Re: [R] Genmod in SAS vs. glm in R

Whats the R equivalent for Proc logistic in SAS ? Is there a stepwise method there ? How to create scoring models in R , for larger datasets (200 mb), Is there a way to compress and use datasets (like options compress=yes;) Ajay On Wed, Sep 10, 2008 at 11:12 AM, Peter Dalgaard [EMAIL PROTECTED]

Re: [R] Genmod in SAS vs. glm in R

Ajay ohri wrote: Whats the R equivalent for Proc logistic in SAS ? glm with the appropriate family (binomial) and link, I guess. There is a book 'R for SAS and SPSS users' forthcoming http://www.springer.com/statistics/computational/book/978-0-387-09417-5 Is there a stepwise method there

[R] Genmod in SAS vs. glm in R

~ x, family=binomial(link='cloglog'), data=bin_data, offset=log(y)) Results from R Coefficients: (Intercept)x -3.9911.358 'log Lik.' -0.9400073 (df=2) -- View this message in context: http://www.nabble.com/Genmod-in-SAS-vs.-glm-in-R

Re: [R] Genmod in SAS vs. glm in R

For one thing your call to glm() is wrong --- didn't you notice the warning messages about ``non-integer #successes in a binomial glm!''? You need to do either: glm(r/k ~ x, family=binomial(link='cloglog'), data=bin_data, offset=log(y), weights=k) or: glm(cbind(r,k-r) ~ x,

Re: [R] Genmod in SAS vs. glm in R

Rolf Turner wrote: For one thing your call to glm() is wrong --- didn't you notice the warning messages about ``non-integer #successes in a binomial glm!''? You need to do either: glm(r/k ~ x, family=binomial(link='cloglog'), data=bin_data, offset=log(y), weights=k) or: glm(cbind(r,k-r) ~