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 :
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 -
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
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)
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
Hello,
I would be very grateful if someone could give me a hand with my split
plot design problems.
So here is my design :
I am studying the crossed-effects of water (wet/dry) and mowing
(mowed/not-mowed = nm) on plant height (PH) within 2 types of plant
communities (Xerobromion and
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 give
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