---
title: "Introduction to bqmm"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Introduction to bqmm}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(collapse = TRUE, comment = "#>", eval = FALSE)
```

`bqmm` fits Bayesian mixed-effects quantile regression using the asymmetric
Laplace working likelihood and Stan.

## A first model

```{r}
library(bqmm)

fit <- bqmm(distance ~ age + (1 | Subject),
            data = nlme::Orthodont,
            tau  = 0.5)

summary(fit)
```

## Several quantiles at once

Passing a vector of quantiles fits each one and returns a `bqmm_multi`:

```{r}
fit3 <- bqmm(distance ~ age + (1 | Subject),
             data = nlme::Orthodont,
             tau  = c(0.1, 0.5, 0.9))

coef(fit3)      # tau-by-coefficient matrix
plot(fit3)      # coefficient-versus-tau paths
```

## Valid inference

By default `bqmm` applies the Yang, Wang and He (2016) correction so that
fixed-effect intervals are asymptotically valid despite the misspecified
asymmetric Laplace likelihood:

```{r}
vcov(fit, adjusted = TRUE)
vcov(fit, adjusted = FALSE)   # naive posterior covariance
```

See `vignette("bqmm-inference")` for details.