rollapply: Apply Rolling Functions
In zoo: S3 Infrastructure for Regular and Irregular Time Series (Z's Ordered Observations)
rollapply R Documentation
Apply Rolling Functions
Description
A generic function for applying a function to rolling margins of an array.
Usage
rollapply(data, ...)
## S3 method for class 'ts'
rollapply(data, ...)
## S3 method for class 'zoo'
rollapply(data, width, FUN, ..., by = 1, by.column = TRUE,
fill = if (na.pad) NA, na.pad = FALSE, partial = FALSE,
align = c("center", "left", "right"), coredata = TRUE)
## Default S3 method:
rollapply(data, ...)
rollapplyr(..., align = "right")
Arguments
data
the data to be used (representing a series of observations).
width
numeric vector or list. In the simplest case this is an integer
specifying the window width (in numbers of observations) which is aligned
to the original sample according to the align argument. Alternatively,
width can be a list regarded as offsets compared to the current
time, see below for details.
FUN
the function to be applied.
...
optional arguments to FUN.
by
calculate FUN at every by-th time point rather than
every point. by is only used if width is length 1 and either
a plain scalar or a list.
by.column
logical. If TRUE, FUN is applied to each column separately.
fill
a three-component vector or list (recycled otherwise) providing
filling values at the left/within/to the right of the data range.
See the fill argument of na.fill for details.
na.pad
deprecated. Use fill = NA instead of na.pad = TRUE.
partial
logical or numeric. If FALSE (default) then FUN is only
applied when all indexes of the rolling window are within the observed time range.
If TRUE, then the subset of indexes that are in range are passed to FUN.
A numeric argument to partial can be used to determin the minimal
window size for partial computations. See below for more details.
align
specifyies whether the index of the result
should be left- or right-aligned or centered (default) compared
to the rolling window of observations. This argument is only used if
width represents widths.
coredata
logical. Should only the coredata(data)
be passed to every width window? If set to FALSE the
full zoo series is used.
Details
If width is a plain numeric vector its elements are regarded as widths
to be interpreted in conjunction with align whereas if width is a list
its components are regarded as offsets. In the above cases if the length of
width is 1 then width is recycled for every by-th point.
If width is a list its components represent integer offsets such that
the i-th component of the list refers to time points at positions
i + width[[i]]. If any of these points are below 1 or above the
length of index(data) then FUN is not evaluated for that
point unless partial = TRUE and in that case only the valid
points are passed.
The rolling function can also be applied to partial windows by setting partial = TRUE
For example, if width = 3, align = "right" then for the first point
just that point is passed to FUN since the two points to its
left are out of range. For the same example, if partial = FALSE then FUN is not
invoked at all for the first two points. If partial is a numeric then it
specifies the minimum number of offsets that must be within range. Negative
partial is interpreted as FALSE.
If width is a scalar then partial = TRUE and fill = NA are
mutually exclusive but if offsets are specified for the width and 0 is not
among the offsets then the output will be shorter than the input even
if partial = TRUE is specified. In that case it may still be useful
to specify fill in addition to partial.
If FUN is mean, max or median and by.column is
TRUE and width is a plain scalar and there are no other arguments
then special purpose code is used to enhance performance.
Also in the case of mean such special purpose code is only invoked if the
data argument has no NA values.
See rollmean, rollmax and rollmedian
for more details.
Currently, there are methods for "zoo" and "ts" series
and "default" method for ordinary vectors and matrices.
rollapplyr is a wrapper around rollapply that uses a default
of align = "right".
If data is of length 0, data is returned unmodified.
Value
A object of the same class as data with the results of the rolling function.
