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

SteveDenham, Thanks for the reply! Based on your explanation for Approach 2, wouldn't the second (or third, so on and so far) animal tagged on another date already be considered "at risk." The idea of the staggered entry approach is to treat each individual separately on their entry into the study. Therefore, if animal 1 is tagged on January 1, 2011 and animal 2 is tagged on March 15, 2011, the start day for each animal should be the day they are marked rather than using the first day the first animal is tagged. Does that make sense? To me, it makes sense to use your first example since each animal is added into the study along the way. In my case, I may have an animal enter the study on January 1, 2011 and another enter the study on March 15, 2011; therefore, I think it would be most appropriate (based on the idea of staggered entry) to calculate the number of days "at risk" from the date of capture until a selected end date. As animals fall out of the study due to death or radio-trasnmitter failure, do they get coded a 0 for censoring? For example (using the first approach), would you set your dataset up in the following manner: ID = Animal ID Days2Event = Days to death or end of study for 1 year Status = censored (0) or not ID    Days2Event     Status 1             35                0 2             364              1 3             190              0 Lastly, if the first date of capture for animal 1 is 12/14/2010 and I wanted to calculate annual survival (using approach 1 above where I set an end date (e.g., December 31, 2011), how would you treat the number of days between 12/14/2010-12/31/2011 as this is obviously longer than a year. This is where approach 2 seems more appropriate because you could set the annual survival based on the first day of capture. Just curious. Thanks, Andy ... View more

Reeza, Yes. According to the Analysis of Wildlife Radio-Tracking Data book by Garrott and White (1990), they state, "PROC LIFETEST of SAS can be used to compute the Kaplan-Meier estimates for these data if all animals enter the experiment at the same time." This is not the case for wildlife telemetry datasets so the SAS code must be adjusted to account for staggered entry. Likewise, Pollock et al. (1989) published a paper on adjusting the Kaplan-Meier survival calculation for a staggered entry approach and mentioned at the conclusion of the paper that PROC LIFETEST could work if all animals entered the study at the same time. Again, this is not the case for wildlife telemetry studies since animals are added or removed throughout the entire study process. Hopefully this helps with your question. I'm still looking for a way to run the analysis in SAS. Hopefully one does exist. Best regards, Andy ... View more

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

Sorry Susan, I forgot to post my first code that I ran this morning that gave me the first LOG Output! See below: PROC GLIMMIX DATA=HABITAT; BY EXPOSURE TREAT DN; CLASS ID TREAT YEAR HABVALUE; MODEL VALUE (EVENT = '1') = HABVALUE ROAD_LOG ELEVATION ELEVATION_QUAD SLOPE SLOPE_QUAD / DIST=BINARY LINK=LOGIT SOLUTION; RANDOM ID(TREAT) TREAT YEAR / TYPE=VC; RANDOM ELEVATION SLOPE ROAD HABVALUE / TYPE=VC SUBJECT=ID; RUN; ... View more

