panel.smoother {latticeExtra}  R Documentation 
Plot a smoothing line with standard error bounds.
This is based on the stat_smooth
function from ggplot2.
panel.smoother(x, y, form = y ~ x, method = "loess", ..., se = TRUE, level = 0.95, n = 100, col = plot.line$col, col.se = col, lty = plot.line$lty, lwd = plot.line$lwd, alpha = plot.line$alpha, alpha.se = 0.25, border = NA, ## ignored: ## subscripts, group.number, group.value, type, col.line, col.symbol, fill, pch, cex, font, fontface, fontfamily)
x, y 
data points. If these are missing, they will be looked for in the
environment of 
form, method 
the smoothing model is constructed (approximately) as

... 
further arguments passed on to the model function ( 
se, level 
estimate standard errors on the smoother, at the given 
n 
number of equispaced points on which to evaluate the smooth function. 
col, col.se, lty, lwd, alpha, alpha.se, border 
graphical parameters. 
subscripts, group.number, group.value,
type, col.line, col.symbol, fill,
pch, cex, font, fontface, fontfamily 
ignored. 
This should work with any model function that takes a formula
argument, and has a predict
method with a se
argument.
Felix Andrews felix@nfrac.org
Based on stat_smooth
by Hadley Wickham.
panel.loess
,
panel.quantile
,
stat_smooth
set.seed(1) xy < data.frame(x = runif(100), y = rt(100, df = 5), y2 = rt(100, df = 5) + 1) xyplot(y ~ x, xy, panel = function(...) { panel.xyplot(...) panel.smoother(..., span = 0.9) }) ## pergroup layers with glayer (pass `...` to get styles) xyplot(y + y2 ~ x, xy) + glayer(panel.smoother(...)) ## natural spline with 5 degrees of freedom if (require("splines")) xyplot(y ~ x, xy) + layer(panel.smoother(y ~ ns(x,5), method = "lm")) ## thin plate regression spline with smoothness ## chosen by cross validation (see ?mgcv::gam) if (require("mgcv")) xyplot(y ~ x, xy) + layer(panel.smoother(y ~ s(x), method = "gam")) ## simple linear regression with standard errors: xyplot(y ~ x, xy) + layer(panel.smoother(x, y, method = "lm"), style = 2)