runquantile | R Documentation |
Moving (aka running, rolling) Window Quantile calculated over a vector
runquantile(x, k, probs, type=7,
endrule=c("quantile", "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 |
endrule |
character string indicating how the values at the beginning
and the end, of the array, should be treated. Only first and last
Similar to |
probs |
numeric vector of probabilities with values in [0,1] range
used by |
type |
an integer between 1 and 9 selecting one of the nine quantile
algorithms, same as |
align |
specifies whether result should be centered (default),
left-aligned or right-aligned. If |
Apart from the end values, the result of y = runquantile(x, k) is the same as
“for(j=(1+k2):(n-k2)) y[j]=quintile(x[(j-k2):(j+k2)],na.rm = TRUE)
”. It can handle
non-finite numbers like NaN's and Inf's (like quantile(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. Relative
speeds of runquantile
is O(n*k)
Functions runquantile
and runmad
are using insertion sort to
sort the moving window, but gain speed by remembering results of the previous
sort. Since each time the window is moved, only one point changes, all but one
points in the window are already sorted. Insertion sort can fix that in O(k)
time.
If x
is a matrix than function runquantile
returns a matrix of
size [n \times
length
(probs)]. If x
is vactor
a than function runquantile
returns a matrix of size
[dim
(x) \times
length
(probs)].
If endrule="trim"
the output will have fewer rows.
Jarek Tuszynski (SAIC) jaroslaw.w.tuszynski@saic.com
About quantiles: Hyndman, R. J. and Fan, Y. (1996) Sample quantiles in statistical packages, American Statistician, 50, 361.
About quantiles: Eric W. Weisstein. Quantile. From MathWorld– A Wolfram Web Resource. http://mathworld.wolfram.com/Quantile.html
About insertion sort used in runmad
and runquantile
:
R. Sedgewick (1988): Algorithms. Addison-Wesley (page 99)
Links related to:
Running Quantile - quantile
, runmed
,
smooth
, rollmedian
from
zoo library
Other moving window functions from this package: runmin
,
runmax
, runmean
, runmad
and
runsd
Running Minimum - min
Running Maximum - max
, rollmax
from zoo library
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 plot using runquantile
k=31; n=200;
x = rnorm(n,sd=30) + abs(seq(n)-n/4)
y=runquantile(x, k, probs=c(0.05, 0.25, 0.5, 0.75, 0.95))
col = c("black", "red", "green", "blue", "magenta", "cyan")
plot(x, col=col[1], main = "Moving Window Quantiles")
lines(y[,1], col=col[2])
lines(y[,2], col=col[3])
lines(y[,3], col=col[4])
lines(y[,4], col=col[5])
lines(y[,5], col=col[6])
lab = c("data", "runquantile(.05)", "runquantile(.25)", "runquantile(0.5)",
"runquantile(.75)", "runquantile(.95)")
legend(0,230, lab, col=col, lty=1 )
# show plot using runquantile
k=15; n=200;
x = rnorm(n,sd=30) + abs(seq(n)-n/4)
y=runquantile(x, k, probs=c(0.05, 0.25, 0.5, 0.75, 0.95))
col = c("black", "red", "green", "blue", "magenta", "cyan")
plot(x, col=col[1], main = "Moving Window Quantiles (smoothed)")
lines(runmean(y[,1],k), col=col[2])
lines(runmean(y[,2],k), col=col[3])
lines(runmean(y[,3],k), col=col[4])
lines(runmean(y[,4],k), col=col[5])
lines(runmean(y[,5],k), col=col[6])
lab = c("data", "runquantile(.05)", "runquantile(.25)", "runquantile(0.5)",
"runquantile(.75)", "runquantile(.95)")
legend(0,230, lab, col=col, lty=1 )
# basic tests against runmin & runmax
y = runquantile(x, k, probs=c(0, 1))
a = runmin(x,k) # test only the inner part
stopifnot(all(a==y[,1], na.rm=TRUE));
a = runmax(x,k) # test only the inner part
stopifnot(all(a==y[,2], na.rm=TRUE));
# basic tests against runmed, including testing endrules
a = runquantile(x, k, probs=0.5, endrule="keep")
b = runmed(x, k, endrule="keep")
stopifnot(all(a==b, na.rm=TRUE));
a = runquantile(x, k, probs=0.5, endrule="constant")
b = runmed(x, k, endrule="constant")
stopifnot(all(a==b, na.rm=TRUE));
# basic tests against apply/embed
a = runquantile(x,k, c(0.3, 0.7), endrule="trim")
b = t(apply(embed(x,k), 1, quantile, probs = c(0.3, 0.7)))
eps = .Machine$double.eps ^ 0.5
stopifnot(all(abs(a-b)<eps));
# test against loop approach
# this test works fine at the R prompt but fails during package check - need to investigate
k=25; n=200;
x = rnorm(n,sd=30) + abs(seq(n)-n/4) # create random data
x[seq(1,n,11)] = NaN; # add NANs
k2 = k
k1 = k-k2-1
a = runquantile(x, k, probs=c(0.3, 0.8) )
b = matrix(0,n,2);
for(j in 1:n) {
lo = max(1, j-k1)
hi = min(n, j+k2)
b[j,] = quantile(x[lo:hi], probs=c(0.3, 0.8), na.rm = TRUE)
}
#stopifnot(all(abs(a-b)<eps));
# compare calculation of array ends
a = runquantile(x, k, probs=0.4, endrule="quantile") # fast C code
b = runquantile(x, k, probs=0.4, endrule="func") # slow R code
stopifnot(all(abs(a-b)<eps));
# test if moving windows forward and backward gives the same results
k=51;
a = runquantile(x , k, probs=0.4)
b = runquantile(x[n:1], k, probs=0.4)
stopifnot(all(a[n:1]==b, na.rm=TRUE));
# test vector vs. matrix inputs, especially for the edge handling
nRow=200; k=25; nCol=10
x = rnorm(nRow,sd=30) + abs(seq(nRow)-n/4)
x[seq(1,nRow,10)] = NaN; # add NANs
X = matrix(rep(x, nCol ), nRow, nCol) # replicate x in columns of X
a = runquantile(x, k, probs=0.6)
b = runquantile(X, k, probs=0.6)
stopifnot(all(abs(a-b[,1])<eps)); # vector vs. 2D array
stopifnot(all(abs(b[,1]-b[,nCol])<eps)); # compare rows within 2D array
# Exhaustive testing of runquantile to standard R approach
numeric.test = function (x, k) {
probs=c(1, 25, 50, 75, 99)/100
a = runquantile(x,k, c(0.3, 0.7), endrule="trim")
b = t(apply(embed(x,k), 1, quantile, probs = c(0.3, 0.7), na.rm=TRUE))
eps = .Machine$double.eps ^ 0.5
stopifnot(all(abs(a-b)<eps));
}
n=50;
x = rnorm(n,sd=30) + abs(seq(n)-n/4) # nice behaving data
for(i in 2:5) numeric.test(x, i) # test small window sizes
for(i in 1:5) numeric.test(x, n-i+1) # test large window size
x[seq(1,50,10)] = NaN; # add NANs and repet the test
for(i in 2:5) numeric.test(x, i) # test small window sizes
for(i in 1:5) numeric.test(x, n-i+1) # test large window size
# Speed comparison
## Not run:
x=runif(1e6); k=1e3+1;
system.time(runquantile(x,k,0.5)) # Speed O(n*k)
system.time(runmed(x,k)) # Speed O(n * log(k))
## End(Not run)
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