---
title: "Getting started with gratia"
output: 
  rmarkdown::html_vignette:
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    fig_height: 5.3333
    dev: "png"
vignette: >
  %\VignetteIndexEntry{Getting started with gratia}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

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

```{r setup}
library("gratia")
library("mgcv")
```

gratia is a package to make working with generalized additive models (GAMs) in R
easier, including producing plots of estimated smooths using the ggplot2
&#128230;.

This introduction will cover some of the basic functionality of gratia to get
you started. We'll work with some classic simulated data often used to
illustrate properties of GAMs

```{r data-sim}
df <- data_sim("eg1", seed = 42)
df
```

and the following GAM

```{r fit-gam}
m <- gam(y ~ s(x0) + s(x1) + s(x2) + s(x3), data = df, method = "REML")
summary(m)
```

## Plotting

gratia provides the `draw()` function to produce plots using the ggplot2
&#128230;. To draw the estimated smooths from the GAM we fitted above, use

```{r draw-gam}
draw(m)
```

This is intended as reasonable overview of the estimated model, but it offers
limited option to modify the resulting plot. If you want full control, you can
obtain the data used to create the plot above with `smooth_estimates()`

```{r smooth-estimates}
sm <- smooth_estimates(m)
sm
```

which will evaluate all smooths are unevenly spaced values over the range of the
covariate(s). If you want to evaluate only selected smooths, you can specify
which via the `smooth` argument. This takes the *smooth labels* which are the
names of the smooths as they are known to mgcv. To list the labels for the
smooths in use
```{r smooths}
smooths(m)
```
To evaluate only $f(x_2)$ use
```{r smooth-estimates-x2}
sm <- smooth_estimates(m, smooth = "s(x2)")
sm
```
Then you can generate your own plot using the ggplot2 package, for example
```{r ggplot-smooth}
library("ggplot2")
library("dplyr")
sm |>
  add_confint() |>
  ggplot(aes(y = .estimate, x = x2)) +
  geom_ribbon(aes(ymin = .lower_ci, ymax = .upper_ci),
    alpha = 0.2, fill = "forestgreen"
  ) +
  geom_line(colour = "forestgreen", linewidth = 1.5) +
  labs(
    y = "Partial effect",
    title = expression("Partial effect of" ~ f(x[2])),
    x = expression(x[2])
  )
```

## Model diagnostics

The `appraise()` function provides standard diagnostic plots for GAMs

```{r appraise}
appraise(m)
```

The plots produced are (from left-to-right, top-to-bottom),

* a quantile-quantile (QQ) plot of deviance residuals,
* a scatterplot of deviance residuals against the linear predictor,
* a histogram of deviance residuals, and
* a scatterplot of observed vs fitted values.

Adding partial residuals to the partial effect plots produced by `draw()`
can also help diagnose problems with the model, such as oversmoothing
```{r draw-partial-residuals}
draw(m, residuals = TRUE)
```

## Want to learn more?

*gratia* is in very active development and an area of development that is
currently lacking is documentation. To find out more about the package, look at
the [help pages for the package](https://gavinsimpson.github.io/gratia/reference/index.html)
and look at the examples for more code to help you get going.
