rolling window

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I want to do a rolling window calculation for multivariate data. In other
words: rollFun in fMultivar makes univariate calculations, ie, given for
example mydata[1:5], mydata[2:6], ? I get one number (the function applied
to mydata[1:5]) every time. rollingFunction in PerformanceAnalytics does the
same but for possibly multivariate data, but one column at a time. rapply in
zoo also does rolling window for univariate data (at least, all examples are
with univariate data).
rollapply (which was called rapply in old versions of zoo but was renamed
to avoid collisions with a new function of the same name in the core of R)
in zoo can handle multivariate zoo series either one column at a time (default)
or combined (via by.column = FALSE):
library(zoo)
z <- zoo(matrix(1:24, 12))
z
1   1 13
2   2 14
3   3 15
4   4 16
5   5 17
6   6 18
7   7 19
8   8 20
9   9 21
10 10 22
11 11 23
12 12 24
rollapply(z, 3, mean, by.column = TRUE) # can omit by.column here
2   2 14
3   3 15
4   4 16
5   5 17
6   6 18
7   7 19
8   8 20
9   9 21
10 10 22
11 11 23
rollapply(z, 3, mean, by.column = FALSE)
2  3  4  5  6  7  8  9 10 11
 8  9 10 11 12 13 14 15 16 17
On 7/19/07, Jordi Molins <jordi.molins.coronado at gmail.com> wrote:
I want to do a rolling window calculation for multivariate data. In other
words: rollFun in fMultivar makes univariate calculations, ie, given for
example mydata[1:5], mydata[2:6], ? I get one number (the function applied
to mydata[1:5]) every time. rollingFunction in PerformanceAnalytics does the
same but for possibly multivariate data, but one column at a time. rapply in
zoo also does rolling window for univariate data (at least, all examples are
with univariate data).
rollapply (which was called rapply in old versions of zoo but was renamed
to avoid collisions with a new function of the same name in the core of R)
in zoo can handle multivariate zoo series either one column at a time (default)
or combined (via by.column = FALSE):
the parameter '...' in rollingFunction() will allow you to pass in the 
by.column parameter described by Gabor.

   - Brian
As a follow-up to Gabor's comment. A more elaborate illustration might be
a rolling regression, e.g.,

  ## 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

Maybe we should add such an example to the rollaply() man page?

Best,
Z

On 7/19/07, Jordi Molins <jordi.molins.coronado at gmail.com> wrote:
I want to do a rolling window calculation for multivariate data. In other
words: rollFun in fMultivar makes univariate calculations, ie, given for
example mydata[1:5], mydata[2:6], 
I get one number (the function applied
to mydata[1:5]) every time. rollingFunction in PerformanceAnalytics does the
same but for possibly multivariate data, but one column at a time. rapply in
zoo also does rolling window for univariate data (at least, all examples are
with univariate data).
rollapply (which was called rapply in old versions of zoo but was renamed
to avoid collisions with a new function of the same name in the core of R)
in zoo can handle multivariate zoo series either one column at a time (default)
or combined (via by.column = FALSE):

library(zoo)
z <- zoo(matrix(1:24, 12))
z
1   1 13
2   2 14
3   3 15
4   4 16
5   5 17
6   6 18
7   7 19
8   8 20
9   9 21
10 10 22
11 11 23
12 12 24
rollapply(z, 3, mean, by.column = TRUE) # can omit by.column here
2   2 14
3   3 15
4   4 16
5   5 17
6   6 18
7   7 19
8   8 20
9   9 21
10 10 22
11 11 23
rollapply(z, 3, mean, by.column = FALSE)
 2  3  4  5  6  7  8  9 10 11
 8  9 10 11 12 13 14 15 16 17

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