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.
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----------------------------------------------------- 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
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