smooth_triangular {alkahest} | R Documentation |
Triangular Smoothing
Description
Weighted sliding-average or triangular smoothing.
Usage
smooth_triangular(x, y, ...)
## S4 method for signature 'numeric,numeric'
smooth_triangular(x, y, m = 3)
## S4 method for signature 'ANY,missing'
smooth_triangular(x, m)
Arguments
x , y |
A |
... |
Currently not used. |
m |
An odd |
Details
It replaces each point in the signal with the weighted mean of m
adjacent points.
Value
Returns a list
with two components x
and y
.
Note
There will be (m - 1) / 2
points both at the beginning and at the end
of the data series for which a complete m
-width window cannot be
obtained. To prevent data loss, progressively wider/narrower windows are
used at both ends of the data series.
Author(s)
N. Frerebeau
See Also
Other smoothing methods:
smooth_likelihood()
,
smooth_loess()
,
smooth_rectangular()
,
smooth_savitzky()
,
smooth_whittaker()
Examples
## Simulate data with some noise
x <- seq(-4, 4, length = 100)
y <- dnorm(x) + rnorm(100, mean = 0, sd = 0.01)
## Plot spectrum
plot(x, y, type = "l", xlab = "", ylab = "")
## Rectangular smoothing
unweighted <- smooth_rectangular(x, y, m = 3)
plot(unweighted, type = "l", xlab = "", ylab = "")
## Triangular smoothing
weighted <- smooth_triangular(x, y, m = 5)
plot(weighted, type = "l", xlab = "", ylab = "")
## Loess smoothing
loess <- smooth_loess(x, y, span = 0.75)
plot(loess, type = "l", xlab = "", ylab = "")
## Savitzky–Golay filter
savitzky <- smooth_savitzky(x, y, m = 21, p = 2)
plot(savitzky, type = "l", xlab = "", ylab = "")
## Whittaker smoothing
whittaker <- smooth_whittaker(x, y, lambda = 1600, d = 2)
plot(whittaker, type = "l", xlab = "", ylab = "")