# Monte Carlo Simulations made easy and tidy with tidyMC

# Package overview

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()`

In the following subsections we will show how you can implement those tasks using the `tidyMC` package.

## 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")

Afterwards you can load the package:

    library(tidyMC)

Moreover, the following packages will be used in this vignette:

    # install.packages("magrittr")
    library(magrittr)
    # install.packages("ggplot2")
    library(ggplot2)
    # install.packages("kableExtra")
    library(kableExtra)

### Run your first Monte Carlo Simulation with `future_mc()`

`future_mc()` allows you to run a Monte Carlo simulation for a user defined function and given parameters. The first argument of `future_mc()` is `fun` which has to be a function that handles the generation of data, the application of the method of interest and the evaluation of the result for a single repetition and parameter combination. `future_mc()` handles the generation of loops over the desired parameter grids and the repetition of the Monte Carlo experiment for each of the parameter constellations. Consider the following example for `fun` and note that it performs the required tasks of generating data, applying the method and evaluating the results:

    # fun
    ols_test <-
      function(b0, b1, b2, n, sigma2, param_x1, param_x2, inc_x2){

        # generation of data
        x1 <- rnorm(n = n, mean = param_x1[1], sd = param_x1[2])
        x2 <- rnorm(n = n,  mean = param_x2[1], sd = param_x2[2])
        e <- rnorm(n, sd = sqrt(sigma2))
        y <- b0 + b1*x1 + b2*x2 + e

        if (inc_x2 == 0){
          x2 <- x2 * inc_x2
        }

        # application of method
        estim <- lm(y ~ x1 + x2)

        # evaluation of the result for a single repetition and parameter combination
        out <- list(b0 = estim$coefficients[1],
                    b1 = estim$coefficients[2],
                    b2 = estim$coefficients[3],
                    sigma2 = var(estim$residuals))
        return(out)
      }

The second argument of `future_mc()` is `repetitions` which should be an integer specifying the number of Monte Carlo iterations. While the third argument `param_list` should be a list whose components are named after the parameters of `fun` which should vary for the different Monte Carlo Simulation and each component is a vector containing the desired grid values for the parameter. Consider the following example for `param_list` and note that its components are named accordingly to the parameters of `ols_test`: `n` and `inc_x2`, respectively:

    # param_list
    param_list_ols <-
      list(n = c(100, 200, 300), inc_x2 = c(0,1))

`future_mc()` takes care of creating all possible parameter combinations of `param_list` and runs the Monte Carlo simulation over all of these for all possible combinations. If you don’t want to run a Monte Carlo simulation for every possible parameter combination you can alternatively define `param_table` with a `data.frame` or `data.table` containing the parameter combinations you are interested in.The `...` argument can be used to specify further arguments of `fun` which are not contained in `param_list`. Those arguments will be held fixed for all parameter combinations. In our OLS example those arguments are `b0`, `b1`, `b2`, `sigma2`, `param_x1`, and `param_x2`.

Moreover, there are four formal requirements that `fun` and thus `ols_test` have to fulfill:

- The arguments of `fun` which are present in `param_list` have to be scalar values. Note that the arguments of `ols_test` which are contained in `param_list_ols`: `n` and `inc_x2` are scalar values. The remaining arguments of `ols_test` are allowed to take non-scalar values.
- Every variable used inside `fun` has either to be defined inside `fun` or given as an argument through the `...` argument.
- The value returned by `fun` has to be a named list. In our example the names of the returned list are `b0`, `b1`, `b2`, and `sigma2`.
- The names of the returned values and those of the arguments contained in `param_list` need to be different. Moreover, they cannot be `params`, `repetitions` or `setup` as these names are already occupied. Note that `b0`, `b1`, `b2`, and `sigma2` are the names of the returned values as well as names of arguments of `ols_test`. However, none of those arguments is contained in `param_list_ols`. If we would add either of those variables to `param_list_ols` we would need to change the name of the returned value for the respective variable.

We recommend to even further restrict the return value of `fun` to be a named list of scalars. This allows you to use all comfort functions of the `tidyMC` package. As you can see, we did that for `ols_test`.

