[R] Strange paradox
Anne@Ch@tton @end|ng |rom hcuge@ch
Mon Oct 8 09:19:34 CEST 2018
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...
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
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.
More information about the R-help