Hi,

I have a function of the second grade, with 2 parameters:
    y~A^2 + A + B^2 + B

The response y is a measurement for the precision of the analytical method, where A en B are method parameters. As its neccesary to keep the precision of the analytical methad as good as possible, its usefull to optimize A en B to keep y as low as possible.
But how can I do this with R?
I have searched the archives, did some search work in the help function ( optimize, nlm, nls, ...)
but could find anything that looks like what I need.
I have written a script which does the work, but I doubt this is the easiest way.

Here are some data and the script:
A<- rep(c(1,4,8),3)
B<- rep(c(1,3,6),each=3)
C <- c(3,2,3,2,1,2,3,2,3)
fit <- lm(C~I(A^2)+A+I(B^2)+B)

Now to optimize:
new <- data.frame(A=rep( seq(0, 8, 0.5),each=17),B=rep(seq(0, 8, 0.5),17))
new$C <- predict(fit, new)
new[which.min(new$C),]

This give me the values for A en B, where C is minimized.
Is there another way?

Kind regards

Bart
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