The argument `parallelisation_plan` allows the user to set a parallelisation plan. While the argument `parallelisation_options` allows the user to fine tune functions, such as `furrr::future_map()` by `furrr::furrr_options()`. The argument `seed` for `furrr::furrr_options()` can be specified in `parallelisation_options` following the formal requirements of its respective documentation. Moreover, the user can also decide not to run the Monte Carlo in parallel by setting `parallel = FALSE`. To construct `parallelisation_plan` the user needs to provide a list named after the arguments of `future::plan`. The main argument `strategy` needs to provide the specific type of parallelisation the user would like to use and the number of cores which are used in the function. Some of the options for strategy are: `multisession`, `multicore` and `cluster`. We strongly recommend the user to read the documentation of the `future` package for a more detailed instruction of how to set up the different strategies.

As a default (`check = TRUE`) `future_mc()` runs a quick check by running a single test-iteration for each parameter combination in order to check for possible errors in `fun`. If a error occurs the user not only receives the error message but also the parameter combinations for which the error occurred:

    set.seed(101)
    first_mc_ols <- future_mc(
      fun = ols_test,
      repetitions = 1000,
      param_list = param_list_ols,
      b0 = 1,
      b1 = 4,
      b2 = 5,
      sigma2 = -2,
      param_x1 = c(0,5),
      param_x2 = c(0,6),
      check = TRUE
    )
    #> Running single test-iteration for each parameter combination...
    #> Error in future_mc(fun = ols_test, repetitions = 1000, param_list = param_list_ols, :
    #>  Function error: NaNs wurden erzeugt
    #>  At the parameters: n=100, inc_x2=0
    #>
    #>  Function error: NaNs wurden erzeugt
    #>  At the parameters: n=200, inc_x2=0
    #>
    #>  Function error: NaNs wurden erzeugt
    #>  At the parameters: n=300, inc_x2=0
    #>
    #>  Function error: NaNs wurden erzeugt
    #>  At the parameters: n=100, inc_x2=1
    #>
    #>  Function error: NaNs wurden erzeugt
    #>  At the parameters: n=200, inc_x2=1
    #>
    #>  Function error: NaNs wurden erzeugt
    #>  At the parameters: n=300, inc_x2=1
    #>

The attentive reader might already have noticed that we specified `sigma2 = -2` which doesn’t make sense, as the variance of the error term cannot be negative. This results in a failed check for all parameter combinations, as this parameter is held fixed for any combination. Once we correct that mistake, we can run our first Monte Carlo simulation:

    set.seed(101)
    first_mc_ols <- future_mc(
      fun = ols_test,
      repetitions = 1000,
      param_list = param_list_ols,
      b0 = 1,
      b1 = 4,
      b2 = 5,
      sigma2 = 2, # correctly specify sigma2
      param_x1 = c(0,5),
      param_x2 = c(0,6),
      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: 00:00:03.168615

`future_mc` returns a list of type `mc` and length 11 consisting of a tibble (`first_mc_ols$output`) containing the return value of `fun` for each iteration and parameter combination. In our case `first_mc_ols$output` contains a column for each output `b0`, `b1`, `b2`, and `sigma2`, as well as a column for each parameter in `param_list_ols` and a column containing the `nice_names` of the parameter combinations. Overall the `first_mc_ols$output` consists of 6000 rows, i.e., for each parameter combination 1.000 rows:

    first_mc_ols$output
    #> # A tibble: 6,000 × 7
    #>    params              n inc_x2     b0    b1    b2 sigma2
    #>    <chr>           <dbl>  <dbl>  <dbl> <dbl> <dbl>  <dbl>
    #>  1 n=100, inc_x2=0   100      0  1.28   3.38    NA   776.
    #>  2 n=100, inc_x2=0   100      0 -4.27   3.89    NA   773.
    #>  3 n=100, inc_x2=0   100      0  5.85   4.36    NA  1130.
    #>  4 n=100, inc_x2=0   100      0  1.57   4.69    NA  1067.
    #>  5 n=100, inc_x2=0   100      0 -0.802  3.93    NA   873.
    #>  6 n=100, inc_x2=0   100      0  1.26   4.44    NA   880.
    #>  7 n=100, inc_x2=0   100      0  5.01   4.22    NA   852.
    #>  8 n=100, inc_x2=0   100      0 -1.49   3.99    NA   938.
    #>  9 n=100, inc_x2=0   100      0  3.07   3.80    NA   779.
    #> 10 n=100, inc_x2=0   100      0  2.06   3.73    NA   999.
    #> # ℹ 5,990 more rows

If `ols_test` would not return a named list of scalars, but a named list of non-scalars, then `first_mc_ols$output` would not contain a column for each output, but a single column containing the named list of non-scalars for each iteration and parameter combination.

