the way I interpret the problem (and I may be wrong here, I don't think you
have been particularly clear with your question) is that you are trying to
make a factorial anova where you are trying to explain "R" as a result of
A,B,C and D, and their interaction terms. so using A*B*C*D.
what you should consider is the error family of your data (poisson,
binomial...) and use: model<-glm(R~A*B*C*D) and then simplify your model.
I suggest reading chapter 7 in Crawleys "Statistics: an introduction using
R." and combined with the statistical knowledge you have learnt on one of
your courses you should hopefully find the answer. Perhaps you could also
speak to someone within the course you are registered to and some statistics
focussed forums - it tends to annoy some people on here when they find a
stats question on their R mailing list, obviously they don't have a delete
button...
Good luck.
Rob
-----Original Message-----
From: Giovanni Azua
Sent: Monday, November 21, 2011 4:59 PM
To: r-help@r-project.org
Subject: Re: [R] [OT] 1 vs 2-way anova technical question
Hello Bert,
Thank you for taking the time to try to answer.
1) I know this, however if one is interested in only interaction between two
specific factors then in R one uses I(A*B*C) meaning 3-way anova for that
and not the implicit 2-ways that would otherwise be computed.
2) True, but it fails.
3) No, I don't have any factors with one level, I never said that. It would
not be a 2^k experiment otherwise, my OP states this clearly, this is a 2^k
experimental design ___2___
4) this is only your judgmental attitude that many people unfortunately have
in some of these lists, focussing on ad-hominem judgements or even attacks
to try to prove their superiority without actually answering nor adding any
value to the question at hand. I have taken many graduate courses in
subjects that have all Statistics in the title and passed all of them.
However, as an experienced Software Engineer working for more than 10 years
in the field, I can tell you that there is a huge difference between solving
toy problems to implementing real-life complex projects. Same rules apply
here, one thing is the toy examples one finds in R books and course
exercises and another totally different story is the real life data I am
trying to model. I'm a student in the quantitative part and learning, so I
do have some gaps, I am curious and trying to learn and I think there is no
shame in that. If this makes you upset maybe you should ask to split the
list in two or more: "Advanc!
ed-PhD-black-belt-10th-dan-in-Statistics-and-R level" list and "newbies"
list.
Best regards,
Giovanni
On Nov 21, 2011, at 3:55 PM, Bert Gunter wrote:
Giovanni:
1. Please read ?formula and/or An Introduction to R for how to specify
linear models in R.
2. Correct specification of what you want (if I understand correctly) is
log(R) ~ A*B + C + D
3. ... which presumably will also fail because some of your factors
have only one level, which means that you cannot use them in your
model.
4. ... which, in turn, suggests you don't know what your doing
statistically and should seek local assistance, especially in trying
to interpret a fit to an unbalanced model (you can't do it as you
probably think you can).
I should say in your defense that posts on this list indicate that
point 4 is a widely shared problem among posters here.
Cheers,
Bert
On Mon, Nov 21, 2011 at 5:02 AM, Giovanni Azua <brave...@gmail.com> wrote:
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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--
Bert Gunter
Genentech Nonclinical Biostatistics
Internal Contact Info:
Phone: 467-7374
Website:
http://pharmadevelopment.roche.com/index/pdb/pdb-functional-groups/pdb-biostatistics/pdb-ncb-home.htm
______________________________________________
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and provide commented, minimal, self-contained, reproducible code.
______________________________________________
R-help@r-project.org mailing list
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PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
