Date:         Wed, 20 Jun 2007 12:33:42 -0700
Reply-To:     Dale McLerran <stringplayer_2@YAHOO.COM>
Sender:       "SAS(r) Discussion" <SAS-L@LISTSERV.UGA.EDU>
From:         Dale McLerran <stringplayer_2@YAHOO.COM>
Subject:      Re: AIC in proc nlin
In-Reply-To:  <5ac4815b0706201132j405ba2c8r847c6979383494b@mail.gmail.com>
Content-Type: text/plain; charset=iso-8859-1

--- Andrew Hill <hill.andrewd@GMAIL.COM> wrote:

> Thanks Matthew.
>
> I'll have to look at these more closely. I am hoping that I can find
> one that I can just move the model statement to and just use. It
> would
> make my live simpler.
>
> Does proc nlin mixed require one to use a mixed model?
>
> The one I'm currently using is an exponential model.
>
> change = exp(b0 + b1x1 + b2x2 + b3x3 + b4x4 + b5x5).
>
> I don't want to use proc reg because the data are definitely
> non-normal so the assumption that go into that are wrong for this
> data.
>
> Thanks,
>
> Andrew
>

Andrew,

From what you have written here, I would recommend the NLMIXED
procedure all the more.  The equation that you specified for AIC
in your previous post assumes that the residuals are normally
distributed.  But you indicate above that the response is not
normally distributed.  Therefore, you should not be using the
form you specified for computing AIC.

In response to your specific question as to whether the NLMIXED
procedure requires that you fit a mixed model, the answer is a
resounding NO!!!  Just search the SAS-L archives for some examples
(most contributed by yours truly) where NLMIXED is employed to model
all manner of likelihood functions.  What the NLMIXED procedure
does require is that one specify a distribution.  Based on the
specified distribution, NLMIXED computes maximum likelihood (or
approximate ML) estimates of the parameters of the model.

I can't tell from way over here, but it sounds as though you
minimized an error sum of squares function in NLIN.  Specifying
a distributional model can be more difficult.  There may not be
a distribution which is exactly appropriate for your data.  But
you can probably specify a distribution which models your
response reasonably well.

Dale


---------------------------------------
Dale McLerran
Fred Hutchinson Cancer Research Center
mailto: dmclerra@NO_SPAMfhcrc.org
Ph:  (206) 667-2926
Fax: (206) 667-5977
---------------------------------------



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