runmean {caTools}

Mean of a Moving Window

Description

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

Usage

  runmean(x, k, alg=c("C", "R", "fast", "exact"),
         endrule=c("mean", "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 1 and n

alg

an option to choose different algorithms

• "C" - a version is written in C. It can handle non-finite numbers like NaN's and Inf's (like mean(x, na.rm = TRUE)). It works the fastest for endrule="mean".

• "fast" - second, even faster, C version. This algorithm does not work with non-finite numbers. It also works the fastest for endrule other than "mean".

• "R" - much slower code written in R. Useful for debugging and as documentation.

• "exact" - same as "C", except that all additions are performed using algorithm which tracks and corrects addition round-off errors

endrule

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

• "mean" - applies the underlying function to smaller and smaller sections of the array. Equivalent to: for(i in 1:k2) out[i] = mean(x[1:(i+k2)]). This option is implemented in C if alg="C", otherwise is done in R.

• "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,mean) 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 "mean" but implimented in R. This option could be very slow, and is included mostly for testing

Similar to endrule in runmed function which has the following options: “c("median", "keep", "constant")” .

align

specifies whether result should be centered (default), left-aligned or right-aligned. If endrule="mean" 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 = runmean(x, k) is the same as “for(j=(1+k2):(n-k2)) y[j]=mean(x[(j-k2):(j+k2)])”.

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 speed of runmean function is O(n).

Function EndRule applies one of the five methods (see endrule argument) to process end-points of the input array x. In current version of the code the default endrule="mean" option is calculated within C code. That is done to improve speed in case of large moving windows.

In case of runmean(..., alg="exact") function a special algorithm is used (see references section) to ensure that round-off errors do not accumulate. As a result runmean is more accurate than filter(x, rep(1/k,k)) and runmean(..., alg="C") functions.

Value

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.

Note

Function runmean(..., alg="exact") is based by code by Vadim Ogranovich, which is based on Python code (see last reference), pointed out by Gabor Grothendieck.

Author(s)

Jarek Tuszynski (SAIC) [e-mail]

References

• About round-off error correction used in runmean: Shewchuk, Jonathan Adaptive Precision Floating-Point Arithmetic and Fast Robust Geometric Predicates, http://www-2.cs.cmu.edu/afs/cs/project/quake/public/papers/robust-arithmetic.ps

See Also

Links related to:

• moving mean - mean, kernapply, filter, decompose, stl, rollmean from zoo library, subsums from magic library,

• Other moving window functions from this package: runmin, runmax, runquantile, runmad and runsd

• runmed

• 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.

Examples

  # show runmean for different window sizes
  n=200;
  x = rnorm(n,sd=30) + abs(seq(n)-n/4)
  x[seq(1,n,10)] = NaN;              # add NANs
  col = c("black", "red", "green", "blue", "magenta", "cyan")
  plot(x, col=col[1], main = "Moving Window Means")
  lines(runmean(x, 3), col=col[2])
  lines(runmean(x, 8), col=col[3])
  lines(runmean(x,15), col=col[4])
  lines(runmean(x,24), col=col[5])
  lines(runmean(x,50), col=col[6])
  lab = c("data", "k=3", "k=8", "k=15", "k=24", "k=50")
  legend(0,0.9*n, lab, col=col, lty=1 )
  
  # basic tests against 2 standard R approaches
  k=25; n=200;
  x = rnorm(n,sd=30) + abs(seq(n)-n/4)      # create random data
  a = runmean(x,k, endrule="trim")          # tested function
  b = apply(embed(x,k), 1, mean)            # approach #1
  c = cumsum(c( sum(x[1:k]), diff(x,k) ))/k # approach #2
  eps = .Machine$double.eps ^ 0.5
  stopifnot(all(abs(a-b)<eps));
  stopifnot(all(abs(a-c)<eps));
  
  # test against loop approach
  # this test works fine at the R prompt but fails during package check - need to investigate
  k=25; 
  data(iris)
  x = iris[,1]
  n = length(x)
  x[seq(1,n,11)] = NaN;                # add NANs
  k2 = k
  k1 = k-k2-1
  a = runmean(x, k)
  b = array(0,n)
  for(j in 1:n) {
    lo = max(1, j-k1)
    hi = min(n, j+k2)
    b[j] = mean(x[lo:hi], na.rm = TRUE)
  }
  #stopifnot(all(abs(a-b)<eps)); # commented out for time beeing - on to do list
  
  # compare calculation at array ends
  a = runmean(x, k, endrule="mean")  # fast C code
  b = runmean(x, k, endrule="func")  # slow R code
  stopifnot(all(abs(a-b)<eps));
  
  # Testing of different methods to each other for non-finite data
  # Only alg "C" and "exact" can handle not finite numbers 
  eps = .Machine$double.eps ^ 0.5
  n=200;  k=51;
  x = rnorm(n,sd=30) + abs(seq(n)-n/4) # nice behaving data
  x[seq(1,n,10)] = NaN;                # add NANs
  x[seq(1,n, 9)] = Inf;                # add infinities
  b = runmean( x, k, alg="C")
  c = runmean( x, k, alg="exact")
  stopifnot(all(abs(b-c)<eps));

  # Test if moving windows forward and backward gives the same results
  # Test also performed on data with non-finite numbers
  a = runmean(x     , alg="C", k)
  b = runmean(x[n:1], alg="C", k)
  stopifnot(all(abs(a[n:1]-b)<eps));
  a = runmean(x     , alg="exact", k)
  b = runmean(x[n:1], alg="exact", 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 = runmean(x, k)
  b = runmean(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

  # Exhaustive testing of different methods to each other for different windows
  numeric.test = function (x, k) {
    a = runmean( x, k, alg="fast")
    b = runmean( x, k, alg="C")
    c = runmean( x, k, alg="exact")
    d = runmean( x, k, alg="R", endrule="func")
    eps = .Machine$double.eps ^ 0.5
    stopifnot(all(abs(a-b)<eps));
    stopifnot(all(abs(b-c)<eps));
    stopifnot(all(abs(c-d)<eps));
  }
  n=200;
  x = rnorm(n,sd=30) + abs(seq(n)-n/4) # nice behaving data
  for(i in 1:5) 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(1e7); k=1e4;
  system.time(runmean(x,k,alg="fast"))
  system.time(runmean(x,k,alg="C"))
  system.time(runmean(x,k,alg="exact"))
  system.time(runmean(x,k,alg="R"))           # R version of the function
  x=runif(1e5); k=1e2;                        # reduce vector and window sizes
  system.time(runmean(x,k,alg="R"))           # R version of the function
  system.time(apply(embed(x,k), 1, mean))     # standard R approach
  system.time(filter(x, rep(1/k,k), sides=2)) # the fastest alternative I know 
  
## End(Not run)
   
  # show different runmean algorithms with data spanning many orders of magnitude
  n=30; k=5;
  x = rep(100/3,n)
  d=1e10
  x[5] = d;     
  x[13] = d; 
  x[14] = d*d; 
  x[15] = d*d*d; 
  x[16] = d*d*d*d; 
  x[17] = d*d*d*d*d; 
  a = runmean(x, k, alg="fast" )
  b = runmean(x, k, alg="C"    )
  c = runmean(x, k, alg="exact")
  y = t(rbind(x,a,b,c))
  y