runlm {oce} | R Documentation |
Fit linear models y=y(x) in local regions of x space, possibly with distance-based Hanning weightings.
runlm(x, y, xout, window=c("hanning", "boxcar"), L, deriv)
x |
a vector holding x values. |
y |
a vector holding y values. |
xout |
optional vector of x values at which the derivative is to be
found. If not provided, |
window |
type of weighting function used to weight data within the window; see ‘Details’. |
L |
width of running window, in x units. If not provided, a reasonable default will be used. |
deriv |
an optional indicator of the desired return value; see ‘Examples’. |
The linear model is calculated from the slope of a localized
least-squares regression model y=y(x). The localization is defined by the
x difference from the point in question, with data at distance exceeding
L/2 being ignored. With a boxcar
window, all data within the local
domain are treated equally, while with a hanning
window, a
raised-cosine weighting function is used; the latter produces smoother
derivatives, which can be useful for noisy data. The function is based on
internal calculation, not on lm
.
If deriv
is not specified, a list containing vectors of output
values y
and y
, derivative (dydx
), along with the
scalar length scale L
. If deriv=0
, a vector of values is
returned, and if deriv=1
, a vector of derivatives is returned.
Dan Kelley
library(oce) # Case 1: smooth a noisy signal x <- 1:100 y <- 1 + x/100 + sin(x/5) yn <- y + rnorm(100, sd=0.1) L <- 4 calc <- runlm(x, y, L=L) plot(x, y, type='l', lwd=7, col='gray') points(x, yn, pch=20, col='blue') lines(x, calc$y, lwd=2, col='red') # Case 2: square of buoyancy frequency data(ctd) par(mfrow=c(1,1)) plot(ctd, which="N2") rho <- swRho(ctd) z <- swZ(ctd) zz <- seq(min(z), max(z), 0.1) N2 <- -9.8 / mean(rho) * runlm(z, rho, zz, deriv=1) lines(N2, -zz, col='red') legend("bottomright", lwd=2, bg="white", col=c("black", "red"), legend=c("swN2()", "using runlm()"))