Local Polynomial Regression Fitting with Variability bands

Nonparametric estimation of mean function with variability bands.

Usage

loess.sd(x, y = NULL, nsigma = 1, ...)

panel.loess(x, y, col = par("col"), bg = NA, pch = par("pch"), cex = 1, 
            col.smooth = "red", span = 2/3, degree = 2, nsigma = 1, ...)

Arguments

x: a vector of values for the predictor variable \(x\).
y: a vector of values for the response variable \(y\).
nsigma: a multiplier for the standard deviation function.
col, bg, pch, cex: numeric or character codes for the color(s), point type and size of points; see also par.
col.smooth: color to be used by lines for drawing the smooths.
span: smoothing parameter for loess.
degree: the degree of the polynomials to be used, see loess.
...: further argument passed to the function loess.

Value

The function loess.sd computes the loess smooth for the mean function and the mean plus and minus k times the standard deviation function.

The function panel.loess can be used to add to a scatterplot matrix panel a smoothing of mean function using loess with variability bands at plus and minus nsigmas times the standard deviation.

References

Weisberg, S. (2005) Applied Linear Regression, 3rd ed., Wiley, New York, pp. 275-278.

Author

Luca Scrucca luca.scrucca@unipg.it

Examples

data(cars)
plot(cars, main = "lowess.sd(cars)")
lines(l <- loess.sd(cars))
lines(l$x, l$upper, lty=2)
lines(l$x, l$lower, lty=2)