About marcioadg

SteveDenham · ‎04-04-2016

I see. Then your code will give likelihood ratio tests for the homogeneity of variance across groups for each of the grouping effects. I hope that is what you are looking for. Steve Denham

plf515 · ‎06-19-2015

Nothing in NLMIXED is as simple as it is in GLIMMIX. However, the NLMIXED documentation provides examples of models with covariates. See e.g. SAS/STAT(R) 9.2 User's Guide, Second Edition where they give this code for a model with several covariates: proc nlmixed data=inhaler corr ecorr; parms b0=0 b1=0 b2=0 b3=0 sd=1 i1=1 i2=1; bounds i1 > 0, i2 > 0; eta = b0 + b1*group + b2*time + b3*gt + u; if (clarity=1) then p = probnorm(-eta); else if (clarity=2) then p = probnorm(i1-eta) - probnorm(-eta); else if (clarity=3) then p = probnorm(i1+i2-eta) - probnorm(i1-eta); else p = 1 - probnorm(i1+i2-eta); if (p > 1e-8) then ll = log(p); else ll = -1e100; model clarity ~ general(ll); random u ~ normal(0,sd*sd) subject=sub; replicate freq; estimate 'thresh2' i1; estimate 'thresh3' i1 + i2; estimate 'icc' sd*sd/(1+sd*sd); run;

SteveDenham · ‎12-15-2014

Hi Marcio, Do you mean to edit the plot, or to work with the dataset behind the plot? If the latter, check out the OUTP= option to the MODEL statement in MIXED, and the OUTPUT statement in GLIMMIX. As far as editing the plot, that's not something I know much about. And if I missed the thrust of your question completely, I wouldn't be surprised. Steve Denham

SteveDenham · ‎11-10-2014

Cool! Now I have code to steal, er, borrow. Steve Denham

marcioadg · ‎10-17-2014

hahaha Now I saw what you did. Just replace b1 by the maximum value of X. Thanks a lot! Saving lives!

lvm · ‎08-19-2014

It is not clear what you are trying to do. I suspect you have two groups and want a different variance for each group?

marcioadg · ‎08-07-2014

lvm, Using the ilink on the lsmeans statement was actually giving the right information, but you are right I was only looking on the left side of the table (so used to it). It is giving me on the right side like you said, the information that I needed. Thanks a lot! I really appreciate it! Problem solved.

SteveDenham · ‎07-22-2014

I would expect the quadratic to be more easily explainable in biological terms (at least if it is quadratic to a plateau). Otherwise, linear is simpler to explain--there is an additive response above a certain cut-off. Steve Denham

marcioadg · ‎07-31-2014

Steve, thanks I will check that! Marcio

lvm · ‎07-21-2014

The code is exactly the same. That is the beauty of mixed model procedures like MIXED. Anyone interested in mixed models (i.e., models with multiple random effects) should read SAS for Mixed Models, 2nd edition (2006).

SteveDenham · ‎10-27-2014

It wouldn't be specific, but more as background on how to use IML to calculate what you need from the various matrixes, following 's note regarding the individual variances of the residual. Now it seems to me that a residual is a sum of two non-independent random variables, and the estimate and the Y value are correlated, so the key is getting the covariance between the two to estimate the variance of the residual. Surely someplace on the web has a matrix representation. Then it is just a matter of of following the right examples in the documentation and Rick's blog. Steve Denham

NareshAbburi · ‎12-14-2013

Try using COLLIN also as option in GLM. Thanks, Naresh

SteveDenham · ‎12-12-2013

I would say it's probably a good idea to compare IC's based on the same optimization technique. If you have trouble with one model/dataset, changing to a different method to get it to work for that instance means you should, at the very least, test that method on the other models/datasets. It would be optimal to use the same technique on all candidate models. Steve Denham

marcioadg · ‎12-12-2013

Really appreciate your input. I agree. I will check out these references.

marcioadg · ‎12-11-2013

Yes, Thank you Very much Steve

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