See Also
rollmean
Examples
suppressWarnings(RNGversion("3.5.0"))
set.seed(1)
## 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)
rr <- 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
## showing the shifts after the oil crisis in Oct 1973
## and after the seatbelt legislation change in Jan 1983
plot(rr)
## rolling mean by time window (e.g., 3 days) rather than
## by number of observations (e.g., when these are unequally spaced):
#
## - test data
tt <- as.Date("2000-01-01") + c(1, 2, 5, 6, 7, 8, 10)
z <- zoo(seq_along(tt), tt)
## - fill it out to a daily series, zm, using NAs
## using a zero width zoo series g on a grid
g <- zoo(, seq(start(z), end(z), "day"))
zm <- merge(z, g)
## - 3-day rolling mean
rollapply(zm, 3, mean, na.rm = TRUE, fill = NA)
##
## - without expansion to regular grid: find interval widths
## that encompass the previous 3 days for each Date
w <- seq_along(tt) - findInterval(tt - 3, tt)
## a solution to computing the widths 'w' that is easier to read but slower
## w <- sapply(tt, function(x) sum(tt >= x - 2 & tt <= x))
##
## - rolling sum from 3-day windows
## without vs. with expansion to regular grid
rollapplyr(z, w, sum)
rollapplyr(zm, 3, sum, partial = TRUE, na.rm = TRUE)
## rolling weekly sums (with some missing dates)
z <- zoo(1:11, as.Date("2016-03-09") + c(0:7, 9:10, 12))
weeksum <- function(z) sum(z[time(z) > max(time(z)) - 7])
zs <- rollapplyr(z, 7, weeksum, fill = NA, coredata = FALSE)
merge(value = z, weeksum = zs)
## replicate cumsum with either 'partial' or vector width 'k'
cumsum(1:10)
rollapplyr(1:10, 10, sum, partial = TRUE)
rollapplyr(1:10, 1:10, sum)
## different values of rule argument
z <- zoo(c(NA, NA, 2, 3, 4, 5, NA))
rollapply(z, 3, sum, na.rm = TRUE)
rollapply(z, 3, sum, na.rm = TRUE, fill = NULL)
rollapply(z, 3, sum, na.rm = TRUE, fill = NA)
rollapply(z, 3, sum, na.rm = TRUE, partial = TRUE)
# this will exclude time points 1 and 2
# It corresponds to align = "right", width = 3
rollapply(zoo(1:8), list(seq(-2, 0)), sum)
# but this will include points 1 and 2
rollapply(zoo(1:8), list(seq(-2, 0)), sum, partial = 1)
rollapply(zoo(1:8), list(seq(-2, 0)), sum, partial = 0)
# so will this
rollapply(zoo(1:8), list(seq(-2, 0)), sum, fill = NA)
# by = 3, align = "right"
L <- rep(list(NULL), 8)
L[seq(3, 8, 3)] <- list(seq(-2, 0))
str(L)
rollapply(zoo(1:8), L, sum)
rollapply(zoo(1:8), list(0:2), sum, fill = 1:3)
rollapply(zoo(1:8), list(0:2), sum, fill = 3)
L2 <- rep(list(-(2:0)), 10)
L2[5] <- list(NULL)
str(L2)
rollapply(zoo(1:10), L2, sum, fill = "extend")
rollapply(zoo(1:10), L2, sum, fill = list("extend", NULL))
rollapply(zoo(1:10), L2, sum, fill = list("extend", NA))
rollapply(zoo(1:10), L2, sum, fill = NA)
rollapply(zoo(1:10), L2, sum, fill = 1:3)
rollapply(zoo(1:10), L2, sum, partial = TRUE)
rollapply(zoo(1:10), L2, sum, partial = TRUE, fill = 99)
rollapply(zoo(1:10), list(-1), sum, partial = 0)
rollapply(zoo(1:10), list(-1), sum, partial = TRUE)
rollapply(zoo(cbind(a = 1:6, b = 11:16)), 3, rowSums, by.column = FALSE)
# these two are the same
rollapply(zoo(cbind(a = 1:6, b = 11:16)), 3, sum)
rollapply(zoo(cbind(a = 1:6, b = 11:16)), 3, colSums, by.column = FALSE)
# these two are the same
rollapply(zoo(1:6), 2, sum, by = 2, align = "right")
aggregate(zoo(1:6), c(2, 2, 4, 4, 6, 6), sum)
# these two are the same
rollapply(zoo(1:3), list(-1), c)
lag(zoo(1:3), -1)
# these two are the same
rollapply(zoo(1:3), list(1), c)
lag(zoo(1:3))
# these two are the same
rollapply(zoo(1:5), list(c(-1, 0, 1)), sum)
rollapply(zoo(1:5), 3, sum)
# these two are the same
rollapply(zoo(1:5), list(0:2), sum)
rollapply(zoo(1:5), 3, sum, align = "left")
# these two are the same
rollapply(zoo(1:5), list(-(2:0)), sum)
rollapply(zoo(1:5), 3, sum, align = "right")
# these two are the same
rollapply(zoo(1:6), list(NULL, NULL, -(2:0)), sum)
rollapply(zoo(1:6), 3, sum, by = 3, align = "right")
# these two are the same
rollapply(zoo(1:5), list(c(-1, 1)), sum)
rollapply(zoo(1:5), 3, function(x) sum(x[-2]))
# these two are the same
rollapply(1:5, 3, rev)
embed(1:5, 3)
# these four are the same
x <- 1:6
rollapply(c(0, 0, x), 3, sum, align = "right") - x
rollapply(x, 3, sum, partial = TRUE, align = "right") - x
rollapply(x, 3, function(x) sum(x[-3]), partial = TRUE, align = "right")
rollapply(x, list(-(2:1)), sum, partial = 0)
