```Date: Wed, 25 Jun 2008 23:07:08 -0700 Reply-To: stringplayer_2@yahoo.com Sender: "SAS(r) Discussion" From: Dale McLerran 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 wrote: > From: 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 --------------------------------------- ```

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