---
title: "Introduction to the SimDesign package"
author: "Phil Chalmers"
date: "`r Sys.Date()`"
output:
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    number_sections: true 
    toc: true
    toc_float:
      collapsed: false
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vignette: >
  %\VignetteIndexEntry{Introduction to the SimDesign package}
  %\VignetteEngine{knitr::rmarkdown}
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---

```{r nomessages, echo = FALSE}
knitr::opts_chunk$set(
  warning = FALSE,
  message = FALSE,
  fig.height = 5,
  fig.width = 5
)
options(digits=4)
par(mar=c(3,3,1,1)+.1)
```

Designing Monte Carlo simulations can be a fun and rewarding experience. Whether you are interested in
evaluating the performance of a new optimizer, re-evaluating previous research claims (like the 
ANOVA is 'robust' to violations of normality), determine power rates for an upcoming research proposal, 
or simply to appease a strange thought in your head about a new statistical idea you heard about, 
designing Monte Carlo simulations can be incredibly rewarding and are extremely important to those who are statistically oriented. 
However, organizing simulations can be a challenge, and all too often coders resort to the dreaded 
"for-loop" strategy, *for*-ever resulting in confusing, error prone, and simulation specific code. 
The package `SimDesign` is one attempt to fix these and other issues that often arise when designing Monte Carlo simulation
experiments.

Generally speaking, Monte Carlo simulations can be broken into three major components:

- **generate** your data from some model/probability density function given various **design** conditions to be studied (e.g., sample size,
distributions, group sizes, etc),
- **analyse** the generated data using whatever statistical analyses you are interested in (e.g., $t$-test,
ANOVA, SEMs, IRT, etc), and collect the statistics/CIs/$p$-values/parameter estimates you are interested in, and
- **summarise** the results after repeating the simulations $R$ number of times to obtain empirical estimates of the population's behavior.

Each operation above represents the essential components of the `SimDesign` package. The **design** component
is represented by a `data.frame` object containing the simulation conditions to be investigated, while **generate**,
**analyse**, and **summarise** represent user-defined functions which comprise the three steps in the simulation.
Each of these components are constructed and passed to the `runSimulation()` function where the simulation 
steps are evaluated, ultimately returning a `data.frame` object containing the simulation results.

# A general overview

```{r include=FALSE}
options(digits = 2)
```

After loading the `SimDesign` package, we begin by defining the required user-constructed functions. To expedite this process,
a call to `SimFunctions()` will create a template to be filled in, where all the necessary functional arguments have been pre-assigned, and only the body of the functions need to be modified. The documentation of each argument can be found in the respective 
R help files, however there organization is very simple conceptually.

To begin, the following code should be copied and saved to an external source (i.e., text) file.

```{r comment=NA}
library(SimDesign)
SimFunctions()
```

Alternatively, if you are lazy (read: efficient) or just don't like copy-and-pasting, `SimFunctions()` can write the output to a file
by providing a `filename` argument. The following creates a file (`mysim.R`) containing the simulation
design/execution and required user-defined functions.

```{r eval=FALSE}
SimFunctions('mysim')
```

For larger simulations, you may want to use two files, and if you'd prefer to 
have helpful comments included then these can be achieved with the `singlefile` and `comments` arguments, respectively.

```{r eval=FALSE}
SimFunctions('mysim', singlefile = FALSE, comments = TRUE)
```

The choice of using a single file or not is entirely a matter of preference, and will not influence the overall simulation implementation.

# Simulation: Determine estimator efficiency

As a toy example, let's consider how the following question can be investigated with `SimDesign`: 

*Question*: How does trimming affect recovering the mean of a distribution? Investigate this using
different sample sizes with Gaussian and $\chi^2$ distributions. Also, demonstrate the effect of using the 
median to recover the mean.

