library("knitr") opts_chunk$set(message = FALSE, warning=FALSE, fig.width = 5.5) build = "cran" if(build=="cran") { draws=100 iter = 3 } else { draws=1000 iter = 10 }

Let's load the necessary packages:

```
library(zoid)
```

This vignette is designed to illustrate how to build priors for the estimated compositions, $\textbf{p}$. When covariates are included in the design matrix, all elements of $\textbf{p}$ in the Dirichlet regression (both intercepts and slopes or offsets) are assigned $\sim N(0,1)$ priors. This approach is similar to the improper priors used in `brms::brm()`

. These priors may be weakly informative, and the user may wish to change the standard deviation -- which can be done with the `prior_sd`

argument in `fit_zoid()`

.

fit <- fit_zoid(data, prior_sd = 2)

A more familiar approach may be to work with Dirichlet priors. We can adjust the standard deviation in our Normal priors to match the Dirichlet. The helper function for this uses `optim`

to minimize the RMSPE between the observed and target values. For example, if we had 8 bins and wanted to find the Dirichlet prior that would correspond to hyperparamters $(\alpha)=1$, we could call the `fit_prior`

function.

set.seed(123) sd = fit_prior(n_bins = 8, n_draws = draws, target = 1, iterations=iter)

The `sd`

object is a list that contains (1) the estimated standard deviation, (2) the value of the objective function at convergence, and (3) whether or not convergence occurred (anything other than 0 is problematic). The value of the standard deviation here in `sd$sd`

is 1.200453.

So in this case, a standard deviation of ~ 1.20 yields a prior equivalent to a $\sim Dirichlet(1)$ prior. This new value can then be entered into our model with the `prior_sd`

argument,

fit <- fit_zoid(data, prior_sd = 1.2)

**Any scripts or data that you put into this service are public.**

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.