---
title: "New methods"
subtitle: "tactile"
author: "Johan Larsson"
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{New methods}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r setup, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.width = 4,
  fig.height = 3.2
)
```

## `xyplot.lm()`: Linear model diagnostics 

tactile provides an `xyplot()` method for `lm` objects -- usually generated
with `stats::lm()` or `stats::glm()`. It provides the same type of diagnostic
plots that `stats::plot.lm()` covers, with some small modifications
This method is provided so that a user may use lattice graphics throughout out a

document, say for a lab report, and produce consistent graphic material that
can be changed on a general level (for instance by using `lattice.options()`).

### Usage

We fit a simple linear model

```{r}
fit <- lm(Sepal.Length ~ Sepal.Width, data = iris)
```

Here, we fist use the default option and plot the diagnostics using 
`stats::plot()`

```{r, fig.cap = "Plot diagnostics with `stats::plot.lm()`.", fig.width = 6, fig.height = 9.5}
par(mfrow = c(3, 2), cex = 1)
plot(fit, which = 1:6)
par(mfrow = c(1, 1))
```

Or instead use the method provided by tactile.

```{r, fig.cap = "Plot diagnostics with `xyplot.lm()`.", fig.width = 6, fig.height = 9.5}
library(tactile)
xyplot(fit, which = 1:6)
```

You will see that the results are similar. The benefits, however, of using
`xyplot.lm()` from tactile are that

* handling of plot margins is much improved,
* the plots are returned as a list of trellis objects (that can be updated),
* general plot settings from `lattice` are respected (mostly), and
* you no longer have to specify `par(mfrow = c(2, 2))`. The plots are arranged
via `gridExtra::grid.arrange()` automatically and arguments `nrow` and `ncol`
have been made accessible in the `xyplot.lm()` call to enable manual
specifications.

## `xyplot.Arima`: ARIMA Model Diagnostics

This function is similar to `xyplot.lm()` but is modeled after
`stats::tsdiag()`. First, we look at the output from the original.

```{r, fig.height = 6, fig.width = 4}
fit <- arima(lh, c(0, 0, 1))
tsdiag(fit)
```

To use the method from tactile, we just call `xyplot()` on the model fit. The
most prominent difference here is that we've added a Q-Q plot of the
standardized residuals as well, but also that we're correcting
the Ljung--Box test to account for the fact that we've fit a model
([see this](http://www.stat.pitt.edu/stoffer/tsa4/Rissues.htm)).

```{r, fig.width = 7, fig.height = 5}
xyplot(fit)
```

## `xyplot.forecast()`: Plotting forecasts with tactile

Robert Hyndman's excellent
[forecast](https://cran.r-project.org/package=forecast) package has built-in
functions for plotting forecasts. These have been reworked to use lattice
graphics in tactile, and also try to place the forecasts on the time
scale of the original data (if such is provided).

For this example, we user `USAccDeaths`, a time series giving the monthly totals
of accidental deaths in the USA. We begin by separating the series into
a training and test set.

```{r}
library(forecast)
train <- window(USAccDeaths, c(1973, 1), c(1977, 12))
test <- window(USAccDeaths, c(1978, 1), c(1978, 12))
```

Then we fit the model and plot the results.

```{r, fig.width = 6, fig.height = 3}
fit <- arima(train, order = c(0, 1, 1), seasonal = list(order = c(0, 1, 1)))
fcast1 <- forecast(fit, 12)
xyplot(fcast1, test,
  grid = TRUE, auto.key = list(corner = c(0, 0.99)),
  ci_key = list(title = "PI Level")
)
```

A so called fan plot can be achieved by increasing the number of prediction
intervals in the call to `forecast`. We also switch to a separate 
color palette by using the `ci_pal` argument.

```{r, fig.width = 6, fig.height = 3}
# A fan plot
fcast2 <- forecast(fit, 12, level = seq(0, 95, 10))
xyplot(fcast2, test, ci_pal = heat.colors(100))
```