Things to do following my meeting:
(1) Observation Simulations lead us to some questions/ideas: We see that the parameters are high prior driven. And in actually we see an interaction between sample size and block structure in that a larger sample size would make things more data driven, but networks with strong block structure appear to be more data driven.
- This is something to think about. What would we need to do to make the model fit more data driven?
- Are the traceplots of the gammas really different (weak vs strong)? Look at the trace plots of the log(gamma) to see.
- It’s interesting that the point estimates don’t appear to be affected by the difference in prior. But we don’t know that for sure unless we look at variability. Do a heat map type plot (roygbiv color) of the point estimates and another one for variance.
(2) What is going on with my Covariate MM stuff?
- Evaluate the log likelihood at a sequence from 0 to 1 at the new value of B proposed. And look at what it looks like. It should look normal.
- Fix B at the truth and see what’s going on with alpha.
- Propose new values for logit(B) instead of B and do a normal random walk instead. An alternative would be a uniform centered at B_0 (see notes).