GitHub - stefanlinner/tidyMC (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, tidyMCaims 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()andplot.summary.mc() - Creating a
LaTeXtable summarizing the results of the Monte Carlo Simulation usingtidy_mc_latex()
Installing tidyMC
Install from CRAN
install.packages("tidyMC")
or download the development version fromGitHub as follows:
install.packages("devtools")
devtools::install_github("stefanlinner/tidyMC", build_vignettes = TRUE)
Afterwards you can load the package:
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: 00:00:06.505337
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
#>
#> 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: 00:00:06.505337
#>
#> 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-10}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\ #> \addlinespace #> 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-10}Mean MCS results} #> \centering #> \begin{tabular}[t]{ccc} #> \toprule #> param & x1 & mean\ #> \midrule #> \addlinespace[0.3em] #> \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\ #> \addlinespace[0.3em] #> \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}