# runmed

0th

Percentile

##### 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.

Keywords
robust, smooth
##### Usage
runmed(x, k, endrule = c("median", "keep", "constant"),
algorithm = NULL, print.level = 0)
##### Arguments
x
numeric vector, the dependent variable to be smoothed.
k
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.
endrule
character string indicating how the values at the beginning and the end (of the data) should be treated. Can be abbreviated. Possible values are: [object Object],[object Object],[object Object]
algorithm
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.
print.level
integer, indicating verboseness of algorithm; should rarely be changed by average users.
##### Details

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: [object Object],[object Object]

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

##### Value

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

UTF-8

##### References

HÃ¤rdle{Haerdle}, W. and Steiger, W. (1995) [Algorithm AS 296] Optimal median smoothing, Applied Statistics 44, 258--264.

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

Martin Maechler (2003) Fast Running Medians: Finite Sample and Asymptotic Optimality; working paper available from the author.

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.
library(stats) require(graphics) utils::example(nhtemp) 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))