Apply Rolling Functions
A generic function for applying a function to rolling margins of an array.
rollapply(data, width, FUN, ..., by = 1, ascending = TRUE, by.column = TRUE, na.pad = FALSE, align = c("center", "left", "right"))
- the data to be used (representing a series of observations).
- number of points per group.
- the function to be applied.
In the case of functions like
%*%, etc., the function name must be quoted.
- optional arguments to
- calculate FUN for trailing width points at every by-th time point.
- logical. If TRUE then points are passed to
FUNin ascending order of time; otherwise, they are passed in descending order.
- logical. If
FUNis applied to each column separately.
- logical. If
TRUEthen additional elements or rows of
NAs are added so that result has same number of elements or rows as
- character specifying whether the index of the result should be left- or right-aligned or centered (default) compared to the rolling window of observations.
Groups time points in successive sets of
width time points and
FUN to the corresponding values. If
TRUE and there are no extra arguments
then special purpose code is used to enhance performance.
for more details.
Currently, there are methods for
In previous versions, this function was called
rapply. It was renamed
because from R 2.4.0 on, base R provides a different function
for recursive (and not rolling) application of functions.
- A object of the same class as
datawith the results of the rolling function.
## rolling mean z <- zoo(11:15, as.Date(31:35)) rollapply(z, 2, mean) ## non-overlapping means z2 <- zoo(rnorm(6)) rollapply(z2, 3, mean, by = 3) # means of nonoverlapping groups of 3 aggregate(z2, c(3,3,3,6,6,6), mean) # same ## optimized vs. customized versions rollapply(z2, 3, mean) # uses rollmean which is optimized for mean rollmean(z2, 3) # same rollapply(z2, 3, (mean)) # does not use rollmean ## rolling regression: ## set up multivariate zoo series with ## number of UK driver deaths and lags 1 and 12 seat <- as.zoo(log(UKDriverDeaths)) time(seat) <- as.yearmon(time(seat)) seat <- merge(y = seat, y1 = lag(seat, k = -1), y12 = lag(seat, k = -12), all = FALSE) ## run a rolling regression with a 3-year time window ## (similar to a SARIMA(1,0,0)(1,0,0)_12 fitted by OLS) fm <- rollapply(seat, width = 36, FUN = function(z) coef(lm(y ~ y1 + y12, data = as.data.frame(z))), by.column = FALSE, align = "right") ## plot the changes in coefficients plot(fm) ## showing the shifts after the oil crisis in Oct 1973 ## and after the seatbelt legislation change in Jan 1983