Hello Tobias,
I am not sure what your wt variable is: I suspect a 'weight'. If it is a
nonnegative measure, then you want a positive density model, not a normal
density in the first place. I think you should try a Gamma GLM, and look at a
Gamma qqplot within each of your conditions. You could try the following:
M1 = glm(wt ~ pes + origin + gender + gender:pes, family=Gamma(link=identity))
M2 = glm(wt ~ pes + origin + gender + gender:pes, family=Gamma(link=log))
M3 = glm(wt ~ pes + origin + gender + gender:pes, family=Gamma(link=inverse))
and see whether one of them fit better, in terms of qqplot adjustment or
comparative fit indicies (AIC, BIC,...).
HTH,
Yvonnick Noel, PhD
University of Brittany
France
Message: 1
Date: Sun, 04 May 2008 11:56:09 +0200
From: Tobias Erik Reiners <[EMAIL PROTECTED]>
Subject: [R] Ancova_non-normality of errors
To: r-help@r-project.org
Message-ID: <[EMAIL PROTECTED]>
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Hello Helpers,
I have some problems with fitting the model for my data...
-->my Literatur says (crawley testbook)=
Non-normality of errors-->I get a banana shape Q-Q plot with opening
of banana downwards
The goal of my analysis is to work out what effect the categorial
factors(origin, gender) on the relation between
log(wt)~log(pes)(-->Condition, fett ressource), have.
Does the source(origin) of translocated animals have an affect on
performance(condition)in the new area?
I have already a best fit model and it looks quite good (or not?see below).
two slopes(gender difference)and 6 intercepts(3origin levels*2gender levels)
lm(formula = log(wt) ~ log(pes) + origin + gender + gender:log(pes))
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