Moreover, `first_mc_ols` returns much other information about the Monte Carlo simulation that can be printed in a dense representation:

    first_mc_ols
    #> Monte Carlo simulation results for the specified function:
    #>
    #>  function (b0, b1, b2, n, sigma2, param_x1, param_x2, inc_x2)
    #> {
    #>     x1 <- rnorm(n = n, mean = param_x1[1], sd = param_x1[2])
    #>     x2 <- rnorm(n = n, mean = param_x2[1], sd = param_x2[2])
    #>     e <- rnorm(n, sd = sqrt(sigma2))
    #>     y <- b0 + b1 * x1 + b2 * x2 + e
    #>     if (inc_x2 == 0) {
    #>         x2 <- x2 * inc_x2
    #>     }
    #>     estim <- lm(y ~ x1 + x2)
    #>     out <- list(b0 = estim$coefficients[1], b1 = estim$coefficients[2],
    #>         b2 = estim$coefficients[3], sigma2 = var(estim$residuals))
    #>     return(out)
    #> }
    #>
    #>  The following 6 parameter combinations:
    #> # A tibble: 6 × 2
    #>       n inc_x2
    #>   <dbl>  <dbl>
    #> 1   100      0
    #> 2   200      0
    #> 3   300      0
    #> 4   100      1
    #> 5   200      1
    #> 6   300      1
    #> are each simulated 1000 times.
    #>
    #>  The Running time was: 00:00:03.168615
    #>
    #>  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 with `summary.mc()`

If `fun` returns a named list of scalars the user can use `summary.mc()` to summarize all Monte Carlo results. The first argument of the function is an object of class `mc` returned by `future_mc()`. The next argument `sum_funs` determines which summarizing functions will be used on the simulation results. The functions can be provided for any combination of: parameter combinations resulting from `param_list`, and the outputs of `fun`. Every specified function can only take one argument, which is the vector (with length `repetitions`) for every output. We will present all customization options of `sum_funs` in a stepwise manner.

The first option of summarizing the results is given by just providing the `mc` object to `summary.mc`. In this case, `mean()` will be applied to all numeric values and `summary()` to all non-numeric data types. When the summarizing functions return one numeric value (like `mean()`) the results are twofold:

- First, a single scalar result of the function evaluated using the complete output vector is returned in the first element.

- Second, a vector with length `repetitions` of numeric results from the stepwise calculation of the function’s result across the output’s vector. We call this resulting vector as the “path” of the summarizing function.

Additionally, to save computation time the parameter `which_path` is available to the user who wants to specify for which outputs the “path” should be calculated. The user needs to provide a character vector with the output names’. Moreover, the options `"all"` (the default) and `"none"` are also available.

For the OLS example since all outputs of `ols_test` are numeric, the returned object will be a named nested list composed of four elements named after the `nice_names` returned by `future_mc()`. Each of this elements are itself lists containing the summarized outputs, i.e. `b0`, `b1`, `b2`, and `sigma2`. Lastly each of these are composed by the “path” and scalar result of `mean()`.