# same as Matlab's buffer(x, n, p) for valid non-negative p
# See http://www.mathworks.com/help/toolbox/signal/buffer.html
x <- 1:30; n <- 7; p <- 3
t(rollapply(c(rep(0, p), x, rep(0, n-p)), n, by = n-p, c))
# these three are the same
y <- 10 * seq(8); k <- 4; d <- 2
# 1
# from http://ucfagls.wordpress.com/2011/06/14/embedding-a-time-series-with-time-delay-in-r-part-ii/
Embed <- function(x, m, d = 1, indices = FALSE, as.embed = TRUE) {
n <- length(x) - (m-1)*d
X <- seq_along(x)
if(n <= 0)
stop("Insufficient observations for the requested embedding")
out <- matrix(rep(X[seq_len(n)], m), ncol = m)
out[,-1] <- out[,-1, drop = FALSE] +
rep(seq_len(m - 1) * d, each = nrow(out))
if(as.embed)
out <- out[, rev(seq_len(ncol(out)))]
if(!indices)
out <- matrix(x[out], ncol = m)
out
}
Embed(y, k, d)
# 2
rollapply(y, list(-d * seq(0, k-1)), c)
# 3
rollapply(y, d*k-1, function(x) x[d * seq(k-1, 0) + 1])
## mimic convolve() using rollapplyr()
A <- 1:4
B <- 5:8
## convolve(..., type = "open")
cross <- function(x) x
rollapplyr(c(A, 0*B[-1]), length(B), cross, partial = TRUE)
convolve(A, B, type = "open")
# convolve(..., type = "filter")
rollapplyr(A, length(B), cross)
convolve(A, B, type = "filter")
# weighted sum including partials near ends, keeping
## alignment with wts correct
points <- zoo(cbind(lon = c(11.8300715, 11.8296697,
11.8268708, 11.8267236, 11.8249612, 11.8251062),
lat = c(48.1099048, 48.10884, 48.1067431, 48.1066077,
48.1037673, 48.103318),
dist = c(46.8463805878941, 33.4921440879536, 10.6101735030534,
18.6085009578724, 6.97253109610173, 9.8912817449265)))
mysmooth <- function(z, wts = c(0.3, 0.4, 0.3)) {
notna <- !is.na(z)
sum(z[notna] * wts[notna]) / sum(wts[notna])
}
points2 <- points
points2[, 1:2] <- rollapply(rbind(NA, coredata(points)[, 1:2], NA), 3, mysmooth)
points2
zoo documentation built on June 8, 2023, 6:59 a.m.
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| rollapply | R Documentation |
Apply Rolling Functions
Description
A generic function for applying a function to rolling margins of an array.
Usage
rollapply(data, ...)
## S3 method for class 'ts'
rollapply(data, ...)
## S3 method for class 'zoo'
rollapply(data, width, FUN, ..., by = 1, by.column = TRUE,
fill = if (na.pad) NA, na.pad = FALSE, partial = FALSE,
align = c("center", "left", "right"), coredata = TRUE)
## Default S3 method:
rollapply(data, ...)
rollapplyr(..., align = "right")
Arguments
data |
the data to be used (representing a series of observations). |
width |
numeric vector or list. In the simplest case this is an integer
specifying the window width (in numbers of observations) which is aligned
to the original sample according to the |
FUN |
the function to be applied. |
... |
optional arguments to |
by |
calculate FUN at every |
by.column |
logical. If |
fill |
a three-component vector or list (recycled otherwise) providing
filling values at the left/within/to the right of the data range.
See the |
na.pad |
deprecated. Use |
partial |
logical or numeric. If |
align |
specifyies whether the index of the result
should be left- or right-aligned or centered (default) compared
to the rolling window of observations. This argument is only used if
|
coredata |
logical. Should only the |
Details
If width is a plain numeric vector its elements are regarded as widths
to be interpreted in conjunction with align whereas if width is a list
its components are regarded as offsets. In the above cases if the length of
width is 1 then width is recycled for every by-th point.
If width is a list its components represent integer offsets such that
the i-th component of the list refers to time points at positions
i + width[[i]]. If any of these points are below 1 or above the
length of index(data) then FUN is not evaluated for that
point unless partial = TRUE and in that case only the valid
points are passed.
The rolling function can also be applied to partial windows by setting partial = TRUE
For example, if width = 3, align = "right" then for the first point
just that point is passed to FUN since the two points to its
left are out of range. For the same example, if partial = FALSE then FUN is not
invoked at all for the first two points. If partial is a numeric then it
specifies the minimum number of offsets that must be within range. Negative
partial is interpreted as FALSE.
