smooth {stats} R Documentation

## Tukey's (Running Median) Smoothing

### Description

Tukey's smoothers, 3RS3R, 3RSS, 3R, etc.

### Usage

```smooth(x, kind = c("3RS3R", "3RSS", "3RSR", "3R", "3", "S"),
twiceit = FALSE, endrule = c("Tukey", "copy"), do.ends = FALSE)
```

### Arguments

 `x` a vector or time series `kind` a character string indicating the kind of smoother required; defaults to `"3RS3R"`. `twiceit` logical, indicating if the result should be ‘twiced’. Twicing a smoother S(y) means S(y) + S(y - S(y)), i.e., adding smoothed residuals to the smoothed values. This decreases bias (increasing variance). `endrule` a character string indicating the rule for smoothing at the boundary. Either `"Tukey"` (default) or `"copy"`. `do.ends` logical, indicating if the 3-splitting of ties should also happen at the boundaries (ends). This is only used for `kind = "S"`.

### Details

`3` is Tukey's short notation for running `median`s of length 3,
`3R` stands for Repeated `3` until convergence, and
`S` for Splitting of horizontal stretches of length 2 or 3.

Hence, `3RS3R` is a concatenation of `3R`, `S` and `3R`, `3RSS` similarly, whereas `3RSR` means first `3R` and then `(S and 3)` Repeated until convergence – which can be bad.

### Value

An object of class `"tukeysmooth"` (which has `print` and `summary` methods) and is a vector or time series containing the smoothed values with additional attributes.

### Note

S and S-PLUS use a different (somewhat better) Tukey smoother in `smooth(*)`. Note that there are other smoothing methods which provide rather better results. These were designed for hand calculations and may be used mainly for didactical purposes.

Since R version 1.2, `smooth` does really implement Tukey's end-point rule correctly (see argument `endrule`).

`kind = "3RSR"` has been the default till R-1.1, but it can have very bad properties, see the examples.

Note that repeated application of `smooth(*)` does smooth more, for the `"3RS*"` kinds.

### References

`runmed` for running medians; `lowess` and `loess`; `supsmu` and `smooth.spline`.

### Examples

```require(graphics)

x1 <- c(4, 1, 3, 6, 6, 4, 1, 6, 2, 4, 2) # very artificial
(x3R <- smooth(x1, "3R")) # 2 iterations of "3"
smooth(x3R, kind = "S")

sm.3RS <- function(x, ...)
smooth(smooth(x, "3R", ...), "S", ...)

y <- c(1, 1, 19:1)
plot(y, main = "misbehaviour of \"3RSR\"", col.main = 3)
lines(sm.3RS(y))
lines(smooth(y))
lines(smooth(y, "3RSR"), col = 3, lwd = 2)  # the horror

x <- c(8:10, 10, 0, 0, 9, 9)
plot(x, main = "breakdown of  3R  and  S  and hence  3RSS")
matlines(cbind(smooth(x, "3R"), smooth(x, "S"), smooth(x, "3RSS"), smooth(x)))

presidents[is.na(presidents)] <- 0 # silly
summary(sm3 <- smooth(presidents, "3R"))