| sm.surface3d {sm} | R Documentation |
Adding a regression surface to an rgl plot.
Description
This function adds a regression surface, defined by a matrix of heights
at a regular grid of values of two covariates, to an rgl plot.
Missing values can be accommodated.
Usage
sm.surface3d(eval.points, surf, scaling,
col = "green", col.mesh = "black",
alpha = 0.7, alpha.mesh = 1, lit = TRUE, ...)
Arguments
eval.points |
if this is a two-column matrix then each column defines the marginal grids of covariate values. Alternatively, a list with two components can also be used to handle cases where the grids are of different size. |
surf |
a matrix of heights corresponding to the grid of covariate values. NAs are allowed. |
scaling |
a function to define the scaling for the |
col |
the colour of the surface. If |
col.mesh |
the colour of the surface mesh. If |
alpha |
the transparency of the filled triangles defining the surface. Setting
this to |
alpha.mesh |
the transparency of the lines drawn across the regular grid of covariate
values. Setting this to |
lit |
a logical variable which controls whether the |
... |
other optional parameters which are passed to |
Details
the principal motivation for this function is that is can handle missing
data in regression surfaces. In particular, it can be used to plot the
results of applying sm.regression. In addition, the function can
be used to build up more complex plots by adding successive surfaces.
Value
a vector of length 2 containing the ids of the filled surface and lines
added to the rgl plot.
Side Effects
a surface is added to the rgl plot.
See Also
Examples
with(trawl, {
Zone93 <- (Year == 1 & Zone == 1)
Position <- cbind(Longitude - 143, Latitude)
model1 <- sm.regression(Position[Zone93,], Score1[Zone93],
h= c(0.1, 0.1), display = "rgl", xlab="Longitude - 143")
model2 <- sm.regression(Position[Zone93,], Score1[Zone93],
h= c(0.2, 0.2), display = "none")
sm.surface3d(model2$eval.points, model2$est, model1$scaling, col = "red")
})