About marcioadg

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.

marcioadg · ‎08-07-2014

Thanks lvm, but the problem are the lsmeans estimates, even with the ilink option on the lsmeans statement it is retuning the negative values. Any thoughts?

marcioadg · ‎08-07-2014

Thanks Reeza, Please let me know if this helps. Note this is only one of the codes I tried. Thanks! proc glimmix data=sows method=quad plots=residualpanel(conditional marginal) ; CLASS week WeighGr90 ; MODEL CV = WeighGr90 / dist=poisson ; *random _residual_ / group=vargroup ; random intercept / subject=week ; lsmeans WeighGr90/pdiff; output out=igausout pred=p resid=r STUDENT=std; run;

marcioadg · ‎08-07-2014

Hi, The QQ Plot suggests that the studentized residuals of the response variables are not normally distributed. When I run it with a poisson distributions (or others) it is outputting completely unfeasible estimates. It should be around 0.20 (which would be 20%) and it is estimating negative numbers. I thought could be scaling issues (multiplied by 100) but it is not. I have tried different estimation methods too (LaPlace, Quadrature). Not sure if it could be the linkfunction? Any thoughts? Thanks, Marcio

marcioadg · ‎07-31-2014

Steve, thanks I will check that! Marcio

marcioadg · ‎07-21-2014

lvm, thanks. This is what I have used. data ppp; set ppp; resid=y-pred; resid_t=resid; run; proc standard data=ppp std =1 out =pred_std; var resid_t; run;

marcioadg · ‎07-21-2014

I appreciate all the input you gave me! That will help me deciding on the model. Kodml - in that example the variable definitely has biological meaning. Thanks! Marcio

marcioadg · ‎07-19-2014

I have two models in NLMIXED (one linear-broken line and the other a quadratic broken-line) and they have the exactly same AIC, BIC and variance components. Their AIC, BIC are smaller than other models I am comparing them to. I am using this models to estimate nutritional requirements of animals. Now, I am normally reporting only the estimated requirement of the best fitting model. In this case, should I report both of them? Or there is any further analysis/investigation can be done to find which one is better? Thank you, Marcio

marcioadg · ‎07-19-2014

So Just to make sure we are on the same page - I had a model where the interaction was not significant, but the AIC/BIC were lower in that model compared to when I took the interaction out. Based on what you all said I should take the interaction out - even though the fit is better?

marcioadg · ‎07-18-2014

Thanks Kodmfl, but how does the AIC/BIC plays on it?

marcioadg · ‎07-18-2014

If the interaction is not significant but the model has a better fit (AIC/BIC), should I keep the interaction in the model? Thanks, Marcio

marcioadg · ‎07-18-2014

Thank you Paige; Here is how I would code as a complete block design for this specific experiment. PROC mixed data=growth ; CLASS Trt block; MODEL response = Trt; Lsmeans Trt/diffs; random block; How would one code if it was an incomplete block design? Thank you

marcioadg · ‎07-18-2014

I went through 2 different books and they are not too clear because they are testing other things (slopes,etc) at the same time. How different is the Proc Mixed code from a complete block design vs an incomplete block design? Thanks, Marcio

marcioadg · ‎07-09-2014

Perfect Steve. Thanks!

marcioadg · ‎07-09-2014

Thank you, that is very helpful! Can I replace _residual_ for my block variable?

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