R^2 has nothing to do with helping with collinearity. You might also entertain a true geospatial comprehensive model instead of doing 250 model fits. There's probably a lot of background reading you need to do before launching analyses. I assume you've had at least 4 stat courses, also.
Frank Lucas wrote > > Dear R community. > > I´m working with a generalized linear model which the response variable is > a categorical one and the predictive variables are weather conditions. I > have 250 different places where I need to fit the model. In some of these > places I have strong correlations between some of the variables so I need > to deal with this problem. > > I found a work similar than mine where they use tha Nagelkerke R2 > coefficient of determination to deal with multicollinearity. Has anyone > ever used this coefficient of determination in R? I need to know how to > use > it and interpreted in my work. > > If anyone can help me I´d really appreciate it. > > Greetings > > Lucas. > > [[alternative HTML version deleted]] > > > ______________________________________________ > R-help@ 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. > ----- Frank Harrell Department of Biostatistics, Vanderbilt University -- View this message in context: http://r.789695.n4.nabble.com/Nagelkerke-R2-tp4446275p4446754.html Sent from the R help mailing list archive at Nabble.com. ______________________________________________ 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.