If width is a scalar then partial = TRUE and fill = NA are
mutually exclusive but if offsets are specified for the width and 0 is not
among the offsets then the output will be shorter than the input even
if partial = TRUE is specified. In that case it may still be useful
to specify fill in addition to partial.
If FUN is mean, max or median and by.column is
TRUE and width is a plain scalar and there are no other arguments
then special purpose code is used to enhance performance.
Also in the case of mean such special purpose code is only invoked if the
data argument has no NA values.
See rollmean, rollmax and rollmedian
for more details.
Currently, there are methods for "zoo" and "ts" series
and "default" method for ordinary vectors and matrices.
rollapplyr is a wrapper around rollapply that uses a default
of align = "right".
If data is of length 0, data is returned unmodified.
Value
A object of the same class as data with the results of the rolling function.
See Also
rollmean
Examples
suppressWarnings(RNGversion("3.5.0"))
set.seed(1)
## 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)
rr <- 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
## showing the shifts after the oil crisis in Oct 1973
## and after the seatbelt legislation change in Jan 1983
plot(rr)
## rolling mean by time window (e.g., 3 days) rather than
## by number of observations (e.g., when these are unequally spaced):
#
## - test data
tt <- as.Date("2000-01-01") + c(1, 2, 5, 6, 7, 8, 10)
z <- zoo(seq_along(tt), tt)
## - fill it out to a daily series, zm, using NAs
## using a zero width zoo series g on a grid
g <- zoo(, seq(start(z), end(z), "day"))
zm <- merge(z, g)
## - 3-day rolling mean
rollapply(zm, 3, mean, na.rm = TRUE, fill = NA)
##
## - without expansion to regular grid: find interval widths
## that encompass the previous 3 days for each Date
w <- seq_along(tt) - findInterval(tt - 3, tt)
## a solution to computing the widths 'w' that is easier to read but slower
## w <- sapply(tt, function(x) sum(tt >= x - 2 & tt <= x))
##
## - rolling sum from 3-day windows
## without vs. with expansion to regular grid
rollapplyr(z, w, sum)
rollapplyr(zm, 3, sum, partial = TRUE, na.rm = TRUE)
## rolling weekly sums (with some missing dates)
z <- zoo(1:11, as.Date("2016-03-09") + c(0:7, 9:10, 12))
weeksum <- function(z) sum(z[time(z) > max(time(z)) - 7])
zs <- rollapplyr(z, 7, weeksum, fill = NA, coredata = FALSE)
merge(value = z, weeksum = zs)
## replicate cumsum with either 'partial' or vector width 'k'
cumsum(1:10)
rollapplyr(1:10, 10, sum, partial = TRUE)
rollapplyr(1:10, 1:10, sum)
## different values of rule argument
z <- zoo(c(NA, NA, 2, 3, 4, 5, NA))
rollapply(z, 3, sum, na.rm = TRUE)
rollapply(z, 3, sum, na.rm = TRUE, fill = NULL)
rollapply(z, 3, sum, na.rm = TRUE, fill = NA)
rollapply(z, 3, sum, na.rm = TRUE, partial = TRUE)
# this will exclude time points 1 and 2
# It corresponds to align = "right", width = 3
rollapply(zoo(1:8), list(seq(-2, 0)), sum)
# but this will include points 1 and 2
rollapply(zoo(1:8), list(seq(-2, 0)), sum, partial = 1)
rollapply(zoo(1:8), list(seq(-2, 0)), sum, partial = 0)
# so will this
rollapply(zoo(1:8), list(seq(-2, 0)), sum, fill = NA)
# by = 3, align = "right"
L <- rep(list(NULL), 8)
L[seq(3, 8, 3)] <- list(seq(-2, 0))
str(L)
rollapply(zoo(1:8), L, sum)
rollapply(zoo(1:8), list(0:2), sum, fill = 1:3)
rollapply(zoo(1:8), list(0:2), sum, fill = 3)
L2 <- rep(list(-(2:0)), 10)
L2[5] <- list(NULL)
str(L2)
rollapply(zoo(1:10), L2, sum, fill = "extend")
rollapply(zoo(1:10), L2, sum, fill = list("extend", NULL))
