Unweighted sliding-average or rectangular Smoothing.
smooth_rectangular(x, y, ...) # S4 method for numeric,numeric smooth_rectangular(x, y, m = 3) # S4 method for ANY,missing smooth_rectangular(x, m)
- x, y
yis missing, an attempt is made to interpret
xin a suitable way (see
Currently not used.
integergiving the window size (i.e. the number of adjacent points to be used; see
list with two components
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.
Other smoothing methods:
## 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 = "")