    # Default
    summary_default <- summary(first_mc_ols)
    summary_default
    #> Results for the output b0:
    #>    n=100, inc_x2=0: 0.9185223
    #>    n=100, inc_x2=1: 1.003477
    #>    n=200, inc_x2=0: 1.02396
    #>    n=200, inc_x2=1: 1.002091
    #>    n=300, inc_x2=0: 1.000223
    #>    n=300, inc_x2=1: 1.002196
    #>
    #>
    #> Results for the output b1:
    #>    n=100, inc_x2=0: 3.985091
    #>    n=100, inc_x2=1: 4.000044
    #>    n=200, inc_x2=0: 4.005565
    #>    n=200, inc_x2=1: 4.000216
    #>    n=300, inc_x2=0: 4.001195
    #>    n=300, inc_x2=1: 4.000031
    #>
    #>
    #> Results for the output b2:
    #>    n=100, inc_x2=0:
    #> [1] NA
    #>
    #>    n=100, inc_x2=1: 5.001864
    #>    n=200, inc_x2=0:
    #> [1] NA
    #>
    #>    n=200, inc_x2=1: 5.001091
    #>    n=300, inc_x2=0:
    #> [1] NA
    #>
    #>    n=300, inc_x2=1: 5.000132
    #>
    #>
    #> Results for the output sigma2:
    #>    n=100, inc_x2=0: 892.7561
    #>    n=100, inc_x2=1: 1.958776
    #>    n=200, inc_x2=0: 895.652
    #>    n=200, inc_x2=1: 1.982949
    #>    n=300, inc_x2=0: 900.6887
    #>    n=300, inc_x2=1: 1.983788
    #>
    #>
    str(summary_default[[1]])
    #> List of 4
    #>  $ b0    :List of 2
    #>   ..$ mean          : num 0.919
    #>   ..$ mean_over_reps: num [1:1000] 1.278 -1.497 0.954 1.108 0.726 ...
    #>  $ b1    :List of 2
    #>   ..$ mean          : num 3.99
    #>   ..$ mean_over_reps: num [1:1000] 3.38 3.63 3.88 4.08 4.05 ...
    #>  $ b2    :List of 1
    #>   ..$ mean: num NA
    #>  $ sigma2:List of 2
    #>   ..$ mean          : num 893
    #>   ..$ mean_over_reps: num [1:1000] 776 774 893 936 923 ...

This nested list structure should give an idea of the reach and flexibility the `sum_funs` argument is allowed to have, since the user can specify a function for each element in this list.

For an intermediate level of customization, the user can provide a combination of summarizing functions for every output, which will be used for all parameter combination. In the case of the OLS example, if we want to apply `mean()` on all estimated coefficients, but we want to use `var()` on the MC results of `sigma2`, the `sum_funs` should have the following structure:

    # summarizing output for each parameter combination with one combination
    sum_funs_ols <- list(b0 = mean, b1 = mean , b2 = mean, sigma2 = var)

Moreover, the user can specify any function provided it takes the output vector as only available argument.

Lastly for the last level of customization, a nested list named after the `nice_names` where every element follows the structure of the last example (components named after the outputs and each component is a function) can be specified. We present an example for this:

    quantile_sum <- function(x) quantile(x, probs = 0.75)

    # summarizing output differently for different parameter combinations
    sum_funs2 <-
      list(
        list(b0 = quantile_sum, b1 = min, b2 = min, sigma2 = mean),
        list(b0 = mean, b1 = quantile_sum, b2 = mean, sigma2 = mean),
        list(b0 = median, b1 = median, b2 = median, sigma2 = mean),
        list(b0 = max, b1 = max, b2 = max, sigma2 = mean),
        list(b0 = min, b1 = min, b2 = min, sigma2 = quantile_sum),
        list(b0 = mean, b1 = mean, b2 = quantile_sum, sigma2 = quantile_sum)
      )
    names(sum_funs2) <- first_mc_ols$nice_names
    summary_out_param_spec <- summary(first_mc_ols, sum_funs = sum_funs2)

We would like to reiterate that the provided summary functions are not restricted regarding the complexity of their return value. However, the path of the summarized output over all simulation repetitions is only returned if the provided summary functions return a single numeric value. Thus, the following comfort functions `plot.summary.mc()` and `tidy_mc_latex()` will only work in this specific case.

### Plot your Monte Carlo Simulation with `plot.mc()` and `plot.summary.mc()`

If `fun` returns a named list of scalars the user can use `plot.mc()` to generate a list of objects of class `gg` and `ggplot2` for all Monte Carlo results. The first argument of the function is an object of class `mc` returned by `future_mc()`. Using the argument `plot` the user can indicate whether the generated plots should be printed immediately or only returned as a list. The list will contain one plot for each output of `fun` comparing the results of the different simulation setups. In general, `plot.mc()` generates density plots for numeric outputs and bar plots for non-numeric outputs. In our example a plot for `b0`, `b1`, `b2`, and `sigma2` will be returned in a list of length four and as `b0`, `b1`, `b2`, and `sigma2` are all numeric outputs `plot.mc()` will return density plots for each of those:

    mc_ols_plot <- plot(first_mc_ols, plot = FALSE)
    names(mc_ols_plot)
    #> [1] "b0"     "b1"     "b2"     "sigma2"

