Please read the help for anova.lme, and note the 'type' argument. You
are
comparing apples and oranges here (exactly as if you did this for a linear
model fit).
Because you have a three-way interaction in your model, looking at the
(marginal) t-tests for any other coefficient than the third-order
interaction violates the marginality principle. And the third-order
interaction seems to be important.
On Thu, 21 Aug 2008, Christoph Scherber wrote:
> Dear all,
>
> When analyzing data from a climate change experiment using linear
> mixed-effects models, I recently
> came across a situation where:
>
> - the summary(model) showed a significant difference between the levels of
a
> two-level factor,
> - while the anova(model) showed no significance for that factor (see
below).
>
> My question now is: Is the anova.lme() approach correct for that model? And
> why does the F-test for CO2 yield a non-significant P-value, while the
t-test
> in the summary.lme() is significant?
CO2 on its own explains little, but allowing different CO2 effects within
the levels of DROUGHT seems important.
A good book on fitiing linear models (e.g. MASS chapter 6) will explain
this to you.
> Many thanks for your help!
>
> Best wishes
> Christoph
>
> ######################################################
>
> mod11=lme(log(ind1+1) ~ CO2*DROUGHT*TEMP,
random=~1|B/C,na.action=na.exclude)
>
> summary(mod11)
> Linear mixed-effects model fit by REML
> Data: NULL
> AIC BIC logLik
> 97.3077 115.6069 -37.65385
>
> Random effects:
> Formula: ~1 | B
> (Intercept)
> StdDev: 1.303146e-05
>
> Formula: ~1 | C %in% B
> (Intercept) Residual
> StdDev: 0.2466839 0.4846578
>
> Fixed effects: log(ind1 + 1) ~ CO2 * DROUGHT * TEMP
> Value Std.Error DF t-value p-value
> (Intercept) 1.9981490 0.2220158 29 9.000030 0.0000
> CO2 -1.0308687 0.3139778 5 -3.283254 0.0219
> DROUGHT -0.9715216 0.2798173 29 -3.471986 0.0016
> TEMP -0.5592615 0.2954130 29 -1.893151 0.0684
> CO2:DROUGHT 1.2196261 0.3957214 29 3.082032 0.0045
> CO2:TEMP 0.9791044 0.4068987 29 2.406261 0.0227
> DROUGHT:TEMP 0.6413038 0.4068987 29 1.576077 0.1259
> CO2:DROUGHT:TEMP -1.1448624 0.5675932 29 -2.017047 0.0530
> Correlation:
> (Intr) CO2 DROUGHT TEMP CO2:DROUGHT CO2:TE DROUGHT:
> CO2 -0.707
> DROUGHT -0.630 0.446 TEMP
> -0.597 0.422 0.474 CO2:DROUGHT 0.446 -0.630
-0.707
> -0.335 CO2:TEMP 0.433 -0.613 -0.344
> -0.726 0.486 DROUGHT:TEMP 0.433 -0.306 -0.688
> -0.726 0.486 0.527 CO2:DROUGHT:TEMP -0.311 0.439 0.493
> 0.520 -0.697 -0.717 -0.717
> Standardized Within-Group Residuals:
> Min Q1 Med Q3 Max
> -1.4631313 -0.5715171 -0.2024273 0.4592221 1.9568914
>
> Number of Observations: 47
> Number of Groups:
> B C %in% B
> 6 12
>
> ######################################################
>
> anova(mod11)
> numDF denDF F-value p-value
> (Intercept) 1 29 162.95719 <.0001
> CO2 1 5 1.15108 0.3324
> DROUGHT 1 29 5.53240 0.0257
> TEMP 1 29 0.04519 0.8331
> CO2:DROUGHT 1 29 5.66686 0.0241
> CO2:TEMP 1 29 1.88455 0.1803
> DROUGHT:TEMP 1 29 0.03481 0.8533
> CO2:DROUGHT:TEMP 1 29 4.06848 0.0530
>
>
> ######################################################
>
> ______________________________________________
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> PLEASE do read the posting guide
http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
>
--
Brian D. Ripley, ripley at stats.ox.ac.uk
Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/
University of Oxford, Tel: +44 1865 272861 (self)
1 South Parks Road, +44 1865 272866 (PA)
Oxford OX1 3TG, UK Fax: +44 1865 272595