You might also consider the additive model fitting with rqss() in the
package quantreg.
url: www.econ.uiuc.edu/~roger Roger Koenker
email [EMAIL PROTECTED] Department of Economics
vox: 217-333-4558 University of Illinois
fax: 217-244-6678 Champaign, IL 61820
On Sep 3, 2008, at 12:01 PM, Monica Pisica wrote:
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 –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: r-help@r-project.org
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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