[R] R memory and CPU requirements

[Alexander Sirotkin [at Yahoo]]

--- Deepayan Sarkar <deepayan at stat.wisc.edu> wrote:
> On Thursday 16 October 2003 19:03, 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.
> 
> That's one way to look at it. You don't have enough
> data for the model you are 
> trying to fit. The usual approach under these
> circumstances is to try 
> 'simpler' models.
> 
> Please try to understand what you are trying to do
> (in this case by reading an 
> introductory linear model text) before blindly
> applying a methodology.
> 
> Deepayan
> 
> 


I did study ANOVA and I do have enough observations.
200 was only a random sample of more then 5000 which I
think should be enough. However, I'm afraid to even
think about amount of RAM I will need with R to fit a
model for this data.