Plot a smooth approximation of z over x and y.
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.
panel.2dsmoother(x, y, z, subscripts = TRUE,
form = z ~ x * y, method = "loess", ...,
args = list(), n = 100)x, y, z |
data points. If these are missing, they will be looked for in the
environment of |
form, method |
the smoothing model is constructed (approximately) as
|
subscripts |
data indices for the current packet, as passed in by |
... |
further arguments passed on to |
args |
a list of further arguments to the model function ( |
n |
number of equi-spaced points along each of x and y on which to evaluate the smooth function. |
This should work with any model function that takes a formula
argument, and has a predict method argument.
Felix Andrews felix@nfrac.org
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")Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.