[R-sig-ME] lmer vs lmer2
dave fournier
otter at otter-rsch.com
Fri Oct 5 15:48:38 CEST 2007
Thanks for that Doug, and I apologize for my bad eyesight.
I really can't see the screen in my old age!
It was unfortunate that when I removed the wrong
observations from the data the LL turned out to be
almost identical to the one from the SAS analysis.
Doing it properly, when I remove the observations for group 197 from
the analysis I obtain the estimates
real_b -1.9486e+00 9.5787e-02
real_b 1.6408e+00 3.3554e-02
real_b 1.9368e-02 1.3501e-03
real_b 1.4427e-01 1.1077e-01
real_b -1.4614e-02 7.4902e-03
which are identical to lmer2
for all practical purposes.
(Intercept) -1.948119 0.095877 -20.32
> Height 1.640650 0.032800 50.02
> Age 0.019379 0.001310 14.79
> InitHeight 0.143977 0.111043 1.30
> InitAge -0.014618 0.007501 -1.95
However what I was interested in was the application
of slightly robust methods in NLMM (Once you go robust
they are nonlinear even if the originalmodel is linear.)
So I fit the entire data set using a
conservative robust likelihood,
a 95% 05% mixture of two normal with the 05% one
having 3 times the std dev. of the 95% one The estimates I obtained
are
real_b -1.9730e+000 9.7074e-002
real_b 1.6160e+000 2.7502e-002
real_b 1.9959e-002 1.2192e-003
real_b 2.1801e-001 1.1086e-001
real_b -1.9375e-002 7.5518e-003
compared to the non robust fit to all the data of
real_b -2.0353e+000 1.0380e-001
real_b 1.6438e+000 3.4430e-002
real_b 1.9337e-002 1.3595e-003
real_b 2.5070e-001 1.1966e-001
real_b -2.1486e-002 8.1618e-003
which is not bad when one does not have to physically remove the
"bad" data. So what I really wanted to argue is that one should
routinely use conservative robust methods when fitting RE models and in
passing point out that ADMB-Re privdes a good platform for doing this.
Cheers,
Dave
--
David A. Fournier
P.O. Box 2040,
Sidney, B.C. V8l 3S3
Canada
Phone/FAX 250-655-3364
http://otter-rsch.com
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