Hello Jeff et al,
This forum and Chapters 7 and 11 in the book have propelled me deep into my analysis but before I bite on the results, I would greatly appreciate a double check on my modelling approach in Rmark.
My data are monthly detection histories (8 per year) of 400+ individually identifiable lions spanning 8 years (64 observations for each lion). The age of lions at marking is estimated to the nearest year based on an established protocol using nose pigmentation and individual lions enter the data at all ages (spanning 0 to 12 years). Five age classes (spanning 2 year intervals up to 8 years of age) tend to capture the meaningful life stages. Consequently, capture histories are diverse, with some lions having entered the data in the first age class and reached the oldest age class by the end of the study and 'marking' of new lions occurring for all age classes in every year of the study.
Survival is thought to be related to age, being low at first, high in middle age classes, than decreasing again in the oldest age class. Survival is also thought to be dependent on gender owing to male harvest, and estimating age and sex specific survival is of primary interest and interactions between sex and age seem likely. Estimating abundance is also of interest, but secondary. There has been a general increase in search effort as the study progressed and hunting was banned midway through the study. It seems reasonable that capture heterogeneity might also exist.
I am interested in the CJSMixture model to estimate survival (with HugHet to estimate N) but I am also curious to explore the use of RD models that might do both (temporary emigration could almost certainly be an issue in this species).
I have fit models defining groups based on sex and age at first detection (in yrs) on import in process.data() then defining the 5 age classes with add.design.data(). I have added a hunting indicator variable to the survival design data based on a conditional statement on the time column to indicate the absence (0) or presence (1) of hunting.
I am interested in exploring other age class groups but my global model for survival is =~-1+ageclass*sex+sex:hunting) which should provide an intercept that is female cub survival, 4 additive terms for the four remaining age classes for females, an additive term for male cubs, 4 additive interaction terms for males in the four remaining age classes and additive term for males in just those years with hunting.
Is treating sex and age as groups for these kinds of questions the most efficient or should they be individual covariates? If sex and age should be modelled as individual covariates, how does the effect of TSM become conditional on the age at marking with my 5 age classes (my initial though is that an ageclass_at_marking*time term might do this?).
I am not certain at all how heterogeneity cold be modelled here with groups. If mixture, ageclass, and sex are potential effecting p, then wouldn’t each group need a group*mixture interaction term? Without such interaction terms (only and additive effect of mixture), is the assumption that the mixture is the same within each age class-sex group?
Thanks in advance!