--- Deepayan Sarkar <[EMAIL PROTECTED]> 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. ______________________________________________ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help
