If you are interested in exploring the "homogeneity of variance" assumption, I would suggest you model the variance explicitly. Doing so allows you to compare the homogeneous variance model to the heterogeneous variance model within a nested model framework. In that framework, you'll have likelihood ratio tests, etc.
This is why I suggested the nlme package and the gls function. The gls function allows you to model the variance. -tgs P.S. WLS is a type of GLS. P.P.S It isn't clear to me how a variance stabilizing transformation would help in this case. On Tue, Sep 14, 2010 at 6:53 AM, Clifford Long <gnolff...@gmail.com> wrote: > Hi Thomas, > > Thanks for the additional information. > > Just wondering, and hoping to learn ... would any lack of homogeneity of > variance (which is what I believe you mean by different stddev estimates) be > found when performing standard regression diagnostics, such as residual > plots, Levene's test (or equivalent), etc.? If so, then would a WLS routine > or some type of variance stabilizing transformation be useful? > > Again, hoping to learn. I'll check out the gls() routine in the nlme > package, as you mentioned. > > Thanks. > > Cliff > > > On Mon, Sep 13, 2010 at 10:02 PM, Thomas Stewart <tgstew...@gmail.com>wrote: > >> Allow me to add to Michael's and Clifford's responses. >> >> If you fit the same regression model for each group, then you are also >> fitting a standard deviation parameter for each model. The solution >> proposed by Michael and Clifford is a good one, but the solution assumes >> that the standard deviation parameter is the same for all three models. >> >> You may want to consider the degree by which the standard deviation >> estimates differ for the three separate models. If they differ wildly, >> the >> method described by Michael and Clifford may not be the best. Rather, you >> may want to consider gls() in the nlme package to explicitly allow the >> variance parameters to vary. >> >> -tgs >> >> On Mon, Sep 13, 2010 at 4:52 PM, Doug Adams <f...@gmx.com> wrote: >> >> > Hello, >> > >> > We've got a dataset with several variables, one of which we're using >> > to split the data into 3 smaller subsets. (as the variable takes 1 of >> > 3 possible values). >> > >> > There are several more variables too, many of which we're using to fit >> > regression models using lm. So I have 3 models fitted (one for each >> > subset of course), each having slope estimates for the predictor >> > variables. >> > >> > What we want to find out, though, is whether or not the overall slopes >> > for the 3 regression lines are significantly different from each >> > other. Is there a way, in R, to calculate the overall slope of each >> > line, and test whether there's homogeneity of regression slopes? (Am >> > I using that phrase in the right context -- comparing the slopes of >> > more than one regression line rather than the slopes of the predictors >> > within the same fit.) >> > >> > I hope that makes sense. We really wanted to see if the predicted >> > values at the ends of the 3 regression lines are significantly >> > different... But I'm not sure how to do the Johnson-Neyman procedure >> > in R, so I think testing for slope differences will suffice! >> > >> > Thanks to any who may be able to help! >> > >> > Doug Adams >> > >> > ______________________________________________ >> > 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. >> > >> >> [[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. >> > > [[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.
