Plot the summarized results of a Monte Carlo Simulation

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,
  ...
)

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.

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 functions.

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
)
#> Running single test-iteration for each parameter combination...
#> 
#>  Test-run successfull: No errors occurred!
#> Running whole simulation: Overall 6 parameter combinations are simulated ...
#> 
#>  Simulation was successfull!
#>  Running time: 00:00:00.999689

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