## Define the conditions

First, define the condition combinations that should be investigated. In this case we wish to study
4 different sample sizes, and use a symmetric and skewed distribution. The use of `createDesign()` is
extremely helpful here to create a completely crossed-design for each combination (there are 8 in total).

```{r}
Design <- createDesign(sample_size = c(30, 60, 120, 240), 
                       distribution = c('norm', 'chi'))
Design
```

Each row in `Design` represents a unique condition to be studied in the simulation. In this case, the first condition to be studied comes from row 1, where $N=30$ and the distribution should be normal. 

## Define the functions

We first start by defining the data generation functional component. The only argument accepted by this function is `condition`, which will always be a *single row from the Design data.frame object* of class `data.frame`. Conditions are run sequentially from row 1 to the last row in `Design`. It is also
possible to pass a `fixed_objects` object to the function for including fixed sets of population parameters and other conditions, however for this simple simulation this input is not required.

```{r}
Generate <- function(condition, fixed_objects = NULL) {
    N <- condition$sample_size
    dist <- condition$distribution
    if(dist == 'norm'){
        dat <- rnorm(N, mean = 3)
    } else if(dist == 'chi'){
        dat <- rchisq(N, df = 3)
    }
    dat
}
```

As we can see above, `Generate()` will return a numeric vector of length $N$ containing the data to
be analysed each with a population mean of 3 (because a $\chi^2$ distribution has a mean equal to its df).
Next, we define the `analyse` component to analyse said data:

```{r}
Analyse <- function(condition, dat, fixed_objects = NULL) {
    M0 <- mean(dat)
    M1 <- mean(dat, trim = .1)
    M2 <- mean(dat, trim = .2)
    med <- median(dat)
    
    ret <- c(mean_no_trim=M0, mean_trim.1=M1, mean_trim.2=M2, median=med)
    ret
}
```

This function accepts the data previously returned from `Generate()` (`dat`), the condition vector previously
mentioned.

At this point, we may conceptually think of the first two functions as being evaluated independently $R$ times to obtain
$R$ sets of results. In other words, if we wanted the number of replications to be 100, the first two functions
would be independently run (at least) 100 times, the results from `Analyse()` would be stored, and we would 
then need to summarise these 100 elements into meaningful meta statistics to describe their empirical properties.
This is where computing meta-statistics such as bias, root mean-square error, detection rates, 
and so on are of primary importance. Unsurprisingly, then, this is the purpose of the `summarise` component:

```{r}
Summarise <- function(condition, results, fixed_objects = NULL) {
    obs_bias <- bias(results, parameter = 3)
    obs_RMSE <- RMSE(results, parameter = 3)
    ret <- c(bias=obs_bias, RMSE=obs_RMSE, RE=RE(obs_RMSE))
    ret
}
```

Again, `condition` is the same as was defined before, while `results` is a `matrix`
containing all the results from `Analyse()`, where each row represents the result returned from each respective
replication, and the number of columns is equal to the length of a single vector returned by `Analyse()`. 

That sounds much more complicated than it is --- all you really need to know for this simulation 
is that an $R$ x 4 matrix called `results` is available to build a suitable summary from. 
Because the results is a matrix, `apply()` is useful to apply a function over each respective row. 
The bias and RMSE are obtained for each respective statistic, and the overall result is returned as a vector.

Stopping for a moment and thinking carefully, we know that each `condition` will be paired with a unique vector returned from
`Summarise()`. Therefore, you might be thinking that the result returned from the simulation will be in
a rectangular form, such as in a `matrix` or `data.frame`. 
Well, you'd be right --- good for you.

## Putting it all together

The last stage of the `SimDesign` work-flow is to pass the four defined elements to the `runSimulation()` 
function which, unsurprisingly given it's name, runs the simulation. There are numerous options available in the 
function, and these should be investigated by reading the `help(runSimulation)` HTML file. Options for 
performing simulations in parallel, storing/resuming temporary results, debugging functions,
and so on are available. Below we simply request that each condition be run 1000 times on a 
single processor, and finally store the results to an object called `results`.

```{r include=FALSE}
set.seed(1234)
```

```{r}
res <- runSimulation(Design, replications = 1000, generate=Generate, 
                         analyse=Analyse, summarise=Summarise)
res
```

As can be seen from the printed results, each result from the `Summarise()` function has been paired with their
respective condition, meta-statistics have been properly named, and three additional columns have been appended
to the results: `REPLICATIONS`, which indicates how many time the conditions were performed, `SIM_TIME`, indicating
the time (in seconds) it took to completely finish the respective conditions, and `SEED`, which indicates the random seeds used by `SimDesign` for each condition (for reproducibility). A call to `View()` in the 
R console may also be a nice way to sift through the `res` object.

