Hello,
Couple of clarifications:
- A,B,C,D are factors and I am also interested in possible interactions but the
model that comes out from aov R~A*B*C*D violates the model assumptions
- My 2^k is unbalanced i.e. missing data and an additional level I also include
in one of the factors i.e. C
- I was referring in the OP to the 4-way interactions and not 2-way, I'm sorry
for my confusion.
- I tried to create an aov model with less interactions this way but I get the
following error:
model.aov <- aov(log(R)~A+B+I(A*B)+C+D,data=throughput)
Error in `contrasts<-`(`*tmp*`, value = "contr.treatment") :
contrasts can be applied only to factors with 2 or more levels
In addition: Warning message:
In Ops.factor(A, B) : * not meaningful for factors
Here I was trying to say: do a one-way anova except for the A and B factors for
which I would like to get their 2-way interactions ...
Thanks in advance,
Best regards,
Giovanni
On Nov 21, 2011, at 12:04 PM, Giovanni Azua wrote:
> Hello,
>
> I know there is plenty of people in this group who can give me a good answer
> :)
>
> I have a 2^k model where k=4 like this:
> Model 1) R~A*B*C*D
>
> If I use the "*" in R among all elements it means to me to explore all
> interactions and include them in the model i.e. I think this would be the so
> called 2-way anova. However, if I do this, it leads to model violations i.e.
> the homoscedasticity is violated, the normality assumption of the sample
> errors i.e. residuals is violated etc. I tried correcting the issues using
> different standard transformations: log, sqrt, Box-Cox forms etc but none
> really improve the result. In this case even though the model assumptions do
> not hold, some of the interactions are found to significatively influence the
> response variable. But then shall I trust the results of this Model 1) given
> that the assumptions do not hold?
>
> Then I try this other model where I exclude the interactions (is this the
> 1-way anova?):
> Model 2) R~A+B+C+D
>
> In this one the model assumptions hold except the existence of some outliers
> and a slightly heavy tail in the QQ-plot.
>
> Given that the assumptions for Model 1) do not hold, I assume I should ignore
> the results altogether for Model 1) or? or instead can I safely use the Sum
> Sq. of Model 1) to get my table of percent of variations?
>
> This to me was a bit counter-intuitive since I assumed that if there was
> collinearity among factors (and there is e.g. I(A*B*C)) the Model 1) and I
> included those interactions, my model would be more accurate ... ok this
> turned into a brand new topic of model selection but I am mostly interested
> in the question: if model is violated can I or must I not use the results
> e.g. Sum Sqr for that model?
>
> Can anyone advice please?
>
> btw I have bought most books on R and statistical analysis. I have researched
> them all and the ANOVA coverage is very shallow in most of them specially in
> the R-sy ones, they just offer a slightly pimped up version of the R-help.
>
> I am also unofficially following a course on ANOVA from the university I am
> registered in and most examples are too simplistic and either the assumptions
> just hold easily or the assumptions don't hold and nothing happens.
>
> Thanks in advance,
> Best regards,
> Giovanni
>
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