can be specified at a time.

A plot is only created for (output - parameter combination)-pairs for which in summary.mc() a function is provided in sum_funs which returns a single numeric value and if the output is included in which_path.

Examples

test_func <- function(param = 0.1, n = 100, x1 = 1, x2 = 2){

  data <- rnorm(n, mean = param) + x1 + x2
  stat <- mean(data)
  stat_2 <- var(data)

  if (x2 == 5){
    stop("x2 can't be 5!")
  }

  return(list(mean = stat, var = stat_2))
}

param_list <- list(param = seq(from = 0, to = 1, by = 0.5),
                   x1 = 1:2)

set.seed(101)
test_mc <- future_mc(
  fun = test_func,
  repetitions = 1000,
  param_list = param_list,
  n = 10,
  x2 = 2
)

returned_plot1 <- plot(summary(test_mc))

returned_plot1$mean +
 ggplot2::theme_minimal()

returned_plot2 <- plot(summary(test_mc),
which_setup = test_mc$nice_names[1:2], plot = FALSE)
returned_plot2$mean

returned_plot3 <- plot(summary(test_mc),
join = test_mc$nice_names[1:2], plot = FALSE)
returned_plot3$mean