Hello, I am currently analysed two nested models using the same sample. Both the simpler model (Model 1 ~ x1 + x2) and the more complex model (Model 2 ~ x1 + x2 + x3 + x4) yield the same adjusted R-square. Yet the p-value associated with the deviance statistic is highly significant (p=0.0047), suggesting that the confounders (x3 and x4) account for the prediction of the dependent variable. Does anyone have an explanation of this strange paradox? Thank you for any suggestion. Anne
This list is about R programming. Statistics questions, which this is, are generally off topic here. Try posting on a statistics list like stats.stackexchange.com instead. Cheers, Bert Bert Gunter "The trouble with having an open mind is that people keep coming along and sticking things into it." -- Opus (aka Berkeley Breathed in his "Bloom County" comic strip ) On Fri, Oct 5, 2018 at 1:48 AM CHATTON Anne via R-help <r-help at r-project.org> wrote:> Hello, > > I am currently analysed two nested models using the same sample. Both the > simpler model (Model 1 ~ x1 + x2) and the more complex model (Model 2 ~ x1 > + x2 + x3 + x4) yield the same adjusted R-square. Yet the p-value > associated with the deviance statistic is highly significant (p=0.0047), > suggesting that the confounders (x3 and x4) account for the prediction of > the dependent variable. > > Does anyone have an explanation of this strange paradox? > > Thank you for any suggestion. > > Anne > > ______________________________________________ > R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see > 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]]
Yes-- there's no paradox; the adjusted R^2 and deviance are looking at/testing different things. Also you don't say *what* deviance you are looking at, but your interpretation of the deviance is probably wrong. A significant test for anova(model2, model1) says that x3 & x4 add significantly to prediction, over and above x1, x2 On 10/5/2018 4:45 AM, CHATTON Anne via R-help wrote:> Hello, > > I am currently analysed two nested models using the same sample. Both the simpler model (Model 1 ~ x1 + x2) and the more complex model (Model 2 ~ x1 + x2 + x3 + x4) yield the same adjusted R-square. Yet the p-value associated with the deviance statistic is highly significant (p=0.0047), suggesting that the confounders (x3 and x4) account for the prediction of the dependent variable. > > Does anyone have an explanation of this strange paradox? > > Thank you for any suggestion. > > Anne >-- Michael Friendly Email: friendly AT yorku DOT ca Professor, Psychology Dept. & Chair, ASA Statistical Graphics Section York University Voice: 416 736-2100 x66249 Fax: 416 736-5814 4700 Keele Street Web: http://www.datavis.ca Toronto, ONT M3J 1P3 CANADA