[R] Non-constant variance and non-Gaussian errors with gnls

[Paul Suckling]

Well, it looks like I am partly answering my own question. gnls is
clearly not going to be the right method to use to try out a
non-Gaussian error structure. The "ls"=Least Squares in "gnls" means
minimising the sum of the square of the residuals ... which is
equivalent to assuming a Gaussian error structure and maximising the
likelihood. So gnls is implicitly Gaussian.

Still, there must be some packages out there that can be applied to
non-linear regression with not-necessarily-Gaussian error structures
and weighting, although I appreciate that that's a difficult problem
to solve. Does anyone here know of any?

Thank you,

Paul

2008/9/2 Paul Suckling <paul.suckling at gmail.com>:
> I have been using the nls function to fit some simple non-linear
> regression models for properties of graphite bricks to historical
> datasets. I have then been using these fits to obtain mean predictions
> for the properties of the bricks a short time into the future. I have
> also been calculating approximate prediction intervals.
>
> The information I have suggests that the assumption of a normal
> distribution with constant variance is not necessarily the most
> appropriate. I would like to see if I can obtain improved fits and
> hence more accurate predictions and prediction intervals by
> experimenting with a) a non-constant (time dependent) variance and b)
> a non-normal
> error distribution.
>
> It looks to me like the gnls function from the nlme R package is
> probably the appropriate one to use for both these situations.
> However, I have looked at the gnls help files/documentation and am
> still left unsure as to how to specify the arguments of the gnls
> function in order to achieve what I want. In particular, I am unsure
> how to use the params argument.
>
> Is anyone here able to help me out or point me to some documentation
> that is likely to help me achieve this?
>
> Thank you.
>



-- 
Nashi Power.
http://nashi.podzone.org/
Registered address: 7 Trescoe Gardens, Harrow, Middx., U.K.