I agree completely. In fact, I have about 5000 observations, which should be enough. I was using 200 samples because of RAM limitations and I'm afraid to think about what amount of RAM I'll need to fit an aov() for such data.
--- John Fox <[EMAIL PROTECTED]> wrote: > Dear Alexander, > > If I understand you correctly, you have a sample of > 200 observations. Even > if you had only two factors with 40 levels each, the > main effects and > interactions of these factors would require about > 1600 degrees of freedom > -- that is, more than the number of observations. > This doesn't make a whole > lot of sense. > > I hope that this helps, > John > > At 05:03 PM 10/16/2003 -0700, Alexander Sirotkin > \[at Yahoo\] wrote: > > >--- Deepayan Sarkar <[EMAIL PROTECTED]> wrote: > > > On Thursday 16 October 2003 17:59, Alexander > > > Sirotkin \[at Yahoo\] wrote: > > > > Thanks for all the help on my previous > questions. > > > > > > > > One more (hopefully last one) : I've been very > > > > surprised when I tried to fit a model (using > > > aov()) > > > > for a sample of size 200 and 10 variables and > > > their > > > > interactions. > > > > > > That doesn't really say much. How many of these > > > variables are factors ? How > > > many levels do they have ? And what is the order > of > > > the interaction ? (Note > > > that for 10 numeric variables, if you allow all > > > interactions, then there will > > > be a 100 terms in your model. This increases for > > > factors.) > > > > > > In other words, how big is your model matrix ? > (See > > > ?model.matrix) > > > > > > Deepayan > > > > > > > > >I see... > > > >Unfortunately, model.matrix() ran out of memory :) > >I have 10 variables, 6 of which are factor, 2 of > which > > > >have quite a lot of levels (about 40). And I would > >like > >to allow all interactions. > > > >I understand your point about categorical > variables, > >but > >still - this does not seem like too much data to > me. > > > > > >I remmeber fitting all kinds of models (mostly > >decision > >trees) for much, much larger data sets. > > > >______________________________________________ > >[EMAIL PROTECTED] mailing list > >https://www.stat.math.ethz.ch/mailman/listinfo/r-help > > ----------------------------------------------------- > John Fox > Department of Sociology > McMaster University > Hamilton, Ontario, Canada L8S 4M4 > email: [EMAIL PROTECTED] > phone: 905-525-9140x23604 > web: www.socsci.mcmaster.ca/jfox > ----------------------------------------------------- > ______________________________________________ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help
