[R] R memory and CPU requirements

[John Fox]

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 <deepayan at stat.wisc.edu> 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: jfox at mcmaster.ca
phone: 905-525-9140x23604
web: www.socsci.mcmaster.ca/jfox