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
______________________________________________
R-help@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.
