tidyMC

https://github.com/stefanlinner/tidymc

Repository

README.Rmd

---
output: github_document
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)
```

# tidyMC


[![R-CMD-check](https://github.com/stefanlinner/tidyMC/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/stefanlinner/tidyMC/actions/workflows/R-CMD-check.yaml)


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

Install from `CRAN`

```{r, eval=FALSE}
install.packages("tidyMC")
```

or download the development version from [GitHub](https://github.com/stefanlinner/tidyMC) as follows:

```{r, eval=FALSE}
# install.packages("devtools")
devtools::install_github("stefanlinner/tidyMC", build_vignettes = TRUE)
```

Afterwards you can load the package:

```{r}
library(tidyMC)
```


## Example

```{r, warning=FALSE, message=FALSE}
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: 

```{r}
browseVignettes(package = "tidyMC")
```

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

```{r}
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
)

test_mc
```

Summarize your results: 

```{r}
sum_res <- summary(test_mc)
sum_res
```

Plot your results / summarized results: 

```{r}
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: 

```{r}
tidy_mc_latex(summary(test_mc)) %>% 
  print()

tidy_mc_latex(
    summary(test_mc),
    repetitions_set = c(10,1000),
    which_out = "mean",
    kable_options = list(caption = "Mean MCS results")
) %>% 
  print()
```

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