running | R Documentation |
Applies a function over subsets of the vector(s) formed by taking a fixed number of previous points.
running(
X,
Y = NULL,
fun = mean,
width = min(length(X), 20),
allow.fewer = FALSE,
pad = FALSE,
align = c("right", "center", "left"),
simplify = TRUE,
by,
...
)
X |
data vector |
Y |
data vector (optional) |
fun |
Function to apply. Default is |
width |
Integer giving the number of vector elements to include in the subsets. Defaults to the lesser of the length of the data and 20 elements. |
allow.fewer |
Boolean indicating whether the function should be
computed for subsets with fewer than |
pad |
Boolean indicating whether the returned results should be
'padded' with NAs corresponding to sets with less than |
align |
One of "right", "center", or "left". This controls the
relative location of ‘short’ subsets with less then |
simplify |
Boolean. If FALSE the returned object will be a list containing one element per evaluation. If TRUE, the returned object will be coerced into a vector (if the computation returns a scalar) or a matrix (if the computation returns multiple values). Defaults to FALSE. |
by |
Integer separation between groups. If |
... |
parameters to be passed to |
running
applies the specified function to a sequential windows on
X
and (optionally) Y
. If Y
is specified the function
must be bivariate.
List (if simplify==TRUE
), vector, or matrix containing the
results of applying the function fun
to the subsets of X
(running
) or X
and Y
.
Note that this function will create a vector or matrix even for objects
which are not simplified by sapply
.
Gregory R. Warnes greg@warnes.net, with contributions by Nitin Jain nitin.jain@pfizer.com.
wapply
to apply a function over an x-y window
centered at each x point, sapply
,
lapply
# show effect of pad
running(1:20, width = 5)
running(1:20, width = 5, pad = TRUE)
# show effect of align
running(1:20, width = 5, align = "left", pad = TRUE)
running(1:20, width = 5, align = "center", pad = TRUE)
running(1:20, width = 5, align = "right", pad = TRUE)
# show effect of simplify
running(1:20, width = 5, fun = function(x) x) # matrix
running(1:20, width = 5, fun = function(x) x, simplify = FALSE) # list
# show effect of by
running(1:20, width = 5) # normal
running(1:20, width = 5, by = 5) # non-overlapping
running(1:20, width = 5, by = 2) # starting every 2nd
# Use 'pad' to ensure correct length of vector, also show the effect
# of allow.fewer.
par(mfrow = c(2, 1))
plot(1:20, running(1:20, width = 5, allow.fewer = FALSE, pad = TRUE), type = "b")
plot(1:20, running(1:20, width = 5, allow.fewer = TRUE, pad = TRUE), type = "b")
par(mfrow = c(1, 1))
# plot running mean and central 2 standard deviation range
# estimated by *last* 40 observations
dat <- rnorm(500, sd = 1 + (1:500) / 500)
plot(dat)
sdfun <- function(x, sign = 1) mean(x) + sign * sqrt(var(x))
lines(running(dat, width = 51, pad = TRUE, fun = mean), col = "blue")
lines(running(dat, width = 51, pad = TRUE, fun = sdfun, sign = -1), col = "red")
lines(running(dat, width = 51, pad = TRUE, fun = sdfun, sign = 1), col = "red")
# plot running correlation estimated by last 40 observations (red)
# against the true local correlation (blue)
sd.Y <- seq(0, 1, length = 500)
X <- rnorm(500, sd = 1)
Y <- rnorm(500, sd = sd.Y)
plot(running(X, X + Y, width = 20, fun = cor, pad = TRUE), col = "red", type = "s")
r <- 1 / sqrt(1 + sd.Y^2) # true cor of (X,X+Y)
lines(r, type = "l", col = "blue")
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