As the single list elements are of class `gg` and `ggplot2`, we can easily customize and extend the single plots using familiar `ggplot2` commands:

    mc_ols_plot$b1 +
      ggplot2::geom_vline(xintercept = 4, col = "red") +
      ggplot2::theme_minimal()

When creating the plots the user can also subset the setups which he/she would like to see in the plots using the `first_mc_ols$nice_names` in the function argument `which_setup`, or a named list in `parameter_comb`. The single components of the list have to be named after the parameters specified in `param_list` and contain vectors specifying the values of the parameters to filter by. In the ols example we can filter by the parameters `n` and `inc_x2`:

    # subsetting by nice_names
    mc_ols_plot_subset1 <-
      plot(first_mc_ols, plot = FALSE, which_setup = first_mc_ols$nice_names[4:6])
    #subsetting by parameter values
    mc_ols_plot_subset2 <-
      plot(first_mc_ols, plot = FALSE, parameter_comb = list(inc_x2 = 1))

    mc_ols_plot_subset1$sigma2

Thus, if the user wants distinct plots for every parameter combination, one needs to subset the plot for any single setup in `first_mc_ols$nice_names`.

Finally, you can also plot the simulation results for several parameter combination in one single plot by specifying the argument `join` with the respective `first_mc_ols$nice_names`:

    mc_ols_plot_joint <- plot(first_mc_ols, plot = FALSE,
                              join = first_mc_ols$nice_names)
    mc_ols_plot_joint$b2
    #> Warning: Removed 3000 rows containing non-finite outside the scale range
    #> (`stat_density()`).

Please be aware that the only one of the three arguments `which_setup`, `parameter_comb`, and `join` can be specified at the same time.

If the provided summary functions in `summary.mc()` return a single numeric value and thus a path of the summarized output over all simulation repetitions is returned, the user can use `plot.summary.mc()` to plot those paths. The first argument of the function is an object of class `summary.mc` returned by `summary.mc()`. Just as `plot.mc()`, `plot.summary.mc()` returns a list of objects of class `gg` and `ggplot2`. The list will contain one line plot for each output of `fun` displaying the paths of the results of the different simulation setups. The arguments `plot`, `which_setup`, `parameter_comb`, and `join` can be specified the same way as for `plot.mc`:

    sum_mc_plot <- plot(summary_default, plot = FALSE)
    sum_mc_plot$b1 +
      ggplot2::geom_vline(xintercept = 100, col = "red") +
      ggplot2::theme(axis.text.x = element_text(angle = 45,
                                                hjust = 0.1,
                                                vjust = 0.2))

    sum_mc_plot_subset1 <-
      plot(summary_default,
           plot = FALSE,
           which_setup = first_mc_ols$nice_names[4:6])

    sum_mc_plot_subset2 <-
      plot(summary_default,
           plot = FALSE,
           parameter_comb = list(inc_x2 = 1))

    sum_mc_plot_subset2$b1

    sum_mc_plot_joint <-
      plot(summary_default, plot = FALSE, join = first_mc_ols$nice_names[4:6])

    sum_mc_plot_joint$b1

### Create a ‘LaTeX’ table of your results with `tidy_mc_latex()`

Using `tidy_mc_latex` the user can present the results from `future_mc` directly into a `LaTeX` document using all the benefits from the `kableExtra` package. The first and main argument `x` needed by `tidy_mc_latex` is a `summary.mc` object obtained from `summary.mc()`. To present the results in a comprehensive manner the function requires that all summarized outputs in `summary.mc` be scalar numeric results for all parameter combinations. In case, the summarizing function returns more than one argument then this will be presented in the table as an `NA` value. The second argument of the function is `repetitions_set` which allows the user to see the certain values of the “path” of the summarized results of `fun`. To illustrate this we use the MC results for the OLS example:

