How to generate a smoothed surface for a three dimensional dataset?

Hi,

I have a dataset with two independent variables (x, y) and a response
variable (z). I was hoping to generate a response surface by plotting x, y,
z on a three dimensional plot. I can plot the data with rgl.points(x, y,
z). I understand I may not have enough data to generate a surface. Is there
a way to smooth out the data points to generate a surface? Thanks a lot.

There are many ways to do that.  You need to fit a model that predicts z 
from (x, y), and then plot the predictions from that model.
An example below follows yours.

Jun

===========================

An example:

x<-runif(20)
y<-runif(20)
z<-runif(20)

library(rgl)
rgl.points(x,y,z)

Don't use rgl.points, use points3d() or plot3d().  Here's the full script:


x<-runif(20)
y<-runif(20)
z<-runif(20)

library(rgl)
plot3d(x,y,z)

fit <- lm(z ~ x + y + x*y + x^2 + y^2)

xnew <- seq(min(x), max(x), len=20)
ynew <- seq(min(y), max(y), len=20)
df <- expand.grid(x = xnew,
                   y = ynew)

df$z <- predict(fit, newdata=df)

surface3d(xnew, ynew, df$z, col="red")


Duncan Murdoch

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