README (original) (raw)

tidyMC

Monte Carlo Simulations aim to study the properties of statistical inference techniques. At its core, a Monte Carlo Simulation works through the application of the techniques to repeatedly drawn samples from a pre-specified data generating process. The tidyMC package aims to cover and simplify the whole workflow of running a Monte Carlo simulation in either an academic or professional setting. Thus, tidyMC aims to provide functions for the following tasks:

• Running a Monte Carlo Simulation for a user defined function over a parameter grid using future_mc()
• Summarizing the results by (optionally) user defined summary functions using summary.mc()
• Creating plots of the Monte Carlo Simulation results, which can be modified by the user using plot.mc() and plot.summary.mc()
• Creating a LaTeX table summarizing the results of the Monte Carlo Simulation using tidy_mc_latex()

Installing tidyMC

Until now, the tidyMC package is not on CRAN, thus you need to download the development version from GitHub as follows:

Afterwards you can load the package:

Example

This is a basic example which shows you how to solve a common problem. For a more elaborate example please see the vignette:

Run your first Monte Carlo Simulation using your own parameter grid:

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, 
  check = TRUE
)
#> 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: 00:00:05.836134

test_mc
#> Monte Carlo simulation results for the specified function: 
#>  
#>  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))
#> } 
#>  
#>  The following 6 parameter combinations: 
#> # A tibble: 6 × 2
#>   param    x1
#>   <dbl> <int>
#> 1   0       1
#> 2   0.5     1
#> 3   1       1
#> 4   0       2
#> 5   0.5     2
#> 6   1       2
#> are each simulated 1000 times. 
#>  
#>  The Running time was: 00:00:05.836134 
#>  
#>  Parallel: TRUE 
#>  
#>  The following parallelisation plan was used: 
#> $strategy
#> multisession:
#> - args: function (..., workers = availableCores(), lazy = FALSE, rscript_libs = .libPaths(), envir = parent.frame())
#> - tweaked: FALSE
#> - call: NULL
#> 
#> 
#>  Seed: TRUE

Summarize your results:

sum_res <- summary(test_mc)
sum_res
#> Results for the output mean: 
#>    param=0, x1=1: 3.015575 
#>    param=0, x1=2: 4.003162 
#>    param=0.5, x1=1: 3.49393 
#>    param=0.5, x1=2: 4.480855 
#>    param=1, x1=1: 3.985815 
#>    param=1, x1=2: 4.994084 
#>  
#>  
#> Results for the output var: 
#>    param=0, x1=1: 0.9968712 
#>    param=0, x1=2: 1.026523 
#>    param=0.5, x1=1: 0.9933278 
#>    param=0.5, x1=2: 0.9997529 
#>    param=1, x1=1: 0.9979682 
#>    param=1, x1=2: 1.005633 
#>  
#> 

Plot your results / summarized results:

Show your results in a LaTeX table: