# smoothEnds

0th

Percentile

##### End Points Smoothing (for Running Medians)

Smooth end points of a vector y using subsequently smaller medians and Tukey's end point rule at the very end. (of odd span),

Keywords
robust, smooth
##### Usage
smoothEnds(y, k = 3)
##### Arguments
y

dependent variable to be smoothed (vector).

k

width of largest median window; must be odd.

##### Details

smoothEnds is used to only do the ‘end point smoothing’, i.e., change at most the observations closer to the beginning/end than half the window k. The first and last value are computed using Tukey's end point rule, i.e., sm = median(y, sm, 3*sm - 2*sm).

##### Value

vector of smoothed values, the same length as y.

##### References

John W. Tukey (1977) Exploratory Data Analysis, Addison.

Velleman, P.F., and Hoaglin, D.C. (1981) ABC of EDA (Applications, Basics, and Computing of Exploratory Data Analysis); Duxbury.

runmed(*, endrule = "median") which calls smoothEnds().
library(stats) # NOT RUN { require(graphics) y <- ys <- (-20:20)^2 y [c(1,10,21,41)] <- c(100, 30, 400, 470) s7k <- runmed(y, 7, endrule = "keep") s7. <- runmed(y, 7, endrule = "const") s7m <- runmed(y, 7) col3 <- c("midnightblue","blue","steelblue") plot(y, main = "Running Medians -- runmed(*, k=7, end.rule = X)") lines(ys, col = "light gray") matlines(cbind(s7k, s7.,s7m), lwd = 1.5, lty = 1, col = col3) legend(1, 470, paste("endrule", c("keep","constant","median"), sep = " = "), col = col3, lwd = 1.5, lty = 1) stopifnot(identical(s7m, smoothEnds(s7k, 7))) # }