Susan, Sorry for the confusion about study description to help with the questions that I'm asking! My study was conducted on a 1,861 hectare property and was divided into 3 treatments of approximately equal size and vegetative composition. The three treats are control, no hunters; low density, 1 hunter/101 ha; and high density, 1 hunter/30 ha. I decided to evaluate exposure to hunting pressure (initial, days 1-3; prolonged, days 4-13) to assess differences in habitat selection and by treatment levels. These are the two categorical variables in the model. The other categorical variable is habitat value (1 = mixed, 2 = forest, and 3 = field). To conduct a logistic regression analysis using PROC GLIMMIX, I allocated random GPS locations on the study area and paired them with actual animal locations. I've been working with two biometricians and we're all stumped on the issues that I'm having in this analysis. We decided to take a more simplistic approach to get away from the three-way interactions using a "BY" statement for EXPOSURE, TREAT, AND DN to use more of an estimation approach since with a large daatset we're bound to find something statistically significant. See my new model below: PROC GLIMMIX DATA=HABITAT; BY EXPOSURE TREAT DN; CLASS ID TREAT YEAR HABVALUE; MODEL VALUE (EVENT = '1') = HABVALUE ROAD_LOG ELEVATION ELEVATION_QUAD SLOPE SLOPE_QUAD / DIST=BINARY LINK=LOGIT SOLUTION; RANDOM ID(TREAT) TREAT YEAR / TYPE=VC; RANDOM ELEVATION SLOPE ROAD HABVALUE / TYPE=VC SUBJECT=ID; RUN; I have two years of data (YEAR), TREAT(control, low, and high), and ID(TREAT) selection of resources made by an animal are more similar or correlated within each treatment. Lastly, RANDOM ELEVATION SLOPE ROAD HABVALUE are added to models the correlation of resource selection within individuals, which means that selection of (e.g., elevation) between intervals is correlated within an individual. I ran this more simplistic model this morning and here's the errors I received back: NOTE: The GLIMMIX procedure is modeling the probability that Value='1'. WARNING: Pseudo-likelihood update fails in outer iteration 5. NOTE: Did not converge. NOTE: The above message was for the following BY group: EXPOSURE=Initial TREAT=Control DN=Diurnal NOTE: The GLIMMIX procedure is modeling the probability that Value='1'. WARNING: Pseudo-likelihood update fails in outer iteration 3. NOTE: Did not converge. NOTE: The above message was for the following BY group: EXPOSURE=Initial TREAT=Control DN=Nocturnal NOTE: The GLIMMIX procedure is modeling the probability that Value='1'. WARNING: Pseudo-likelihood update fails in outer iteration 3. NOTE: Did not converge. NOTE: The above message was for the following BY group: EXPOSURE=Initial TREAT=High DN=Diurnal NOTE: The GLIMMIX procedure is modeling the probability that Value='1'. WARNING: Pseudo-likelihood update fails in outer iteration 4. NOTE: Did not converge. NOTE: The above message was for the following BY group: EXPOSURE=Initial TREAT=High DN=Nocturnal NOTE: The GLIMMIX procedure is modeling the probability that Value='1'. WARNING: Pseudo-likelihood update fails in outer iteration 3. NOTE: Did not converge. NOTE: The above message was for the following BY group: EXPOSURE=Initial TREAT=Low DN=Diurnal NOTE: The GLIMMIX procedure is modeling the probability that Value='1'. WARNING: Pseudo-likelihood update fails in outer iteration 7. NOTE: Did not converge. NOTE: The above message was for the following BY group: EXPOSURE=Initial TREAT=Low DN=Nocturnal NOTE: The GLIMMIX procedure is modeling the probability that Value='1'. WARNING: Pseudo-likelihood update fails in outer iteration 2. NOTE: Did not converge. NOTE: The above message was for the following BY group: EXPOSURE=Prolong TREAT=Control DN=Diurnal NOTE: The GLIMMIX procedure is modeling the probability that Value='1'. WARNING: Pseudo-likelihood update fails in outer iteration 4. NOTE: Did not converge. NOTE: The above message was for the following BY group: EXPOSURE=Prolong TREAT=Control DN=Nocturnal NOTE: The GLIMMIX procedure is modeling the probability that Value='1'. WARNING: Pseudo-likelihood update fails in outer iteration 2. NOTE: Did not converge. NOTE: The above message was for the following BY group: EXPOSURE=Prolong TREAT=High DN=Diurnal NOTE: The GLIMMIX procedure is modeling the probability that Value='1'. WARNING: Pseudo-likelihood update fails in outer iteration 2. NOTE: Did not converge. NOTE: The above message was for the following BY group: EXPOSURE=Prolong TREAT=High DN=Nocturnal NOTE: The GLIMMIX procedure is modeling the probability that Value='1'. WARNING: Pseudo-likelihood update fails in outer iteration 3. NOTE: Did not converge. NOTE: The above