    tidy_mc_latex(
      x = summary(first_mc_ols),
      repetitions_set = c(10, 1000)
    ) %>%
      print()
    #> \begin{table}
    #>
    #> \caption{\label{tab:unnamed-chunk-19}Monte Carlo simulations results}
    #> \centering
    #> \begin{tabular}[t]{cccccc}
    #> \toprule
    #> n & inc_x2 & b0 & b1 & b2 & sigma2\\
    #> \midrule
    #> \addlinespace[0.3em]
    #> \multicolumn{6}{l}{\textbf{N = 10}}\\
    #> \hspace{1em}100 & 0 & 1.354 & 4.043 & NA & 906.484\\
    #> \hspace{1em}100 & 1 & 0.989 & 4.011 & 4.999 & 1.915\\
    #> \hspace{1em}200 & 0 & 2.326 & 4.156 & NA & 871.074\\
    #> \hspace{1em}200 & 1 & 1.025 & 3.996 & 5.002 & 2.015\\
    #> \hspace{1em}300 & 0 & 0.083 & 4.006 & NA & 950.222\\
    #> \hspace{1em}300 & 1 & 0.989 & 3.997 & 4.997 & 2.058\\
    #> \addlinespace[0.3em]
    #> \multicolumn{6}{l}{\textbf{N = 1000}}\\
    #> \hspace{1em}100 & 0 & 0.919 & 3.985 & NA & 892.756\\
    #> \hspace{1em}100 & 1 & 1.003 & 4.000 & 5.002 & 1.959\\
    #> \hspace{1em}200 & 0 & 1.024 & 4.006 & NA & 895.652\\
    #> \hspace{1em}200 & 1 & 1.002 & 4.000 & 5.001 & 1.983\\
    #> \hspace{1em}300 & 0 & 1.000 & 4.001 & NA & 900.689\\
    #> \hspace{1em}300 & 1 & 1.002 & 4.000 & 5.000 & 1.984\\
    #> \bottomrule
    #> \multicolumn{6}{l}{\textsuperscript{} Total repetitions = 1000, total parameter}\\
    #> \multicolumn{6}{l}{combinations = 6}\\
    #> \end{tabular}
    #> \end{table}

The resulting table is composed of two panels, which corresponds to the length of `repetitions_set`. In them the columns correspond to the results of the summarizing functions for `b0`, `b1`, `b2`, and `sigma2`, and the rows correspond to specific combinations of the parameters provided in `parameter_list`. The footnote in the table shows the number of repetitions and the total parameter combinations provided to `future_mc`.

Moreover, the next three arguments in `tidy_mc_latex` are comfort options to select which results of `summary.mc` depending on the parameter combinations will be presented in the table. On one hand, The argument `which_setup` allows the user to make use of the `nice_names` of the parameter combinations in the returned object by `future_mc()` to subset the rows in the table. On the other hand, the argument `parameter_comb` is used to directly filter the parameters by their values. This argument requires a named list, containing vector or scalar values of all parameters to be filtered from. The user must only provide one of this arguments at a time. We show how to make use of both parameters to subset the rows of table for \(n = 100\) and \(inc_{x2}=1\):

    tidy_mc_latex(
      x = summary(first_mc_ols),
      repetitions_set = c(10, 1000),
      which_setup = first_mc_ols$nice_names[1]) %>%
      print()
    #> \begin{table}
    #>
    #> \caption{\label{tab:unnamed-chunk-20}Monte Carlo simulations results}
    #> \centering
    #> \begin{tabular}[t]{ccccc}
    #> \toprule
    #> n & inc_x2 & b0 & b1 & sigma2\\
    #> \midrule
    #> \addlinespace[0.3em]
    #> \multicolumn{5}{l}{\textbf{N = 10}}\\
    #> \hspace{1em}100 & 0 & 1.354 & 4.043 & 906.484\\
    #> \addlinespace[0.3em]
    #> \multicolumn{5}{l}{\textbf{N = 1000}}\\
    #> \hspace{1em}100 & 0 & 0.919 & 3.985 & 892.756\\
    #> \bottomrule
    #> \multicolumn{5}{l}{\textsuperscript{} Total repetitions = 1000, total}\\
    #> \multicolumn{5}{l}{parameter combinations = 6}\\
    #> \end{tabular}
    #> \end{table}

