[R] Non-constant variance and non-Gaussian errors with gnls
paul.suckling at gmail.com
Tue Sep 2 15:13:40 CEST 2008
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)
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?
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