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@r-project.org' <r-help@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
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