Mark Herzog <[EMAIL PROTECTED]> writes: > I was a little hesitant to post to everyone until I figured out why > there is a discrepancy in the intercept estimates when compared to the > same model run in SAS vs. R. Everything else comes out correctly, > including the other coefficient estimates... so perhaps it is just the > numerical method used. I think glm in R is using IWLS, and SAS is using ML. (ML is not a numerical method, just the goal of it. IWLS maximizes the likelihood insofar as it converges.)
> If anyone has another idea feel free to let me know. Watch out for the parametrization: In SAS the intercept (in *this* context!, it is different in other procs...) corresponds to parastat==1 and patsize==small, and I wager that at least the former is vice-versa in R, quite possibly both. > # Standard Wald 95% Chi- > #Parameter DF Estimate Error Limits Square Pr > ChiSq > ##Intercept 1 2.6973 0.2769 2.1546 3.2399 94.92 <.0001 > #parastat0 1 -1.0350 0.5201 -2.0544 -0.0155 3.96 0.0466 > #parastat1 0 0.0000 0.0000 0.0000 0.0000 . . > #patsizelarge 1 1.0844 0.5094 0.0861 2.0827 4.53 0.0333 > #patsizesmall 0 0.0000 0.0000 0.0000 0.0000 . . > #Scale 0 1.0000 0.0000 1.0000 1.0000 > # > #NOTE: The scale parameter was held fixed. -- O__ ---- Peter Dalgaard Øster Farimagsgade 5, Entr.B c/ /'_ --- Dept. of Biostatistics PO Box 2099, 1014 Cph. K (*) \(*) -- University of Copenhagen Denmark Ph: (+45) 35327918 ~~~~~~~~~~ - ([EMAIL PROTECTED]) FAX: (+45) 35327907 ______________________________________________ [email protected] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
