In R, -- lm fits ordinary least squares linear models; this is likely to be what you want if you were using PROC GLM. -- glm fits generalized linear models (e.g. logit, Poisson, Gaussian, etc.). -- lmer and lme fit mixed models, similar to PROC MIXED Cheers, Hank On Jan 5, 2007, at 10:05 AM, cressonim wrote:
> I am sorry to ask a trivial question but I am not a statistician. > When I need to compare more than two groups in a unbalanced design > with > SAS system I use PROC GLM (like the example in data5.csv from Cody R. > "Applied statistics and SAS programming language" p.223). R glm gives > different results. > > Thanks > > Massimo Cressoni > > ______________________________________________ > [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 > and provide commented, minimal, self-contained, reproducible code. Dr. Martin Henry H. Stevens, Assistant Professor 338 Pearson Hall Botany Department Miami University Oxford, OH 45056 Office: (513) 529-4206 Lab: (513) 529-4262 FAX: (513) 529-4243 http://www.cas.muohio.edu/~stevenmh/ http://www.muohio.edu/ecology/ http://www.muohio.edu/botany/ "E Pluribus Unum" If you send an attachment, please try to send it in a format anyone can read, such as PDF, text, Open Document Format, HTML, or RTF. Please try not to send me MS Word or PowerPoint attachments- Why? See: http://www.gnu.org/philosophy/no-word-attachments.html ______________________________________________ [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 and provide commented, minimal, self-contained, reproducible code.
