runlm.Rd

% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/run.R
\name{runlm}
\alias{runlm}
\title{Calculate Running Linear Models}
\usage{
runlm(x, y, xout, window = c("hanning", "boxcar"), L, deriv)
}
\arguments{
\item{x}{a vector holding x values.}

\item{y}{a vector holding y values.}

\item{xout}{optional vector of x values at which the derivative is to be
found.  If not provided, \code{x} is used.}

\item{window}{type of weighting function used to weight data within the
window; see \dQuote{Details}.}

\item{L}{width of running window, in x units.  If not provided, a reasonable
default will be used.}

\item{deriv}{an optional indicator of the desired return value; see
\dQuote{Examples}.}
}
\value{
If \code{deriv} is not specified, a list containing vectors of
output values \code{y} and \code{y}, derivative (\code{dydx}), along with
the scalar length scale \code{L}.  If \code{deriv=0}, a vector of values is
returned, and if \code{deriv=1}, a vector of derivatives is returned.
}
\description{
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 \code{boxcar} window, all data within the local domain are
treated equally, while with a \code{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 \code{\link[=lm]{lm()}}.
}
\examples{

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()")
)
}
\author{
Dan Kelley
}