LISTSERV at the University of Georgia
Menubar Imagemap
Home Browse Manage Request Manuals Register
Previous (more recent) messageNext (less recent) messagePrevious (more recent) in topicNext (less recent) in topicPrevious (more recent) by same authorNext (less recent) by same authorPrevious page (June 2008, week 4)Back to main SAS-L pageJoin or leave SAS-L (or change settings)ReplyPost a new messageSearchProportional fontNon-proportional font
Date:         Wed, 25 Jun 2008 23:07:08 -0700
Reply-To:     stringplayer_2@yahoo.com
Sender:       "SAS(r) Discussion" <SAS-L@LISTSERV.UGA.EDU>
From:         Dale McLerran <stringplayer_2@YAHOO.COM>
Subject:      Re: nlmixed for random coefficient model
In-Reply-To:  <6e32250a-416c-4515-8e1a-ec107a77a764@26g2000hsk.googlegroups.com>
Content-Type: text/plain; charset=us-ascii

--- On Wed, 6/25/08, inbox@MYSATURDAYSELF.COM <inbox@MYSATURDAYSELF.COM> wrote:

> From: inbox@MYSATURDAYSELF.COM <inbox@MYSATURDAYSELF.COM> > Subject: nlmixed for random coefficient model > To: SAS-L@LISTSERV.UGA.EDU > Date: Wednesday, June 25, 2008, 8:51 PM > > Hi all, I am using nlmixed for random coefficient model, it's a linear > regression model y=alpha+beta*x+theta, so I suppose all i should do > is add a line with > > random theta~normal(0,sigma**2) subject=id > > is this right? Thanks

Well, we don't see all of your code. I presume that you have a couple of statements something like

eta = alpha + beta*x + theta; y ~ normal(eta, sigma_error**2);

If you have such code, then adding your RANDOM statement as specified above should be all that you would need. Note, though, that I prefer to parameterize the models such that the square root of the variance must be positive. There is no such constraint with the parameterization on either the RANDOM or MODEL statements shown above. However, it is easy to revise the code such that the square root of the variance is positive. The code below is how I would write my statements:

proc nlmixed data=mydata; eta = alpha + beta*x + theta; model y ~ normal(eta, exp(2*log_sigma_err)); random theta ~ normal(0, exp(2*log_sigma_theta)) subject=id; run;

Now, even if log_sigma_err or log_sigma_theta is negative, the square root of the variance is positive.

But why are you using NLMIXED for this model instead of the MIXED procedure? The MIXED procedure is really preferable for a situation where you have a Gaussian response.

Dale

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


Back to: Top of message | Previous page | Main SAS-L page