sm.surface3d {sm} | R Documentation |
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.
sm.surface3d(eval.points, surf, scaling, col = "green", col.mesh = "black", alpha = 0.7, alpha.mesh = 1, lit = TRUE, ...)
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 |
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.
a vector of length 2 containing the ids of the filled surface and lines
aded to the rgl
plot.
a surface is added to the rgl
plot.
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") })