On Dec 1, 2007 10:08 AM, Dieter Menne <[EMAIL PROTECTED]> wrote:
> Douglas Bates stat.wisc.edu> writes:
>
> (lmer)
>
> > The default is PQL, to refine the
> > starting estimates, followed by optimization of the Laplace
> > approximation. In some cases it is an advantage to suppress the PQL
> > it
Douglas Bates stat.wisc.edu> writes:
(lmer)
> The default is PQL, to refine the
> starting estimates, followed by optimization of the Laplace
> approximation. In some cases it is an advantage to suppress the PQL
> iterations which can be done with one of the settings for the control
> argument.
On Dec 1, 2007 9:26 AM, Douglas Bates <[EMAIL PROTECTED]> wrote:
> On Nov 29, 2007 8:09 PM, M-J Milloy <[EMAIL PROTECTED]> wrote:
> >
> > Hello all,
> >
> > I'm attempting to fit a generalized linear mixed-effects model using lmer
> > (R v 2.6.0, lmer 0.99875-9, Mac OS X 10.4.10) using the call:
>
On Nov 29, 2007 8:09 PM, M-J Milloy <[EMAIL PROTECTED]> wrote:
>
> Hello all,
>
> I'm attempting to fit a generalized linear mixed-effects model using lmer
> (R v 2.6.0, lmer 0.99875-9, Mac OS X 10.4.10) using the call:
>
> vidusLMER1 <- lmer(jail ~ visit + gender + house + cokefreq + cracfreq +
>
Hello all,
I'm attempting to fit a generalized linear mixed-effects model using lmer
(R v 2.6.0, lmer 0.99875-9, Mac OS X 10.4.10) using the call:
vidusLMER1 <- lmer(jail ~ visit + gender + house + cokefreq + cracfreq +
herofreq + borcur + comc + (1 | code), data = vidusGD, family = binomial,
co
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