Plots a lag plot using ggplot.

## Usage

```
gglagplot(
x,
lags = ifelse(frequency(x) > 9, 16, 9),
set.lags = 1:lags,
diag = TRUE,
diag.col = "gray",
do.lines = TRUE,
colour = TRUE,
continuous = frequency(x) > 12,
labels = FALSE,
seasonal = TRUE,
...
)
gglagchull(
x,
lags = ifelse(frequency(x) > 1, min(12, frequency(x)), 4),
set.lags = 1:lags,
diag = TRUE,
diag.col = "gray",
...
)
```

## Arguments

- x
a time series object (type

`ts`

).- lags
number of lag plots desired, see arg set.lags.

- set.lags
vector of positive integers specifying which lags to use.

- diag
logical indicating if the x=y diagonal should be drawn.

- diag.col
color to be used for the diagonal if(diag).

- do.lines
if TRUE, lines will be drawn, otherwise points will be drawn.

- colour
logical indicating if lines should be coloured.

- continuous
Should the colour scheme for years be continuous or discrete?

- labels
logical indicating if labels should be used.

- seasonal
Should the line colour be based on seasonal characteristics (TRUE), or sequential (FALSE).

- ...
Not used (for consistency with lag.plot)

## Details

“gglagplot” will plot time series against lagged versions of themselves. Helps visualising 'auto-dependence' even when auto-correlations vanish.

“gglagchull” will layer convex hulls of the lags, layered on a single plot. This helps visualise the change in 'auto-dependence' as lags increase.

## Examples

```
gglagplot(woolyrnq)
gglagplot(woolyrnq,seasonal=FALSE)
lungDeaths <- cbind(mdeaths, fdeaths)
gglagplot(lungDeaths, lags=2)
gglagchull(lungDeaths, lags=6)
gglagchull(woolyrnq)
```