constant survivor wrote:ok. After I read Appendix D.4.3.
Here is what I tried to separately calculate mean for each sex (sex is coded in inp file as '1 0' and '0 1'):
1. build model phi(s+t)p(t) (with Design matrix)
Why that model? Should be phi(s*t)p(s*t) (you use the fully specifid model, not the most parsimonious). This model at is the default PIM-based model, which is all you need.
3. parameter indices selected: 1 to 60
Probabgly wrong, since you should (i) only include the survival parameters that (ii) aren't confounded.
4. Design matrix specifications: user specified
Correct...
5. for design matrix two columns: one for intercept (phi1-60='1', one for sex (phi1-30='1'; phi31-60='0')
This will work, but you need to correctly backtransform from the offset-coded linear model to the real paramter scale.
Far easier to simply have (i) a 2-column DM, (ii) first N rows (say 390, but I suspect thats wrong), make '1 0', and (iii) second N rows make '0 1'. This way, each column corresponds to the group, rather than and intercept and offset.
There is a discussion of this in Appendix E (the MCMC appendix) - starting at the bottom of p. 34. The idea applies equally well to variance components.
6. ok
The numerical output says:
beta-hat
-----------
0.129
0.065
My conclusion: mean survival of females is 0.129. Mean survival of males is 0.065 higher.
On the logit or sin scale, depending on your choice of link functions. You'd need to back-transform.
Please give me a hint.
Before you start messing with variance components, or MCMC, the assumption (which I pointed out) is that you're expert in design matrices, and have fully worked through Chapter 6. At which point, some of the above would be fairly obvious.