This has come up before and I'll again ask the question "why would you want robust standard errors in lmer"? Traditional econometric thinking suggests that there is model mispecification if OLS is used and there is a violation to the assumption of independence. So, one may still get the point estimates via OLS but then get robust standard errors. This makes sense.
But, mixed models are designed to account for violations to the iid assumption via correctly specified random effects. So, if your lmer model is correctly specified, the standard errors should yield an accurate estimate of the true sampling variance. > -----Original Message----- > From: [EMAIL PROTECTED] > [mailto:[EMAIL PROTECTED] On Behalf Of Sundar Dorai-Raj > Sent: Wednesday, September 19, 2007 11:42 AM > To: Abdus Sattar > Cc: [EMAIL PROTECTED]; [EMAIL PROTECTED] > Subject: Re: [R] Robust or Sandwich estimates in lmer2 > > Abdus Sattar said the following on 9/19/2007 7:03 AM: > > Dear R-Users: > > > > I am trying to find the robust (or sandwich) estimates of > the standard error of fixed effects parameter estimates using > the package "lmer2". In model-1, I used "robust=TRUE" on the > other, in model-2, I used "robust=FALSE". Both models giving > me the same estimates. So my question is, does the robust > option works in lmer2 to get the robust estimates of the > standard error? If anybody could offer me a suggestion I > would greatly appreciate it. Thank you. > > > > Model-1: > > > >> p.mle<-lmer2(ddimer~race+steroid+psi+sofa+apache + > (apache|subject), > >> method="ML", data=final, robust=TRUE, cluster="id", > weights=final$w) > >> beta=fixef(p.mle) > >> Vcov=vcov(p.mle, useScale=FALSE) > >> se=sqrt(diag(Vcov)) > >> beta > > (Intercept) race steroid psi > sofa apache > > 5.826489820 -0.001920670 -0.242040171 0.005293996 0.075468340 > > 0.009245152 > >> se > > [1] 0.108325229 0.058921371 0.055975547 0.001285687 0.018119089 > > 0.002559902 > > > > Model-2: > > > >> p.mle<-lmer2(ddimer~race+steroid+psi+sofa+apache + > (apache|subject), > >> method="ML", data=final, robust=FALSE, cluster="id", > weights=final$w) > >> beta=fixef(p.mle) > >> Vcov=vcov(p.mle, useScale=FALSE) > >> se=sqrt(diag(Vcov)) > >> beta > > (Intercept) race steroid psi > sofa apache > > 5.826489820 -0.001920670 -0.242040171 0.005293996 0.075468340 > > 0.009245152 > >> se > > [1] 0.108325229 0.058921371 0.055975547 0.001285687 0.018119089 > > 0.002559902 > > > > > > Best Regards, > > > > Sattar > > > > > > The help page to ?lmer2 in the lme4 package makes no mention > of "cluster" or "robust" arguments. To me, that would mean > these arguments are ignored. > > HTH, > > --sundar > > ______________________________________________ > R-help@r-project.org mailing list > 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. > ______________________________________________ R-help@r-project.org mailing list 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.
