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From |
Austin Nichols <austinnichols@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: simulation to derive power for GLM multiple regression |

Date |
Tue, 9 Mar 2010 11:36:18 -0500 |

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 your simulation. Is this a post-hoc power analysis? On Tue, Mar 9, 2010 at 10:51 AM, Jake <daedalus702@yahoo.com> wrote: > Hello, > > I am interested in learning details about how to conduct simulations to calculate power for a test of a single coefficient in a GLM (negative binomial) multiple regression model. I am somewhat familiar with the relevant methods outlined by Feiveson (2002). However, what I am curious about which Feiveson does not discuss is how best to simulate when there are k covariates, in addition to the coefficient of interest. > > I assume I would treat the covariates as fixed -- using the real (already collected) data in the simulation, in other words. Then I imagine that I would simulate only the variable for the coefficient of interest. I'm not certain of how to generate this random variable so that it would be (asymptotically) correlated with all of the other variables in specified ways. I imagine I would use the cholesky method? But many of the variables are not normally distributed -- some are dichotomous, etc. > > I would appreciate any help that you might provide. > > > > Thanks, > > Jacob Felson > Assistant Professor > Dept. of Sociology > William Paterson University > > Reference > > Feiveson, A.H. 2002 "Power by simulation." The Stata Journal 2: 107-124. > http://www.stata.com/support/faqs/stat/power.html * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**References**:**st: simulation to derive power for GLM multiple regression***From:*Jake <daedalus702@yahoo.com>

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