On Mar 12, 2008, at 10:11 AM, Verkuilen, Jay wrote:
David Airey wrote:
I have a typical pilot data set.<
A few things:
(1) Why not try xtmelogit? Can't do any harm to see what it does.
Joseph Coveney posted code comparing xtlogit with ttests on summary
statistics. The two results from his code relevant to my data were:
delta = 0.000 rho = 0.050
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------------
delta | 300 .0037128 .3009899 -.9131391 .7421806
rho | 300 .0402976 .0260353 2.02e-10 .1294623
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------------
pos_p_r | 300 .1 .3005013 0 1
pos_p_t | 300 .0466667 .2112763 0 1
pos_p_l | 300 .0466667 .2112763 0 1
delta = 1.000 rho = 0.050
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------------
delta | 300 1.003721 .2986725 .0978231 1.812812
rho | 300 .0371925 .0277583 8.65e-10 .1519448
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------------
pos_p_r | 300 .9233333 .2665064 0 1
pos_p_t | 300 .8566667 .3509979 0 1
pos_p_l | 300 .86 .3475668 0 1
In general I follow your advice, thinking that the right model for a
small data set can't be a bad thing. Following Joseph's posts, I think
the key to making informed choices is to be able to program
simulations to see where each approach succeeds and fails. This is one
of the primary advantages to Stata. Stata makes it easier to do this.
Very easy for Joseph! Five times less easy for me. But I learn more
every time someone posts code! Very fun.
(2) Resampling methods seem tailor-made for this kind of problem.
Stata's got really impressive support for clustering and
stratification
for both jackknifing and bootstrapping.
That's a good thought.
(3) On a problem like this, I'd give serious consideration to a fully
Bayesian model.
I not facile with Bayesian methods in practice.
Thanks,
-Dave
Jay
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