[R] Strange paradox

[CHATTON Anne]

Dear all,
Thank you for your remarks.
The data under analysis were multiply-imputed using Mice.
To compare the nested models, I used the following R codes by van Buuren:
pool.compare (Model2, Model1, method = c("wald"), data = NULL)
As far as I know the Wald statistic tests the null hypothesis that the extra parameters are all zero. But I might be wrong...

-----Message d'origine-----
De : CHATTON Anne 
Envoyé : vendredi, 5 octobre 2018 10:46
À : 'r-help using r-project.org' <r-help using r-project.org>
Objet : Strange paradox

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