Hi Jean-Paul,
>> However, I've tried both solutions on my model, and I got different
>> residuals :...
>> What could be the difference between the two?
There is no difference. You have made a mistake.
##
tt <- data.frame(read.csv(file="tt.csv", sep="")) ## imports your data set
T.aov <- aov(PH
Thanks Mark and Richard for your propositions on how to extract residuals.
However, I've tried both solutions on my model, and I got different
residuals :
If we consider the within residuals :
Mark's solution gave me a vector of 24 residuals :
as.vector(residuals(aov(PH~Community*Mowing*Wat
Jean-Paul Maalouf wrote:
Do you have any idea on how can I verify preliminary assumptions in
this model (normality of the residuals and variance homogeneity),
since R is not able to extract residuals?
Of course, R extracts residuals. Use the proj() function. See ?proj
for the example
to ge
Hi Jean-Paul,
>> ... since R is not able to extract residuals?
R can extract the residuals, but they are a "hidden" in models with an error
structure
##
str(aov(PH~Community*Mowing*Water + Error(Block)))
residuals(aov(PH~Community*Mowing*Water + Error(Block))$Block)
residuals(aov(PH~Community*M
Thanks a lot for your answer.
My blocks are geographically well-separated, and within each block my
four treatments are randomized. Therefore I am choosing the first model.
Do you have any idea on how can I verify preliminary assumptions in
this model (normality of the residuals and varianc
I don't think you are clear enough about the layout within each block. If
the four treatments are randomized, I would choose the first model.
KW
On Tue, Jul 21, 2009 at 9:38 AM, Jean-Paul Maalouf <
jean-paul.maal...@u-bordeaux1.fr> wrote:
> Hello,
>
> I would be very grateful if someone could
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