# 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:

# install.packages("devtools")
devtools::install_github("stefanlinner/tidyMC", build_vignettes = TRUE)

Afterwards you can load the package:

library(tidyMC)

## Example

library(magrittr)
library(ggplot2)
library(kableExtra)

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

browseVignettes(package = "tidyMC")
#> starte den http Server für die Hilfe fertig

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:
#> $strategy #> multisession: #> - args: function (..., workers = availableCores(), lazy = FALSE, rscript_libs = .libPaths(), envir = parent.frame()) #> - tweaked: FALSE #> - call: NULL #> #> #> Seed: TRUE Summarize your results: sum_res <- summary(test_mc) sum_res #> Results for the output mean: #> param=0, x1=1: 3.015575 #> param=0, x1=2: 4.003162 #> param=0.5, x1=1: 3.49393 #> param=0.5, x1=2: 4.480855 #> param=1, x1=1: 3.985815 #> param=1, x1=2: 4.994084 #> #> #> Results for the output var: #> param=0, x1=1: 0.9968712 #> param=0, x1=2: 1.026523 #> param=0.5, x1=1: 0.9933278 #> param=0.5, x1=2: 0.9997529 #> param=1, x1=1: 0.9979682 #> param=1, x1=2: 1.005633 #> #>  Plot your results / summarized results: returned_plot1 <- plot(test_mc, plot = FALSE) returned_plot1$mean +
ggplot2::theme_minimal() +
ggplot2::geom_vline(xintercept = 3)


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


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


returned_plot1 <- plot(summary(test_mc), plot = FALSE)

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

Show your results in a LaTeX table:

tidy_mc_latex(summary(test_mc)) %>%
print()
#> \begin{table}
#>
#> \caption{\label{tab:unnamed-chunk-9}Monte Carlo simulations results}
#> \centering
#> \begin{tabular}[t]{cccc}
#> \toprule
#> param & x1 & mean & var\\
#> \midrule
#> 0.0 & 1 & 3.016 & 0.997\\
#> 0.0 & 2 & 4.003 & 1.027\\
#> 0.5 & 1 & 3.494 & 0.993\\
#> 0.5 & 2 & 4.481 & 1.000\\
#> 1.0 & 1 & 3.986 & 0.998\\
#> 1.0 & 2 & 4.994 & 1.006\\
#> \bottomrule
#> \multicolumn{4}{l}{\textsuperscript{} Total repetitions = 1000,}\\
#> \multicolumn{4}{l}{total parameter combinations}\\
#> \multicolumn{4}{l}{= 6}\\
#> \end{tabular}
#> \end{table}

tidy_mc_latex(
summary(test_mc),
repetitions_set = c(10,1000),
which_out = "mean",
kable_options = list(caption = "Mean MCS results")
) %>%
print()
#> \begin{table}
#>
#> \caption{\label{tab:unnamed-chunk-9}Mean MCS results}
#> \centering
#> \begin{tabular}[t]{ccc}
#> \toprule
#> param & x1 & mean\\
#> \midrule
#> \multicolumn{3}{l}{\textbf{N = 10}}\\
#> \hspace{1em}0.0 & 1 & 3.193\\
#> \hspace{1em}0.0 & 2 & 3.810\\
#> \hspace{1em}0.5 & 1 & 3.434\\
#> \hspace{1em}0.5 & 2 & 4.550\\
#> \hspace{1em}1.0 & 1 & 4.156\\
#> \hspace{1em}1.0 & 2 & 5.030\\
#> \multicolumn{3}{l}{\textbf{N = 1000}}\\
#> \hspace{1em}0.0 & 1 & 3.016\\
#> \hspace{1em}0.0 & 2 & 4.003\\
#> \hspace{1em}0.5 & 1 & 3.494\\
#> \hspace{1em}0.5 & 2 & 4.481\\
#> \hspace{1em}1.0 & 1 & 3.986\\
#> \hspace{1em}1.0 & 2 & 4.994\\
#> \bottomrule
#> \multicolumn{3}{l}{\textsuperscript{} Total repetitions =}\\
#> \multicolumn{3}{l}{1000, total}\\
#> \multicolumn{3}{l}{parameter}\\
#> \multicolumn{3}{l}{combinations = 6}\\
#> \end{tabular}
#> \end{table}