message was for the following BY group: EXPOSURE=Prolong TREAT=Low DN=Diurnal NOTE: The GLIMMIX procedure is modeling the probability that Value='1'. NOTE: Did not converge. NOTE: The above message was for the following BY group: EXPOSURE=Prolong TREAT=Low DN=Nocturnal NOTE: PROCEDURE GLIMMIX used (Total process time): real time 49.51 seconds cpu time 40.78 seconds So I decided to remove the last RANDOM statment and used the following model: PROC GLIMMIX DATA=HABITAT; BY EXPOSURE TREAT DN; CLASS ID YEAR TREAT HABVALUE; MODEL VALUE (EVENT = '1') = HABVALUE ROAD_LOG ELEVATION ELEVATION_QUAD SLOPE SLOPE_QUAD / DIST=BINARY LINK=LOGIT SOLUTION; RANDOM ID ID(TREAT) TREAT YEAR / TYPE=VC; RUN; I received the message: "Estimated G Matrx is not positive definite" again and you mentioned that there are some ways to tweek the model to eliminate this error. Additionally, I only had one Warning: Pseudo-likelihood update fails in outer iteration 3. NOTE: Did not converge. See rest of LOG Output below: NOTE: The GLIMMIX procedure is modeling the probability that Value='1'. NOTE: Convergence criterion (PCONV=1.11022E-8) satisfied. NOTE: The above message was for the following BY group: EXPOSURE=Initial TREAT=Control DN=Diurnal NOTE: The GLIMMIX procedure is modeling the probability that Value='1'. NOTE: Convergence criterion (PCONV=1.11022E-8) satisfied. NOTE: Estimated G matrix is not positive definite. NOTE: The above message was for the following BY group: EXPOSURE=Initial TREAT=Control DN=Nocturnal NOTE: The GLIMMIX procedure is modeling the probability that Value='1'. WARNING: Pseudo-likelihood update fails in outer iteration 3. NOTE: Did not converge. NOTE: The above message was for the following BY group: EXPOSURE=Initial TREAT=High DN=Diurnal NOTE: The GLIMMIX procedure is modeling the probability that Value='1'. NOTE: Convergence criterion (PCONV=1.11022E-8) satisfied. NOTE: Estimated G matrix is not positive definite. NOTE: The above message was for the following BY group: EXPOSURE=Initial TREAT=High DN=Nocturnal NOTE: The GLIMMIX procedure is modeling the probability that Value='1'. NOTE: Convergence criterion (PCONV=1.11022E-8) satisfied. NOTE: The above message was for the following BY group: EXPOSURE=Initial TREAT=Low DN=Diurnal NOTE: The GLIMMIX procedure is modeling the probability that Value='1'. NOTE: Convergence criterion (PCONV=1.11022E-8) satisfied. NOTE: Estimated G matrix is not positive definite. NOTE: The above message was for the following BY group: EXPOSURE=Initial TREAT=Low DN=Nocturnal NOTE: The GLIMMIX procedure is modeling the probability that Value='1'. NOTE: Convergence criterion (PCONV=1.11022E-8) satisfied. NOTE: Estimated G matrix is not positive definite. NOTE: The above message was for the following BY group: EXPOSURE=Prolong TREAT=Control DN=Diurnal NOTE: The GLIMMIX procedure is modeling the probability that Value='1'. NOTE: Convergence criterion (PCONV=1.11022E-8) satisfied. NOTE: Estimated G matrix is not positive definite. NOTE: The above message was for the following BY group: EXPOSURE=Prolong TREAT=Control DN=Nocturnal NOTE: The GLIMMIX procedure is modeling the probability that Value='1'. NOTE: Convergence criterion (PCONV=1.11022E-8) satisfied. NOTE: Estimated G matrix is not positive definite. NOTE: The above message was for the following BY group: EXPOSURE=Prolong TREAT=High DN=Diurnal NOTE: The GLIMMIX procedure is modeling the probability that Value='1'. NOTE: Convergence criterion (PCONV=1.11022E-8) satisfied. NOTE: Estimated G matrix is not positive definite. NOTE: The above message was for the following BY group: EXPOSURE=Prolong TREAT=High DN=Nocturnal NOTE: The GLIMMIX procedure is modeling the probability that Value='1'. NOTE: Convergence criterion (PCONV=1.11022E-8) satisfied. NOTE: Estimated G matrix is not positive definite. NOTE: The above message was for the following BY group: EXPOSURE=Prolong TREAT=Low DN=Diurnal NOTE: The GLIMMIX procedure is modeling the probability that Value='1'. NOTE: Convergence criterion (PCONV=1.11022E-8) satisfied. NOTE: Estimated G matrix is not positive definite. NOTE: The above message was for the following BY group: EXPOSURE=Prolong TREAT=Low DN=Nocturnal NOTE: PROCEDURE GLIMMIX used (Total process time): real time 14.18 seconds cpu time 10.98 seconds Based on the new SAS code using a more simplistic approach to get away from 3-way interactions, I think I'm getting closer. Overall, I want to estimate the probability of animals selecting different resources within each treatment. Additionally, I'm not missing any data in the entire dataset. Do you have any additional suggestions based on where I'm at now? Thank you very much for all of your input! ... View more

