runsd {caTools} | R Documentation |
Moving (aka running, rolling) Window's Standard Deviation calculated over a vector
runsd(x, k, center = runmean(x,k), endrule=c("sd", "NA", "trim", "keep", "constant", "func"), align = c("center", "left", "right"))
x |
numeric vector of length n or matrix with n rows. If |
k |
width of moving window; must be an integer between one and n. In case
of even k's one will have to provide different |
endrule |
character string indicating how the values at the beginning
and the end, of the data, should be treated. Only first and last
Similar to |
center |
moving window center. Defaults
to running mean ( |
align |
specifies whether result should be centered (default),
left-aligned or right-aligned. If |
Apart from the end values, the result of y = runmad(x, k) is the same as
“for(j=(1+k2):(n-k2)) y[j]=sd(x[(j-k2):(j+k2)], na.rm = TRUE)
”. It can handle
non-finite numbers like NaN's and Inf's (like mean(x, na.rm = TRUE)
).
The main incentive to write this set of functions was relative slowness of
majority of moving window functions available in R and its packages. With the
exception of runmed
, a running window median function, all
functions listed in "see also" section are slower than very inefficient
“apply(embed(x,k),1,FUN)
” approach.
Returns a numeric vector or matrix of the same size as x
. Only in case of
endrule="trim"
the output vectors will be shorter and output matrices
will have fewer rows.
Jarek Tuszynski (SAIC) jaroslaw.w.tuszynski@saic.com
Links related to:
runsd
- sd
Other moving window functions from this package: runmin
,
runmax
, runquantile
, runmad
and
runmean
generic running window functions: apply
(embed(x,k), 1, FUN)
(fastest), running
from gtools
package (extremely slow for this purpose), subsums
from
magic library can perform running window operations on data with any
dimensions.
# show runmed function k=25; n=200; x = rnorm(n,sd=30) + abs(seq(n)-n/4) col = c("black", "red", "green") m=runmean(x, k) y=runsd(x, k, center=m) plot(x, col=col[1], main = "Moving Window Analysis Functions") lines(m , col=col[2]) lines(m-y/2, col=col[3]) lines(m+y/2, col=col[3]) lab = c("data", "runmean", "runmean-runsd/2", "runmean+runsd/2") legend(0,0.9*n, lab, col=col, lty=1 ) # basic tests against apply/embed eps = .Machine$double.eps ^ 0.5 k=25 # odd size window a = runsd(x,k, endrule="trim") b = apply(embed(x,k), 1, sd) stopifnot(all(abs(a-b)<eps)); k=24 # even size window a = runsd(x,k, endrule="trim") b = apply(embed(x,k), 1, sd) stopifnot(all(abs(a-b)<eps)); # test against loop approach # this test works fine at the R prompt but fails during package check - need to investigate k=25; n=200; x = rnorm(n,sd=30) + abs(seq(n)-n/4) # create random data x[seq(1,n,11)] = NaN; # add NANs k2 = k k1 = k-k2-1 a = runsd(x, k) b = array(0,n) for(j in 1:n) { lo = max(1, j-k1) hi = min(n, j+k2) b[j] = sd(x[lo:hi], na.rm = TRUE) } #stopifnot(all(abs(a-b)<eps)); # compare calculation at array ends k=25; n=100; x = rnorm(n,sd=30) + abs(seq(n)-n/4) a = runsd(x, k, endrule="sd" ) # fast C code b = runsd(x, k, endrule="func") # slow R code stopifnot(all(abs(a-b)<eps)); # test if moving windows forward and backward gives the same results k=51; a = runsd(x , k) b = runsd(x[n:1], k) stopifnot(all(abs(a[n:1]-b)<eps)); # test vector vs. matrix inputs, especially for the edge handling nRow=200; k=25; nCol=10 x = rnorm(nRow,sd=30) + abs(seq(nRow)-n/4) x[seq(1,nRow,10)] = NaN; # add NANs X = matrix(rep(x, nCol ), nRow, nCol) # replicate x in columns of X a = runsd(x, k) b = runsd(X, k) stopifnot(all(abs(a-b[,1])<eps)); # vector vs. 2D array stopifnot(all(abs(b[,1]-b[,nCol])<eps)); # compare rows within 2D array # speed comparison ## Not run: x=runif(1e5); k=51; # reduce vector and window sizes system.time(runsd( x,k,endrule="trim")) system.time(apply(embed(x,k), 1, sd)) ## End(Not run)