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

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: RE: fitted values in xtmepoisson and xtpoisson |

Date |
Tue, 11 May 2010 17:42:56 -0400 |

Dan-- The fitted values assume e_i=0 which is rather more consequential in the normal case; look at the means, or try webuse epilepsy, clear xtset subject qui poisson seizures lage i.subject predict f la var f "FE Poisson" qui xtpoisson seizures lage, re normal predict n, nu0 la var n "Normal error" qui xtpoisson seizures lage, re predict g, nu0 la var g "Gamma error" sort lage g x=round(50*exp(lage)) qui xtpoisson seizures i.x, re normal predict yn, nu0 qui xtpoisson seizures i.x, re predict yg, nu0 su n g yn yg seizures g rn=seizures/n su rn, mean g infln=n*r(mean) table x, c(mean yn mean yg mean seizures) table x, c(mean g mean n mean infln) su seizures n g yn yg infln sort x tw lowess seizures x||line n x||line g x||line infln x On Tue, May 11, 2010 at 12:53 PM, Dan MacNulty <macn0007@umn.edu> wrote: > Thanks for spotting my error Garry; I did neglect to specify the -normal- > option. And this highlights my core question: why do the marginal fitted > values differ so much between poisson models where the random effects follow > a normal versus a gamma distribution? Moreover, it seems the former provide > a comparatively poor fit to the observed data. Specifically, the marginal > fitted values from the poisson model with normally distributed random > effects appear to underestimate the observed counts. This is illustrated > with the following code: > > . use http://www.stata-press.com/data/r10/epilepsy > . xtset subject > . xtpoisson seizures lage, re normal > . predict xt_norm, nu0 > . xtpoisson seizures lage, re > . predict xt_gamma, nu0 > . twoway (lowess seizures lage) (line xt_norm lage, sort) (line xt_gamma > lage, sort) > > Garry Anderson wrote: >> >> Dear Dan, >> >> If you use the -,normal- option on the -xtpoisson- model the fitted >> values are the same as -xtmepoisson-. That is, the -xtmepoisson- command >> assumes that the random effects are normally distributed, whereas the >> -xtpoisson- command assumes that they have a gamma distribution. Page >> 286 of the Stata 11 XT manual refers to -xtmepoisson- and says "Because >> this is a simple random-intercept model, you can obtain equivalent >> results by using >> xtpoisson with the re and normal options." >> >> Cheers, Garry * * 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: fitted values in xtmepoisson and xtpoisson***From:*Dan MacNulty <macn0007@umn.edu>

**st: RE: fitted values in xtmepoisson and xtpoisson***From:*Garry Anderson <g.anderson@unimelb.edu.au>

**Re: st: RE: fitted values in xtmepoisson and xtpoisson***From:*Dan MacNulty <macn0007@umn.edu>

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