R help - Sep 2008 - Non-constant variance and non-Gaussian errors with gnls

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.

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.