## Interpreting the results

In this case, visually inspecting the simulation table is enough to understand what is occurring, though for
other Monte Carlo simulations use of ANOVAs, marginalized tables, and graphics should be used to capture the
essentially phenomenon in the results. Monte Carlo simulations are just like collecting and analysing data for experiments,
so my advice would be to put on your analysis hats and present your data as though it were data collected from the real world. 

In this particular simulation, it is readily apparent that using the un-adjusted mean will adequately recover
the population mean with little bias. The precision also seems to increase as sample sizes increase, which 
is indicated by the decreasing RMSE statistics. Generally, trimming causes less efficiency in the estimates,
where greater amounts of trimming results in even less efficiency, and using the median as a proxy to estimate
the mean is the least effective method. This is witnessed rather clearly in the following table, which 
prints the relative efficiency of the estimators:

```{r}
REs <- res[,grepl('RE\\.', colnames(res))]
data.frame(Design, REs)
```

Finally, when the $\chi^2$ distribution was investigated only the un-adjusted mean accurately portrayed the
population mean. This isn't surprising, because the trimmed mean is, after all, making inferences about the 
population trimmed mean, and the median is making inferences about, well, the median. Only when the
distributions under investigation are symmetric are the statistics able to draw inferences about the same inferences about
the mean of the population.

# Conceptual walk-through of what runSimulation() is doing

The following is a conceptual breakdown of what `runSimulation()` is actually doing behind the
scenes. Here we demonstrate the results from the first condition (row 1 of `Design`) to show
what each function returns. 

A single replication in a Monte Carlo simulation results in the following objects:

```{r, include=FALSE}
set.seed(1)
```

```{r}
(condition <- Design[1, ])
dat <- Generate(condition)
dat

res <- Analyse(condition, dat)
res
```

We can see that `Generate()` returns a `numeric` vector which is accepted by `Analyse()`. The `Analyse()` function then completes the analysis portion using the generated data, and returns a 
named vector with the observed parameter estimates. Of course, this is only a single replication, and therefore is not really meaningful in the grand scheme of things; so, it must be repeated a number of times.

```{r}
# repeat 1000x
results <- matrix(0, 1000, 4)
colnames(results) <- names(res)
for(i in 1:1000){
    dat <- Generate(condition)
    res <- Analyse(condition, dat)
    results[i, ] <- res
}
head(results)
```

The matrix stored in `results` contains 1000 parameter estimates returned from each statistic. 
After this is obtained, we can move on to summarising the output through the `Summarise()` function
to obtain average estimates, their associated sampling error, their efficiency, and so on.

```{r}
Summarise(condition, results) 
```

This scheme is then repeated for each row in the `Design` object until the entire simulation study
is complete. 

Of course, `runSimulation()` does much more than this conceptual outline, which is why it exists. Namely, errors and warnings are controlled and tracked, data is re-drawn when needed, parallel processing is supported, debugging is easier with the `debug` input (or by inserting `browser()` directly), temporary and full results can be saved to external files, the simulation state can be saved/restored, build-in safety features are included, and more. The point, however, is that you as the user *should not be bogged down with the nitty-gritty details of setting up the simulation work-flow/features*; instead, you need only focus your time on the important generate-analyse-summarise steps, organized in the body of these function, required to obtain your interesting simulation results.

To access further examples and instructions feel free to visit the [package wiki on Github](https://github.com/philchalmers/SimDesign/wiki)
