R: Plot a smooth approximation of z over x and y.

panel.2dsmoother {latticeExtra}

R Documentation

Plot a smooth approximation of z over x and y.

Description

Plot a smooth approximation, using loess by default, of one variable (z) against two others (x and y).

This panel function should be used with a levelplot.

Usage

panel.2dsmoother(x, y, z, subscripts = TRUE,
    form = z ~ x * y, method = "loess", ...,
    args = list(), n = 100)

Arguments

`x, y, z`	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, z=z), {args})`. See the Examples section for common choices.
`subscripts`	data indices for the current packet, as passed in by `levelplot`.
`...`	further arguments passed on to `panel.levelplot`.
`args`	a list of further arguments to the model function (`method`).
`n`	number of equi-spaced points along each of x and y on which to evaluate the smooth function.

Details

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

Author(s)

Felix Andrews felix@nfrac.org

Examples

set.seed(1)
xyz <- data.frame(x = rnorm(100), y = rnorm(100))
xyz$z <- with(xyz, x * y + rnorm(100, sd = 1))

levelplot(z ~ x * y, xyz, panel = panel.2dsmoother)

## showing data points on the same color scale
levelplot(z ~ x * y, xyz,
          panel = panel.levelplot.points, cex = 1.2) +
  layer_(panel.2dsmoother(..., n = 200))

## simple linear regression model
levelplot(z ~ x * y, xyz,
          panel = panel.levelplot.points) +
  layer_(panel.2dsmoother(..., method = "lm"))

## GAM smoother with smoothness by cross validation
if (require("mgcv"))
  levelplot(z ~ x * y, xyz, panel = panel.2dsmoother,
            form = z ~ s(x, y), method = "gam")

[Package latticeExtra version 0.6-25 Index]