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. ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
