runmed {stats}R Documentation

Running Medians – Robust Scatter Plot Smoothing


Compute running medians of odd span. This is the ‘most robust’ scatter plot smoothing possible. For efficiency (and historical reason), you can use one of two different algorithms giving identical results.


runmed(x, k, endrule = c("median", "keep", "constant"),
       algorithm = NULL,
       na.action = c("+Big_alternate", "-Big_alternate", "na.omit", "fail"),
       print.level = 0)



numeric vector, the ‘dependent’ variable to be smoothed.


integer width of median window; must be odd. Turlach had a default of k <- 1 + 2 * min((n-1)%/% 2, ceiling(0.1*n)). Use k = 3 for ‘minimal’ robust smoothing eliminating isolated outliers.


character string indicating how the values at the beginning and the end (of the data) should be treated. Can be abbreviated. Possible values are:


keeps the first and last k_2 values at both ends, where k_2 is the half-bandwidth k2 = k %/% 2, i.e., y[j] = x[j] for j \in \{1,\ldots,k_2; n-k_2+1,\ldots,n\};


copies median(y[1:k2]) to the first values and analogously for the last ones making the smoothed ends constant;


the default, smooths the ends by using symmetrical medians of subsequently smaller bandwidth, but for the very first and last value where Tukey's robust end-point rule is applied, see smoothEnds.


character string (partially matching "Turlach" or "Stuetzle") or the default NULL, specifying which algorithm should be applied. The default choice depends on n = length(x) and k where "Turlach" will be used for larger problems.


character string determining the behavior in the case of NA or NaN in x, (partially matching) one of


Here, all the NAs in x are first replaced by alternating \pm B where B is a “Big” number (with 2B < M*, where M*=.Machine $ double.xmax). The replacement values are “from left” (+B, -B, +B, \ldots), i.e. start with "+".


almost the same as "+Big_alternate", just starting with -B ("-Big...").


the result is the same as runmed(x[!], k, ..).


the presence of NAs in x will raise an error.


integer, indicating verboseness of algorithm; should rarely be changed by average users.


Apart from the end values, the result y = runmed(x, k) simply has y[j] = median(x[(j-k2):(j+k2)]) (k = 2*k2+1), computed very efficiently.

The two algorithms are internally entirely different:


is the Härdle–Steiger algorithm (see Ref.) as implemented by Berwin Turlach. A tree algorithm is used, ensuring performance O(n \log k) where n = length(x) which is asymptotically optimal.


is the (older) Stuetzle–Friedman implementation which makes use of median updating when one observation enters and one leaves the smoothing window. While this performs as O(n \times k) which is slower asymptotically, it is considerably faster for small k or n.

Note that, both algorithms (and the smoothEnds() utility) now “work” also when x contains non-finite entries (\pmInf, NaN, and NA):




currently simply works by applying the underlying math library (‘libm’) arithmetic for the non-finite numbers; this may optionally change in the future.

Currently long vectors are only supported for algorithm = "Stuetzle".


vector of smoothed values of the same length as x with an attribute k containing (the ‘oddified’) k.


Martin Maechler, based on Fortran code from Werner Stuetzle and S-PLUS and C code from Berwin Turlach.


Härdle, W. and Steiger, W. (1995) Algorithm AS 296: Optimal median smoothing, Applied Statistics 44, 258–264. doi:10.2307/2986349.

Jerome H. Friedman and Werner Stuetzle (1982) Smoothing of Scatterplots; Report, Dep. Statistics, Stanford U., Project Orion 003.

See Also

smoothEnds which implements Tukey's end point rule and is called by default from runmed(*, endrule = "median"). smooth uses running medians of 3 for its compound smoothers.



myNHT <- as.vector(nhtemp)
myNHT[20] <- 2 * nhtemp[20]
plot(myNHT, type = "b", ylim = c(48, 60), main = "Running Medians Example")
lines(runmed(myNHT, 7), col = "red")

## special: multiple y values for one x
plot(cars, main = "'cars' data and runmed(dist, 3)")
lines(cars, col = "light gray", type = "c")
with(cars, lines(speed, runmed(dist, k = 3), col = 2))

## nice quadratic with a few outliers
y <- ys <- (-20:20)^2
y [c(1,10,21,41)] <- c(150, 30, 400, 450)
all(y == runmed(y, 1)) # 1-neighbourhood <==> interpolation
plot(y) ## lines(y, lwd = .1, col = "light gray")
lines(lowess(seq(y), y, f = 0.3), col = "brown")
lines(runmed(y, 7), lwd = 2, col = "blue")
lines(runmed(y, 11), lwd = 2, col = "red")

## Lowess is not robust
y <- ys ; y[21] <- 6666 ; x <- seq(y)
col <- c("black", "brown","blue")
plot(y, col = col[1])
lines(lowess(x, y, f = 0.3), col = col[2])

lines(runmed(y, 7),      lwd = 2, col = col[3])
legend(length(y),max(y), c("data", "lowess(y, f = 0.3)", "runmed(y, 7)"),
       xjust = 1, col = col, lty = c(0, 1, 1), pch = c(1,NA,NA))

## An example with initial NA's - used to fail badly (notably for "Turlach"):
x15 <- c(rep(NA, 4), c(9, 9, 4, 22, 6, 1, 7, 5, 2, 8, 3))
rS15 <- cbind(Sk.3 = runmed(x15, k = 3, algorithm="S"),
              Sk.7 = runmed(x15, k = 7, algorithm="S"),
              Sk.11= runmed(x15, k =11, algorithm="S"))
rT15 <- cbind(Tk.3 = runmed(x15, k = 3, algorithm="T", print.level=1),
              Tk.7 = runmed(x15, k = 7, algorithm="T", print.level=1),
              Tk.9 = runmed(x15, k = 9, algorithm="T", print.level=1),
              Tk.11= runmed(x15, k =11, algorithm="T", print.level=1))
cbind(x15, rS15, rT15) # result for k=11  maybe a bit surprising ..
Tv <- rT15[-(1:3),]
stopifnot(3 <= Tv, Tv <= 9, 5 <= Tv[1:10,])
matplot(y = cbind(x15, rT15), type = "b", ylim = c(1,9), pch=1:5, xlab = NA,
        main = "runmed(x15, k, algo = \"Turlach\")")
mtext(paste("x15 <-", deparse(x15)))
points(x15, cex=2)
legend("bottomleft", legend=c("data", paste("k = ", c(3,7,9,11))),
       bty="n", col=1:5, lty=1:5, pch=1:5)

[Package stats version 4.4.0 Index]