That's good insight, and gives me some good ideas for what direction to this. Thanks everyone !
Doug P.S. - I guess if you have a significant interaction, that implies the slopes of the individual regression lines are significantly different anyway, doesn't it... On Tue, Sep 14, 2010 at 11:33 AM, Thomas Stewart <tgstew...@gmail.com> wrote: > 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. >> > > ______________________________________________ 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.
