R: Time series lag ggplots

gglagplot {forecast} | R Documentation

## Time series lag ggplots

### Description

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.

### Value

None.

### Author(s)

Mitchell O'Hara-Wild

### See Also

`lag.plot`

### Examples

    gglagplot(woolyrnq)
    gglagplot(woolyrnq,seasonal=FALSE)

    lungDeaths <- cbind(mdeaths, fdeaths)
    gglagplot(lungDeaths, lags=2)
    gglagchull(lungDeaths, lags=6)

    gglagchull(woolyrnq)

[Package forecast version 8.2 Index]