hello Steve, First of all thank you so much for your answer and second of all, sorry for my lousy explanation of the design, i will try to clarify it better now My design consist on the causes of death of some animals, i have several data for each animal and the options ((response variable)are alive, dead by predator 1 and dead by predator 2. And in those locations where they are alive or predated they are in certain landscape environments. the idea is to understand if the odds of dying by one cause or other increase in certain environments. At the same time data were taken 2 different years and the animals are grouped by family groups. i hope i was more clear now :S 1) YES, landscapevariable1 and 2 are CERTAINLY continuous measures AND INDEPENDENT VARIABLES. 2)YES, the families and individuals observed in any one YEAR are AS YOU ASSUME different from one year to the other. 3) I think the response variable HAS TO BE Classified as Class according to the mannual in the cases of multinomial NOMINAL regressions. see for example this case found in the manual proc glimmix; class preference; model preference(ref=first) = feature price / dist=multinomial link=glogit; random intercept / subject=store group=preference; run; 4) Thank you for the suggestion of the random effects, it makes sense and i am totally going to try that , thanks a lot. 5) the only problem is that glimmix multinomial link=glogit they ask me to introduce the GROUP= option into ALL random statements if not it gives the error "Nominal models require that the response variable is a group effect on RANDOM statements. You need to add 'GROUP=response" so your random would end up being: random intercept family/subject=individual group=response; random year/subject=individual(family) group=response; i hope i was more clear now :S, thanks a lot anyway kj
... View more