On 22/08/12 16:36, Bert Gunter wrote:
Models with different fixed effects estimated by REML cannot be compared by anova.
I have seen that much in "Modern Applied Statistics in S", and therefore have chosen the model = "ML"
In future, please post questions on mixed effects models on the r-sig-mixed-effects mailing lists. You're likely to receive more informative replies there, too.
Thanks - wasn't aware of this sig - I'll send the reply there as well. Thanks, Rainer
-- Bert On Wed, Aug 22, 2012 at 7:23 AM, Rainer M Krug <r.m.krug at gmail.com> wrote:
Hi
I am comparing four different linear mixed effect models, derived from
updating the original one. To compare these, I want to use anova(). I
therefore do the following (not reproducible - just to illustration
purpose!):
dat <- loadSPECIES(SPECIES)
subs <- expression(dead==FALSE & recTreat==FALSE)
feff <- noBefore~pHarv*year # fixed effect in the model
reff <- ~year|plant # random effect in the model, where year is
the
corr <- corAR1(form=~year|plant) # describing the within-group correlation
structure
#
dat.lme <- lme(
fixed = feff, # fixed effect in the
model
data = dat,
subset = eval(subs),
method = "ML",
random = reff, # random effect in the
model
correlation = corr,
na.action = na.omit
)
dat.lme.r1 <- update(dat.lme, random=~1|plant)
dat.lme.f1 <- update(dat.lme, fixed=noBefore~year)
dat.lme.r1.f1 <- update(dat.lme.r1, fixed=noBefore~year)
The anova is as follow:
anova(dat.lme, dat.lme.r1, dat.lme.f1, dat.lme.r1.f1)
Model df AIC BIC logLik Test L.Ratio p-value dat.lme 1 9 1703.218 1733.719 -842.6089 dat.lme.r1 2 7 1699.218 1722.941 -842.6089 1 vs 2 1.019230e-07 1 dat.lme.f1 3 7 1705.556 1729.279 -845.7779 dat.lme.r1.f1 4 5 1701.556 1718.501 -845.7779 3 vs 4 8.498318e-08 1 I have two questions: 1) I am wondering why the "2 vs 3" does not give the Test values? Is this because the two models are considered as "identical", which would be strange, due to the different logLik values. 2) If I want to compare all models among each other - is there a "best" way? I would be reluctant to do several ANOVA's, due to necessary corrections for multple tests (although this should not be a problem here?) I can obviously select the best model based on the AIC. Thanks in advance, Rainer -- Rainer M. Krug, PhD (Conservation Ecology, SUN), MSc (Conservation Biology, UCT), Dipl. Phys. (Germany) Centre of Excellence for Invasion Biology Stellenbosch University South Africa Tel : +33 - (0)9 53 10 27 44 Cell: +33 - (0)6 85 62 59 98 Fax : +33 - (0)9 58 10 27 44 Fax (D): +49 - (0)3 21 21 25 22 44 email: Rainer at krugs.de Skype: RMkrug
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Rainer M. Krug, PhD (Conservation Ecology, SUN), MSc (Conservation Biology, UCT), Dipl. Phys. (Germany) Centre of Excellence for Invasion Biology Stellenbosch University South Africa Tel : +33 - (0)9 53 10 27 44 Cell: +33 - (0)6 85 62 59 98 Fax : +33 - (0)9 58 10 27 44 Fax (D): +49 - (0)3 21 21 25 22 44 email: Rainer at krugs.de Skype: RMkrug