.runquantile {diveMove}

Quantile of Moving Window

Description

Moving (aka running, rolling) Window Quantile calculated over a vector

Usage

.runquantile(
  x,
  k,
  probs,
  type = 7,
  endrule = c("quantile", "NA", "trim", "keep", "constant", "func"),
  align = c("center", "left", "right")
)

Arguments

x

numeric vector of length n or matrix with n rows. If x is a matrix than each column will be processed separately.

k

width of moving window; must be an integer between one and n.

probs

numeric vector of probabilities with values in [0,1] range used by runquantile.

type

an integer between 1 and 9 selecting one of the nine quantile algorithms, same as type in quantile function. Another even more readable description of nine ways to calculate quantiles can be found at http://mathworld.wolfram.com/Quantile.html.

endrule

character string indicating how the values at the beginning and the end, of the array, should be treated. Only first and last k2 values at both ends are affected, where k2 is the half-bandwidth k2 = k %/% 2.

* "quantile" Applies the quantile function to smaller and smaller sections of the array. Equivalent to: for(i in 1:k2) out[i]=quantile(x[1:(i+k2)]). * "trim" Trim the ends; output array length is equal to length(x)-2*k2 (out = out[(k2+1):(n-k2)]). This option mimics output of apply (embed(x,k),1,FUN) and other related functions. * "keep" Fill the ends with numbers from x vector (out[1:k2] = x[1:k2]) * "constant" Fill the ends with first and last calculated value in output array (out[1:k2] = out[k2+1]) * "NA" Fill the ends with NA's (out[1:k2] = NA) * "func" Same as "quantile" but implimented in R. This option could be very slow, and is included mostly for testing

align

specifies whether result should be centered (default), left-aligned or right-aligned. If endrule="quantile" then setting align to "left" or "right" will fall back on slower implementation equivalent to endrule="func".

Details

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. 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)

Function runquantile uses 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.

Value

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.

Author(s)

Jarek Tuszynski (SAIC) [e-mail]

References

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)

Examples

## show plot using runquantile
k <- 31; n <- 200
x <- rnorm(n, sd=30) + abs(seq(n)-n/4)
y <- diveMove:::.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)

## basic tests against apply/embed
a <- diveMove:::.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 %/% 2
k1 <- k - k2 - 1
a <- diveMove:::.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 <- diveMove:::.runquantile(x, k, probs=0.4,
                             endrule="quantile") # fast C code
b <- diveMove:::.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 <- diveMove:::.runquantile(x, k, probs=0.4)
b <- diveMove:::.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 <- diveMove:::.runquantile(x, k, probs=0.6)
b <- diveMove:::.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 <- diveMove:::.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(diveMove:::.runquantile(x, k, 0.5)) # Speed O(n*k)

## End(Not run)