    tidy_mc_latex(
      x = summary(first_mc_ols),
      repetitions_set = c(10, 1000),
      parameter_comb = list(n = 100, inc_x2 = 1)) %>%
      print()
    #> \begin{table}
    #>
    #> \caption{\label{tab:unnamed-chunk-20}Monte Carlo simulations results}
    #> \centering
    #> \begin{tabular}[t]{cccccc}
    #> \toprule
    #> n & inc_x2 & b0 & b1 & b2 & sigma2\\
    #> \midrule
    #> \addlinespace[0.3em]
    #> \multicolumn{6}{l}{\textbf{N = 10}}\\
    #> \hspace{1em}100 & 1 & 0.989 & 4.011 & 4.999 & 1.915\\
    #> \addlinespace[0.3em]
    #> \multicolumn{6}{l}{\textbf{N = 1000}}\\
    #> \hspace{1em}100 & 1 & 1.003 & 4.000 & 5.002 & 1.959\\
    #> \bottomrule
    #> \multicolumn{6}{l}{\textsuperscript{} Total repetitions = 1000, total parameter}\\
    #> \multicolumn{6}{l}{combinations = 6}\\
    #> \end{tabular}
    #> \end{table}

The user can also subset the outputs of the original function (columns in the table) using the parameter `which_out`. This is done using a character vector with the names of the outputs, e.g. to only show the columns for `b0` and `sigma2` the user needs to set `which_out = c("b0", "sigma2")`.

Lastly, by providing a named list to the argument `kable_options`, the user can change all arguments of the underlying function `kable::kbl()`. The names of the list have to be equal to the names of the arguments of the function and the contents of every element also has to fulfill its requirements. We provide an example of how this list should be constructed, but for optimal usage we strongly recommend the user to see the documentation of the `kable::kbl()` function.

To allow for further customization the returned object by `tidy_mc_latex` is of class `knitr_kable`, therefore the user can utilize most functions from the `kableExtra` package in the standard tidyverse manner. For example:

    tidy_mc_latex(summary(first_mc_ols),
                  repetitions_set = c(10, 1000),
                  kable_options = list(
                    col.names = c("Number of observations",
                                  "$x_2$ included or not",
                                  "$\\beta_0$", "$\\beta_1$",
                                  "$\\beta_2$", "$s^2$"),
                    caption = "Ommited variable bias MC results"
                  )
    ) %>%
      kableExtra::kable_styling(latex_options = "HOLD_position") %>%
      print()
    #> \begin{table}[H]
    #>
    #> \caption{\label{tab:unnamed-chunk-21}Ommited variable bias MC results}
    #> \centering
    #> \begin{tabular}[t]{cccccc}
    #> \toprule
    #> Number of observations & $x_2$ included or not & $\beta_0$ & $\beta_1$ & $\beta_2$ & $s^2$\\
    #> \midrule
    #> \addlinespace[0.3em]
    #> \multicolumn{6}{l}{\textbf{N = 10}}\\
    #> \hspace{1em}100 & 0 & 1.354 & 4.043 & NA & 906.484\\
    #> \hspace{1em}100 & 1 & 0.989 & 4.011 & 4.999 & 1.915\\
    #> \hspace{1em}200 & 0 & 2.326 & 4.156 & NA & 871.074\\
    #> \hspace{1em}200 & 1 & 1.025 & 3.996 & 5.002 & 2.015\\
    #> \hspace{1em}300 & 0 & 0.083 & 4.006 & NA & 950.222\\
    #> \hspace{1em}300 & 1 & 0.989 & 3.997 & 4.997 & 2.058\\
    #> \addlinespace[0.3em]
    #> \multicolumn{6}{l}{\textbf{N = 1000}}\\
    #> \hspace{1em}100 & 0 & 0.919 & 3.985 & NA & 892.756\\
    #> \hspace{1em}100 & 1 & 1.003 & 4.000 & 5.002 & 1.959\\
    #> \hspace{1em}200 & 0 & 1.024 & 4.006 & NA & 895.652\\
    #> \hspace{1em}200 & 1 & 1.002 & 4.000 & 5.001 & 1.983\\
    #> \hspace{1em}300 & 0 & 1.000 & 4.001 & NA & 900.689\\
    #> \hspace{1em}300 & 1 & 1.002 & 4.000 & 5.000 & 1.984\\
    #> \bottomrule
    #> \multicolumn{6}{l}{\textsuperscript{} Total repetitions = 1000, total parameter combinations = 6}\\
    #> \end{tabular}
    #> \end{table}