```Date: Wed, 16 Jul 2008 10:54:28 -0500 Reply-To: Robin R High Sender: "SAS(r) Discussion" From: Robin R High Subject: Re: Simulated ZIP: SAS for Mixed Models, 2nd Ed. Comments: To: Kevin Viel In-Reply-To: <200807161341.m6GAkVrk026055@mailgw.cc.uga.edu> Content-Type: text/plain; charset="US-ASCII" Hi Kevin, Regarding the second question, computation of lambda is the linear predictor, x_i*B for each i, which is exponentiated to get the mean_i = EXP(x_i*B) for each observation and since the distribution is Poisson, it computes the variance as well. Since this is Poisson regression, the iid assertion isn't relevant (as it is in linear regression with normally distributed errors), since the variance is defined to change with the mean for Poisson. Of course, there is still a possibility for overdispersion with a ZIP model, so still need to be aware of that feature. Robin High UNMC Kevin Viel Sent by: "SAS(r) Discussion" 07/16/2008 08:42 AM Please respond to Kevin Viel To SAS-L@LISTSERV.UGA.EDU cc Subject Simulated ZIP: SAS for Mixed Models, 2nd Ed. I finally got my hands on a copy of the second edition, however I note the name change. The first edition was the SAS *System* for Mixed Models. As I am focused on zero-inflated models at the moment, I jumped staight to page 589. The authors offer the following: /* Littell et al. SAS for Mixed Models, Second Edition Page 589-590 */ %let pi = 0.27 ; data zip ; do s = 1 to 100 ; u = rannor( 556712 ) ; do i = 1 to 20 ; x = int( ranuni( 0 ) * 100 ) ; y = int( rannor( 0 ) * 100 ) ; if ( ranuni( 0 ) < &pi ) then do ; count = 0 ; lambda = . ; end ; else do ; lambda = exp( -2 + 0.01 * x + 0.01 * y + u ) ; count = ranpoi( 0 , lambda ) ; end ; output ; end ; end ; drop i u lambda ; run ; I was disappointed to see the use of a zero seed. I assert the polemic that one should use non-zero seeds in pedagogy, so that the students arrive at the exact same data. I also have a great confusion. For each i from the non-zero part, lambda should have a different value. Does this not immediately violate the assumptions of a regression model, namely iid? Thanks in advance, Kevin ```

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