[R] Fit non-lineair 3D Data

[Antonio, Fabio Di Narzo]

2005/12/18, Bart Joosen <bartjoosen at hotmail.com>:
> Hi,
>
> I have a problem with fitting a model:
> I made a dataframe with this data:
>     a <- 1:3
>     b <- 1:3
>     c <- c(3, 2, 3, 2, 1, 2, 3, 2, 3)
>     df <- expand.grid(a,b)
>     df$result <- c
>     names(df) <- c("A","B", "result")
>
> Although I can make a graph of the data:
>     require(lattice)
>     wireframe(result~A*B, data=df)
>
> I can't get a model to fit this 3D data.
>
>
> I have tried the lm function, but its easy to see that this a non lineair
> data set. The use of poly also isn't a solution.
> I tried to use nls, but there seems to be an error?
>     mod <- nls(result~A:B, df, start = list (A=0, B=0))
>     Error in qr.qty(QR, resid) : 'qr' and 'y' must have the same number of
> rows

That's not the proper way to use 'nls'. You have to already know which
model to fit to data. 'nls' doesn't magically find it for you.

Watch at: "An Introduction to R"->"Statistical Models in R"->
"Nonlinear least squares and maximum likelihood models" for an
overview.

There are various functions in R packages to do nonparametric even
nonlinear fitting. Anyway, you should keep in mind that you really
have'nt a lot of observations, so parametric models should be more
appropriate.

Antonio, Fabio Di Narzo.

>
> Is there a way to fit this data?
>
>
> Thanks for your time by reading this, hopefully I will get an answer.
>
> Bart Joosen
>
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>
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