I wish to run some Monte-Carlo simulations of several discrete time
competing risk survival analysis regression models, and have some
questions about how best to handle this using -simulate-. My intended
programs look something like the following in outline:
~~~~~~~~~~~
create data set containing 'true' explanatory variables (X1-Xk).
-use- this data set
program define mysim
... generate depvars YA, YB, and additional 'duration' vbles (D1-Dt),
from the X1-Xk and uniform()
... -ml-based regression #1 of YA, YB, on X1-Xk and D1-Dt (2-eqn model)
... -ml-based regression #2
... -ml-based regression #3
[for each model, the main results of interest
are, for each of the 2 equations, the estimated
coefficients on the X1-Xk, and their associated standard errors]
... return scalar ???
... return matrix ???
end
simulate mysim ....
~~~~~~~~~
Questions:
1. What is the most efficient way of returning as saved results all
the estimated coefficients and standard errors? If I define program
mysim as rclass, does this mean that I have to have an -return scalar
<result>- line for each and every one of the coeffs and SEs, or is there a
better way? [For example, I see that there is a -return matrix- command,
but I am not sure if it can be used in this context, as the matrices
presumably wouldn't accumulate in the simulation data set.]
2. Complication: sometimes the -ml-based regressions may fall over because
of collinearities among the D1-Dt variables. Does one simply trap these
errors in a standard way? [I think there has been Statalist posting on
this but I couldn't find it in the archive.] And should one change
the number of reps if this happens?
3. What is the -nowarn- option in the example in [R] simulate, p.73?
It doesn't appear to be in the -simulate- syntax diagram on p.69
(though there is a -nocheck- option cited there)
thanks
Stephen
=============================================
Professor Stephen P. Jenkins <[email protected]>
Institute for Social and Economic Research (ISER)
University of Essex, Colchester CO4 3SQ, UK
Phone: +44 1206 873374. Fax: +44 1206 873151.
http://www.iser.essex.ac.uk
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