R: Rolling cross-correlation at given lags.

rollccf {hydromad}

R Documentation

Rolling cross-correlation at given lags.

Description

Rolling cross-correlation at given lags. Can be useful to show how the relationship between two time series changes over time, including out-by-one timing errors.

Usage

rollccf(DATA = data.frame(Q = , P = ),
        width = list(365, 90),
        by = 28,
        lags = base.lag + c(0, 1, -1),
        base.lag = estimateDelay(DATA, rises = rises, plot = FALSE),
        rises = FALSE,
        na.action = na.contiguous,
        na.max.fraction = 1/3)

## S3 method for class 'rollccf'
xyplot(x, data = NULL, ...,
       with.data = TRUE,
       type = list(c("h", "b")),
       type.data = "l",
       par.settings = simpleTheme(pch = ".", cex = 2),
       layout = c(1, length(x$rolls) + with.data * 2),
       strip = strip.default,
       ylim = c(0, 1), xlab = NULL, as.table = TRUE)

Arguments

`DATA`	a named list, data frame, time series or zoo object containing the two data series.
`width`	a list or number specifying the width of window(s), in time steps, in which to calculate cross correlations.
`by`	temporal resolution: cross correlation is calculated in windows spaced every `by` time steps.
`lags, base.lag`	`lags` for which to calculate the cross correlation. By default these are based on the overall maximum cross correlation, `base.lag`.
`rises`	if `TRUE`, compute the cross correlation with rises in streamflow. In this case the streamflow series must be named `"Q"`.
`na.action`	function to handle missing data in each window (not the whole series). This is only applied when the number of missing values is less than `na.max.fraction`. Could be `na.exclude`.
`na.max.fraction`	if the proportion of missing values in the moving window exceeds this value, the corresponding result will be `NA`.
`x, data`	`x` is an object produced by the `rollccf` function. `data` is ignored.
`with.data`	if `TRUE`, include the original data series in the plot.
`type, type.data`	drawing styles for the cross correlation series and input data series. See `panel.xyplot`.
`par.settings, layout, strip, ylim, xlab, as.table, ...`	passed to `xyplot`.

Details

This is a fairly straightforward application of rollapply with the ccf function. It may be better to do a time-varying regression between the two series.

Value

rollccf returns a list of class "rollccf", with components:

`rolls`	a list of time series of cross correlations. One element for each value of `width`.
`data`	time series input data.
`lags, width, call`	values of arguments used.

Author(s)

Felix Andrews felix@nfrac.org

Examples

data(Canning)
foo <- rollccf(Canning)
xyplot(foo)

[Package hydromad version 0.9-18 Index]