An embedded and charset-unspecified text was scrubbed... Name: not available URL: <https://stat.ethz.ch/pipermail/r-sig-mixed-models/attachments/20130404/c3d889eb/attachment.pl>
no-normal dependent variable
2 messages · Iasonas Lamprianou, Ben Bolker
Iasonas Lamprianou <lamprianou at ...> writes:
Hi all, a very quick question.
In the context of mixed effects linear models, the theory tells us that we should not worry if the distribution of the dependent variable is not normal (e.g. if it is uniform, or skewed) as long as the residuals are normally and randomly distributed (and in a homoscedastic way): the estimates should be consistent, but the standard errors might be inflated. So, if a reviewer worries too much about the standard errors, is there a way to use lmer or lme to compute robust standard errors for my fixed and/or random effects? It seems that neither lme4 nor nlme currently support this, but there might be some other software out there to do the trick.
I may be missing something here: if so, sorry. As I understand it the theory of mixed models doesn't say *anything* about the *marginal* distribution of the response variable (I prefer "predictor"/"response" to "independent"/"dependent"); it *only* refers to the conditional distribution (= distribution of the residuals) (and to the distribution of the random effects, but that's a tangent). But perhaps you are referring to the conditional distribution being non-normal? You might try seeing whether the vcovHC function from the sandwich package can be adapted, although I was unsuccessful at my first attempt ...