I'm very much a novice with R, and not a statistician. But as my group has been working with generalized additive models (semi-parametric regression), we have followed Wood's advice about using the R anova function to do model comparison for different regressions. I would imagine at least some of yours might be nested, e.g., adding polynomial functions to some? If so, model comparisons like this might be justified.
Will Shadish On 7/29/2012 10:38 PM, Luis Fernando García Hernández wrote: > Dear R users, > > I have seven regression lines I´d like to compare, in order to find out if > these are significatively different. The main problem is that these are > curves, non normal, non homogeneous data, I´ve tried to linearize them but > it has not worked. So I´d like to know if you know any command or source in > R which explains how to perform this kind of comparison. > > Thanks in advance for your help! > > [[alternative HTML version deleted]] > > > > ______________________________________________ > R-help@r-project.org 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. -- William R. Shadish Distinguished Professor Founding Faculty Chair, Psychological Sciences Mailing Address: William R. Shadish University of California School of Social Sciences, Humanities and Arts 5200 North Lake Rd Merced CA 95343 Physical/Delivery Address: University of California Merced ATTN: William Shadish School of Social Sciences, Humanities and Arts Facilities Services Building A 5200 North Lake Rd. Merced, CA 95343 209-228-4372 voice 209-228-4390 fax wshad...@ucmerced.edu http://faculty.ucmerced.edu/wshadish/index.htm http://psychology.ucmerced.edu [[alternative HTML version deleted]]
______________________________________________ R-help@r-project.org 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.
