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# Re: st: simulation to derive power for GLM multiple regression

 From Jake To statalist@hsphsun2.harvard.edu Subject Re: st: simulation to derive power for GLM multiple regression Date Tue, 9 Mar 2010 12:18:17 -0800 (PST)

```Austin,

Thank you for your advice.  Yes, this is a post-hoc power analysis, so the goal is to estimate power rather than estimate necessary sample size.  You wrote that I could draw errors from the empirical distribution of errors in the estimated model.  I wanted to make sure of something -- you said that I could sample from the estimated errors -- I assume then that I would sample *with* replacement such that each value had an equal probability of being selected?

Also, since I am interested mainly in whether there is sufficient power to detect one particular effect, I would prefer to treat the parameters for the covariates as given.  Would this make sense?

Thank you again for you help,

Jacob Felson

Austin Nichols wrote:
Jake <daedalus702@yahoo.com> :
If you have the data on explanatory variables, you need only specify
true coefs and draw error terms.  This involves far fewer choice than
generating all the  explanatory variables, with various possible joint
distributions.  But you still have to make choices about what
combinations of parameter values to test, and what family of
distributions to draw errors from.  Or you can use your estimated
parameter values in the current dataset, and perhaps twice and half
each, and draw errors from the empirical distribution of errors in
your estimated model.  If you have a negative binomial, perhaps you'd
like to specify errors as being multiplicative, so y=f(X.b)e and the
estimated errors are y/f(X.bhat) which you can then sample from in