On 2011-04-17 02:34, Dorien Herremans wrote:
Thanks for your remarks. I've been reading about R for the last two days,
but I don't really get when I should use lm or aov.
I don't think that reading about R is the answer at this stage.
It appears to me that you need to learn more about regression.
There are many good introductory books. If you want to learn
the R way at the same time, you could look at the books section
on CRAN. Perhaps Peter Dalgaard's Intro to Stats with R or
An R Companion to Applied Regression by J. Fox and S. Weisberg,
or the books by Verzani or Heiberger/Holland.
After that, you'll find that the R documentation is actually
quite good. Most complaints about R's documentation seem to
amount to complaints that it doesn't teach statistics. That's
a good thing.
About your data: I'm fairly sure that several, if not most, of
your predictors should be factors.
Peter Ehlers
I have attached the dataset, feel free to take a look at it.
So far, running it with alle the combinations did not take too long and
there seem to be some effects between the parameters. However, 2x2
combinations might suffice.
Thanks for any help, or a pointer to some good documentation,
Dorien
On 16 April 2011 10:13, Dieter Menne<dieter.me...@menne-biomed.de> wrote:
dorien wrote:
fit<- lm((tos~nh1*nh2*nh3*randsize*aweights*tt1*tt2*tt3*iters*length,
data=expdata))
Error: unexpected ',' in "fit<-
lm((tos~nh1*nh2*nh3*randsize*aweights*tt1*tt2*tt3*iters*length,"
Peter's point is the important one: too many interactions, and even with +
instead of * you might be running into problems.
But anyway: if you don't let us access
/home/dorien/UA/meta-music/optimuse/optimuse1-build-desktop/results/results_processedCP
you cannot expect a better answer which will depend on the structure of the
data set.
Dieter
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