package quantreg.

>

> Hi Paul,

>

> Take a look at gam() from package mgcv (gam = generalized additive

> models), maybe this will help you. GAMs can work with other

> distributions as well. Generalized additive models consist of a

> random component, an additive component, and a link function

> relating these two components. The response Y, the random component,

> is assumed to have a density in the exponential family. I am not

> sure about errors, though.

>

> This modeling package uses penalized versions of the least squares

> or maximum â€“likelihood / IRLS methods. The penalizing or smoothing

> factor is calculated by minimizing the generalized cross validation

> (GCV), or the information criterion (AIC) scores using a Newton type

> optimization based on exact first and second derivatives, as

> described in Wood (2008).

>

> Wood, S.N. (2004) Stable and efficient multiple smoothing parameter

> estimation for generalized additive models.Journal of the American

> Statistical Association. 99:673-686.

>

> Wood, S.N. (2006) Generalized Additive Models: An Introduction with

> R. Chapman and Hall/CRC.

>

> Wood, S.N. (2008) Fast stable direct fitting and smoothness

> selection for generalized additive models. Journal of the Royal

> Statistical Society (B) 70(2): - .

>

>

> Hope this helps some,

>

> Monica

>

> -----------------------------------------------------------

> Message: 94

> Date: Wed, 3 Sep 2008 09:24:10 +0100

> From: "Paul Suckling"

> Subject: Re: [R] Non-constant variance and non-Gaussian errors with

> gnls

> To:

[hidden email]
> Message-ID:

>

> Content-Type: text/plain; charset=UTF-8

>

> 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 :

>> 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.

>>

>

>

>

>

> _________________________________________________________________

>

> Live.

>

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