Thank you Susan! I'm using your suggestions to review the model structure to find the problem. I greatly appreciate your input and will let you know how the issue was solved. ... View more

TYPO: A friend of mine ran an even more complex dataset than mine using a normal desktop computer but for some reason I can't get the model to run correctly. ... View more

So are you saying that I can't create the DATE2 column? The INFORMAT date and time are in the following format: Date = 11/22/2008 and Time = 09:22:26. My goal is to create a time stamp joining both date and time. I see your point about FORMAT and INFORMAT must be the same variable type but doesn't the code reflect that? Sorry for the confusion! Thank you very much for your help! ... View more

Ok! I've read over the comments you posted below and still am a little confused. See my SAS code below: DATA DATETIME; SET DEER1; RUN; DATA CORRECT; SET DATETIME; INFORMAT DATE MMDDYY10.; DATE2=DATE; FORMAT DATE2 MMDDYY10.; HRID=HMS(HOUR,MINUTE,SECOND); H_M_S=HRID; FORMAT H_M_S TIME.; DTID=DHMS(DATE2,HOUR,MINUTE,SECOND); RUN; PROC PRINT; RUN; DATA HABITAT; SET CORRECT; RUN; Do you see any inherent problems? I'm importing the data from an Excel file because I have approximately 100,000 lines of data and figured it would be easier to make sure things don't get jumbled up in the process of using DATALINES. I ran the code last night and kept getting the following errors: 46573 DATA DATETIME; 46574 SET DEER1; 46575 RUN; NOTE: There were 46492 observations read from the data set WORK.DEER1. NOTE: The data set WORK.DATETIME has 46492 observations and 37 variables. NOTE: DATA statement used (Total process time): real time 0.10 seconds cpu time 0.09 seconds 　 46576 DATA CORRECT; 46577 SET DATETIME; 46578 INFORMAT DATE MMDDYY10.; --------- 48 ERROR 48-59: The informat $MMDDYY was not found or could not be loaded. 46579 DATE2=DATE; 46580 FORMAT DATE2 MMDDYY10.; --------- 48 ERROR 48-59: The format $MMDDYY was not found or could not be loaded. 46581 HRID=HMS(HOUR,MINUTE,SECOND); 46582 H_M_S=HRID; 46583 FORMAT H_M_S TIME.; 46584 DTID=DHMS(DATE2,HOUR,MINUTE,SECOND); 46585 RUN; NOTE: Character values have been converted to numeric values at the places given by: (Line):(Column). 46584:11 NOTE: The SAS System stopped processing this step because of errors. WARNING: The data set WORK.CORRECT may be incomplete. When this step was stopped there were 0 observations and 41 variables. NOTE: DATA statement used (Total process time): real time 0.03 seconds cpu time 0.06 seconds 　 46586 PROC PRINT; RUN; NOTE: No observations in data set WORK.CORRECT. NOTE: PROCEDURE PRINT used (Total process time): Thank you very much for any help that you can give me! ... View more