`xyplot.lm()`

: Linear model diagnosticstactile 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 modificiations.

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()`

).

We fit a simple linear model

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

Or instead use the method provided by tactile.

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 DiagnosticsThis function is similar to `xyplot.lm()`

but is modelled after `stats::tsdiag()`

. First, we look at the output from the original.

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 the Ljung–Box test to account for the fact that we’ve fit a model (see this).

`xyplot.forecast()`

: Plotting forecasts with tactileRobert Hyndman’s excellent 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.

```
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.

```
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.