---
title: "Simulating data"
author: ""
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Simulating data}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r set-knitr-options, cache=FALSE, echo=FALSE}
library("knitr")
opts_chunk$set(message = FALSE, warning=FALSE, fig.width = 5.5)
```

Let's load the necessary packages:

```{r, message=FALSE, warning=FALSE}
library(zoid)
```

## Simulating data

We will use the "broken stick" approach to simulate data from the Dirichlet - trinomial model. This model assumes that the group proportions for each observation are Dirichlet, but the observed values are either 0, the total sample size (N) or a number between 0 and N.

Our `broken_stick` function can be called as follows,

```{r}
y = broken_stick(n_obs = 10,
                        n_groups = 10,
                        tot_n = 100)
```

The object `y` is a list with 2 elements, (1) the true underlying compositions (p) and the realized data (X_obs). They can be accessed as 

```{r eval=FALSE}
y$p
y$X_obs
```

By default, the simulation function assumes a uniform prior for the Dirichlet, with hyperparameters = 1. We can change this by specifying our own values of hyperparameters. Using the argument `p`, we can simulate new values with a slightly larger effective sample size, and pass that into `broken_stick`

```{r}
p = gtools::rdirichlet(1, alpha = rep(2,10))

y = broken_stick(n_obs = 10,
                        n_groups = 10,
                        tot_n = 100,
                 p = p)
```
