Dear Dave, there are some inconsistencies in your explanation of the
problem. You said your variables are:
CO is a continuous response variable,
Week is a fixed categorical factor,
Habitat is a fixed categorical factor, and
Location is a random categorical factor nested within Habitat.
What does this last statement mean? Are the Locations identified with
the same names in both Habitats (e.g. Location=1,2,3,... for
Habitat=Control, and then Location=1,2,3... for Habitat=Treatment,
although the Locations of both Habitats have nothing to do with each
other? Or do all 13 Locations receive different names?
If Location is really nested within Habitat, you would be in the former
case, and then your random terms should include the interaction
"Location:Habitat". In the latter case, the random term would just be
"Location".
But then, your model with aov is:
mCO = aov(CO ~ Week * Habitat + Error(Location/Week))
Since you don't include Habitat in the Error term, I would say that
Location is not really nested within Habitat. But then, why is Week
nested within Location? Do you mean that the effect of Week may be
affected by the random Location?
Anyway, your interpretation of the ANOVA table is misleading:
Error: Location
Df Sum Sq Mean Sq F value Pr(>F)
Habitat 1 182566 182566 8.6519 0.01341 *
Residuals 11 232115 21101
Error: Location:Week
Df Sum Sq Mean Sq F value Pr(>F)
Week 10 596431 59643 11.0534 7.5e-13 ***
Week:Habitat 10 196349 19635 3.6389 0.0003251 ***
Residuals 110 593551 5396
This actually means:
For the F test of Habitat, the denominator MS is that for Location
For the F test of Week, the denominator MS is that for the Location x
Week
For the F test of Habitat x Week, the denominator MS is that for
Location x Week
And then, you wrote your attempt with lmer:
m. = lmer(CO ~ Week * Habitat + (1|Habitat/Location))
The random term here (1|Habitat/Location) has nothing to do with the
Error term you used in aov.
If location is really nested within Habitat, perhaps you meant
m. = lmer(CO ~ Week * Habitat + (1|Habitat:Location))
(Habitat/Location means that Habitat has a random effect per se as
well, and I guess you don't mean that!)
Or if Location is not really nested,
m. = lmer(CO ~ Week * Habitat + (1|Location))
or if you really wanted the same model as with aov:
m. = lmer(CO ~ Week * Habitat + (1|Location/Week))
Please clarify your model. Otherwise it would be impossible to make any
comparison.
Helios
El día 29/09/2011 a las 6:30, Dave Robichaud<drobich...@lgl.com>
escribió:
Hi All,
I am frustrated by mixed-effects model! I have searched the web for
hours, and found lots on the nested anova, but nothing useful on my
specific case, which is: a random factor (C) is nested within one of
the
fixed-factors (A), and a second fixed factor (B) is crossed with the
first fixed factor:
C/A
A
B
A x B
My question: I have a functioning model using the aov command (see
below), and I would now would like to recode it, using a more
flexible
command such as lme or lmer. Once I have the equivalent syntax down,
I
would ideally like to re-run my analysis using "family = poisson", as
CO
is actually count data.
I have a dataset including a response variable CO, measured once per
Week (for 11 weeks) at 13 Locations. The 13 Locations are divided
into 2
habitat types (Control and Treatment).
Thus:
CO is a continuous response variable,
Week is a fixed categorical factor,
Habitat is a fixed categorical factor, and
Location is a random categorical factor nested within Habitat.
Here is my model in R:
mCO = aov(CO ~ Week * Habitat + Error(Location/Week))
summary(mCO)
And the output:
Error: Location
Df Sum Sq Mean Sq F value Pr(>F)
Habitat 1 182566 182566 8.6519 0.01341 *
Residuals 11 232115 21101
Error: Location:Week
Df Sum Sq Mean Sq F value Pr(>F)
Week 10 596431 59643 11.0534 7.5e-13 ***
Week:Habitat 10 196349 19635 3.6389 0.0003251 ***
Residuals 110 593551 5396
Given that this is a mixed model, I believe the appropriate error
terms
are as follows:
For the F test of Habitat, the denominator MS is that for
location/habitat;
For the F test of Week, the denominator MS is the residual; and
For the F test of Habitat x Week, the denominator MS is the
residual.
My tinkering with lmer and lme have not produced results similar to
the
above
For example,
m. = lmer(CO ~ Week * Habitat + (1|Habitat/Location))
anova(m.)
produces:
Analysis of Variance Table
Df Sum Sq Mean Sq F value
Week 10 596431 59643 11.0534
Habitat 1 28652 28652 5.3100
Week:Habitat 10 196349 19635 3.6389
Any coding advice would be greatly appreciated!
Thanks for your consideration,
Dave Robichaud
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