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From: r-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org]
On
Behalf Of Anita Narwani
Sent: June-03-10 8:49 PM
To: Joris Meys
Cc: r-help@r-project.org
Subject: Re: [R] Nested ANOVA with covariate using Type
Thanks for your response Joris.
I was aware of the potential for aliasing, although I thought that this was
only a problem when you have missing cell means. It was interesting to read
the vehement argument regarding the Type III sums of squares, and although I
knew that there were different
I would just like to add that when I remove the co-variate of Mean.richness
from the model (i.e. eliminating the non-orthogonality), the aliasing
warning is replaced by the following error message:
Error in t(Z) %*% ip : non-conformable arguments
That is when I enter this model:
Could you copy the data?
Data - data.frame(C.Mean,Mean.richness,Zoop,Diversity,Phyto)
dput(Data)
I have the feeling something's wrong there. I believe you have 48
observations (47df + 1 for the intercept), 2 levels of Diversity, 4 of Phyto
and 48/(3*4)=4 levels of Zoop. But you don't have 3df
I see where my confusion comes from. I counted 4 levels of Phyto, but
you have 8, being 4 in every level of Diversity. There's your
aliasing.
table(Diversity,Phyto)
Phyto
Diversity M1 M2 M3 M4 P1 P2 P3 P4
H 0 0 0 0 6 6 6 6
L 6 6 6 6 0 0 0 0
There's no
Hi Anita,
I have to correct myself too, I've been rambling a bit. Off course you don't
delete the variable out of the interaction term when you test the main
effect. What I said earlier didn't really make any sense.
That testing a main effect without removing the interaction term is has a
tricky
Hi Joris,
That seems to have worked and the contrasts look correct.
I have tried comparing the results to what SPSS produces for the same model.
The two programs produce very different results, although the model F
statistics, R squared and adjusted R squared values are identical. The
results are
SPSS uses a different calculation. As far as I understood, they test main
effects without the covariate. Regarding the difference between my and your
results, did you use sum contrasts?
options(contrasts=c(contr.sum,contr.poly))
On Fri, Jun 4, 2010 at 2:19 AM, Anita Narwani
Yes I understood the strangeness of removing a main effect without
interactions that contain it because I did this during my efforts using
model simplification. I had checked out the link you sent a couple of days
ago. It was useful. So does Type II SS remove both the factor and any
interactions
Hello,
I have been trying to get an ANOVA table for a linear model containing a
single nested factor, two fixed factors and a covariate:
carbonmean-lm(C.Mean~ Mean.richness + Diversity + Zoop + Diversity/Phyto +
Zoop*Diversity/Phyto)
where, *Mean.richness* is a covariate*, Zoop* is a
That's what one would expect with type III sum of squares. You have Phyto
twice in your model, but only as a nested factor. To compare the full model
with a model without diversity of zoop, you have either the combination
diversity/phyto, zoop/phyto or phyto twice in the formula. That's aliasing.
that's diversity/phyto, zoop or phyto twice in the formula.
On Thu, Jun 3, 2010 at 3:00 AM, Joris Meys jorism...@gmail.com wrote:
That's what one would expect with type III sum of squares. You have Phyto
twice in your model, but only as a nested factor. To compare the full model
with a model
Hello,
I have been trying to get an ANOVA table for a linear model containing a
single nested factor, two fixed factors and a covariate:
carbonmean-lm(C.Mean~ Mean.richness + Diversity + Zoop + Diversity/Phyto
+ Zoop*Diversity/Phyto)
where, Mean.richness is a covariate, Zoop is a categorical
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