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.

1 2 3 | ```
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.

1 2 3 4 5 6 7 8 9 | ```
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")
})
``` |

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