Reeza, Thanks for the reply! I'm trying to address both annual (year-to-year) survival and seasonal (fall, winter, spring, summer) survival. I have two research questions: 1) what is the annual survival of a select avian species?; and 2) what is the seasonal survival of the select avian species? I have 2.5 years of survival data that I would like to use. We started sampling (or tagging) the avian species on 12/14/2010 and ended on March 15, 2011. We then re-started tagging during June-July 2011. Then again in January 1, 2012-March 15, 2012. Then once again from January 1, 2013-March 15, 2013. Individuals were tagged throughout all sampling periods. Likewise, individuals were monitored throughout the entire year during 2011 and 2012; however, monitoring ceased after July 31, 2013. Therefore, I planned to only calculate annual survival estimates for 2011 and 2012 since I had a full year of data for each time period. Thereafter, I would break each year into separate seasons (to see if a seasonal difference in survival existed). The seasonal periods would then include the 2013 data up to July 31, 2013. Does that make sense? Thanks again for the help! Best regards, Andy ... View more

Thanks Susan! I greatly appreciate your input! ... View more

i struggled for hours with this, worked on one computer and not on the next one sorting the data solved the issue ... View more

assuming these are string columns of date and time, you can make one timestamp with the code using the INPUT() function with the informat MDYAMPM. [pre]timestamp = input( date !!'-'!! time, mdyampm. ) ; ... View more

There is very little information in the GLIMMIX documentation on this. My previous posting from ages ago basically tells you what is given in the documentation. In essence, for the fixed effects, GLIMMIX apparently scales (standardizes) all the X variables (including interactions)  internally; this would make the optimization methods more stable (avoiding extremely large or small values). This scaling involves dividing each X by the square root of the sum of squares of the relevant X variables.This is not the usual standardization in linear models where the standard deviation of the Y is also utilized. After model fitting, 'unscaling' is done to get the parameters back on the scale of the original X (this is what is displayed). If you put in the STDCOEF option, you will get the scaled parameter estimates. You can read about this in the help documentation for GLIMMIX. Go to STDCOEF (under MODEL), and then go to NOCENTER option to see the equations. Centering means subtracting mean X (it can be done with or without this). I don't think you should overly interpret the standardized parameter estimates. These are not the same as those you get in linear models (they are not unitless because the Y units are still embedded in the parameters, based on the documentation). However, since  the response variable is the same for all the X predictors, then I suppose that one can roughly compare the magnitude of the standardized parameter estimates. But I would be careful even doing that. ... View more

See my response to your duplicate post in SAS Procedures. ... View more

As indicated elsewhere, you must have multiple observations for each ID (individual) if you have ID as a random effect. A statement such as plots=residualpanel should give you four graphs, including a 'normal' quantile plot (residual vs. quantile on a normal scale), residual vs. linear predictor (which is the estimate logit here), a histogram of residuals, and a boxplot. I prefer that you use plots=studentpanel to get the studentized residuals (actually conditional studentized residuals here). Easier to spot outliers. Since your response is binary (0/1), these diagnostic plots are challenging. Although you can do all the standard residual plots, but as stated by David Collett in Modelling Binary Data, "some of them become difficult to interpret." You can get strange looking residual plots. The Collett book has an excellent chapter on GLM diagnostics, although he does not deal with random effects (in that chapter). GLIMMIX does not (yet) have formal influence diagnostics (as found in MIXED). ... View more