# runmed

##### 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.

##### 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:

`"keep"`

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 \(j \in \{1,\ldots,k_2; n-k_2+1,\ldots,n\}\);`"constant"`

copies

`median(y[1:k2])`

to the first values and analogously for the last ones making the smoothed ends*constant*;`"median"`

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`

.

- 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:

`"Turlach"`

is the H<U+00E4>rdle--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.`"Stuetzle"`

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 \(n\).

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

.

##### Value

vector of smoothed values of the same length as `x`

with an
`attr`

ibute `k`

containing (the ‘oddified’)
`k`

.

##### References

H<U+00E4>rdle, W. and Steiger, W. (1995)
Algorithm AS 296: Optimal median smoothing,
*Applied Statistics* **44**, 258--264.
10.2307/2986349.

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.

##### 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.

##### Examples

`library(stats)`

```
# NOT RUN {
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))
# }
# NOT RUN {
<!-- %% FIXME: Show how to do it properly ! tapply(*, unique(.), median) -->
# }
# NOT RUN {
## 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])
# }
# NOT RUN {
<!-- %% predict(loess(y ~ x, span = 0.3, degree=1, family = "symmetric")) -->
# }
# NOT RUN {
<!-- %% gives 6-line warning but does NOT break down -->
# }
# NOT RUN {
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))
# }
```

*Documentation reproduced from package stats, version 3.6.1, License: Part of R 3.6.1*