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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--
Brian D. Ripley, [EMAIL PROTECTED]
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
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