smoothEnds {stats} | R Documentation |

## End Points Smoothing (for Running Medians)

### Description

Smooth end points of a vector `y`

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

### 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[1] = median(y[1], sm[2], 3*sm[2] - 2*sm[3])`

.

### Value

vector of smoothed values, the same length as `y`

.

### Author(s)

Martin Maechler

### 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.

### See Also

`runmed(*, end.rule = "median")`

which calls
`smoothEnds()`

.

### Examples

y <- ys <- (-20:20)^2
y [c(1,10,21,41)] <- c(100, 30, 400, 470)
s7k <- runmed(y,7, end = "keep")
s7. <- runmed(y,7, end = "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("end.rule",c("keep","constant","median"),sep=" = "),
col = col3, lwd = 1.5, lty = 1)
stopifnot(identical(s7m, smoothEnds(s7k, 7)))

[Package

*stats* version 2.2.1

Index]