R: Plot a smoothing line with standard error bounds.

panel.smoother {latticeExtra}

R Documentation

Plot a smoothing line with standard error bounds.

Description

Plot a smoothing line with standard error bounds. This is based on the stat_smooth function from ggplot2.

Usage

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)

Arguments

`x, y`	data points. If these are missing, they will be looked for in the environment of `form`. So in many cases you can skip these if passing `form`. In fact, for convenience, the formula can be passed as the first argument (i.e. `x`).
`form, method`	the smoothing model is constructed (approximately) as `method(form, data = list(x=x, y=y), ...)`. See the Examples section for common choices.
`...`	further arguments passed on to the model function (`method`).
`se, level`	estimate standard errors on the smoother, at the given `level`, and plot these as a band.
`n`	number of equi-spaced points on which to evaluate the smooth function.
`col, col.se, lty, lwd, alpha, alpha.se, border`	graphical parameters. `col` and `alpha` apply to the smoothing line, while `col.se` and `alpha.se` apply to the shaded `se` region.
`subscripts, group.number, group.value, type, col.line, col.symbol, fill, pch, cex, font, fontface, fontfamily`	ignored.

Details

This should work with any model function that takes a formula argument, and has a predict method with a se argument.

Author(s)

Felix Andrews felix@nfrac.org

Based on stat_smooth by Hadley Wickham.

Examples

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)
})

## per-group 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)

[Package latticeExtra version 0.6-25 Index]