rollapply(zoo(1:10), L2, sum, fill = list("extend", NA))
rollapply(zoo(1:10), L2, sum, fill = NA)
rollapply(zoo(1:10), L2, sum, fill = 1:3)
rollapply(zoo(1:10), L2, sum, partial = TRUE)
rollapply(zoo(1:10), L2, sum, partial = TRUE, fill = 99)
rollapply(zoo(1:10), list(-1), sum, partial = 0)
rollapply(zoo(1:10), list(-1), sum, partial = TRUE)
rollapply(zoo(cbind(a = 1:6, b = 11:16)), 3, rowSums, by.column = FALSE)
# these two are the same
rollapply(zoo(cbind(a = 1:6, b = 11:16)), 3, sum)
rollapply(zoo(cbind(a = 1:6, b = 11:16)), 3, colSums, by.column = FALSE)
# these two are the same
rollapply(zoo(1:6), 2, sum, by = 2, align = "right")
aggregate(zoo(1:6), c(2, 2, 4, 4, 6, 6), sum)
# these two are the same
rollapply(zoo(1:3), list(-1), c)
lag(zoo(1:3), -1)
# these two are the same
rollapply(zoo(1:3), list(1), c)
lag(zoo(1:3))
# these two are the same
rollapply(zoo(1:5), list(c(-1, 0, 1)), sum)
rollapply(zoo(1:5), 3, sum)
# these two are the same
rollapply(zoo(1:5), list(0:2), sum)
rollapply(zoo(1:5), 3, sum, align = "left")
# these two are the same
rollapply(zoo(1:5), list(-(2:0)), sum)
rollapply(zoo(1:5), 3, sum, align = "right")
# these two are the same
rollapply(zoo(1:6), list(NULL, NULL, -(2:0)), sum)
rollapply(zoo(1:6), 3, sum, by = 3, align = "right")
# these two are the same
rollapply(zoo(1:5), list(c(-1, 1)), sum)
rollapply(zoo(1:5), 3, function(x) sum(x[-2]))
# these two are the same
rollapply(1:5, 3, rev)
embed(1:5, 3)
# these four are the same
x <- 1:6
rollapply(c(0, 0, x), 3, sum, align = "right") - x
rollapply(x, 3, sum, partial = TRUE, align = "right") - x
rollapply(x, 3, function(x) sum(x[-3]), partial = TRUE, align = "right")
rollapply(x, list(-(2:1)), sum, partial = 0)
# same as Matlab's buffer(x, n, p) for valid non-negative p
# See http://www.mathworks.com/help/toolbox/signal/buffer.html
x <- 1:30; n <- 7; p <- 3
t(rollapply(c(rep(0, p), x, rep(0, n-p)), n, by = n-p, c))
# these three are the same
y <- 10 * seq(8); k <- 4; d <- 2
# 1
# from http://ucfagls.wordpress.com/2011/06/14/embedding-a-time-series-with-time-delay-in-r-part-ii/
Embed <- function(x, m, d = 1, indices = FALSE, as.embed = TRUE) {
n <- length(x) - (m-1)*d
X <- seq_along(x)
if(n <= 0)
stop("Insufficient observations for the requested embedding")
out <- matrix(rep(X[seq_len(n)], m), ncol = m)
out[,-1] <- out[,-1, drop = FALSE] +
rep(seq_len(m - 1) * d, each = nrow(out))
if(as.embed)
out <- out[, rev(seq_len(ncol(out)))]
if(!indices)
out <- matrix(x[out], ncol = m)
out
}
Embed(y, k, d)
# 2
rollapply(y, list(-d * seq(0, k-1)), c)
# 3
rollapply(y, d*k-1, function(x) x[d * seq(k-1, 0) + 1])
## mimic convolve() using rollapplyr()
A <- 1:4
B <- 5:8
## convolve(..., type = "open")
cross <- function(x) x
rollapplyr(c(A, 0*B[-1]), length(B), cross, partial = TRUE)
convolve(A, B, type = "open")
# convolve(..., type = "filter")
rollapplyr(A, length(B), cross)
convolve(A, B, type = "filter")
# weighted sum including partials near ends, keeping
## alignment with wts correct
points <- zoo(cbind(lon = c(11.8300715, 11.8296697,
11.8268708, 11.8267236, 11.8249612, 11.8251062),
lat = c(48.1099048, 48.10884, 48.1067431, 48.1066077,
48.1037673, 48.103318),
dist = c(46.8463805878941, 33.4921440879536, 10.6101735030534,
18.6085009578724, 6.97253109610173, 9.8912817449265)))
mysmooth <- function(z, wts = c(0.3, 0.4, 0.3)) {
notna <- !is.na(z)
sum(z[notna] * wts[notna]) / sum(wts[notna])
}
points2 <- points
points2[, 1:2] <- rollapply(rbind(NA, coredata(points)[, 1:2], NA), 3, mysmooth)
points2
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