running {gtools} | 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")