Are y and s continuous or is one of them a factor/dummy variable? From the mle specification I grasp that s is the unit of observation and is factor-coded. If that is so, then estimating lm(o~y+s) includes a lot of dummy/factor variables (lots of columns of 0/1 in the X matrix), and then there could be a problem as you describe it. To avoid this problem you may want to use the plm library. This library conducts fixed-effects analyses without including dummy variables to denote the unit of observation. Instead, it demeans the data for each unit of observation and readjusts the standard errors to account for the reduction in the degrees of freedom due to the fixed effects.
HTH, Daniel ------------------------- cuncta stricte discussurus ------------------------- -----Ursprüngliche Nachricht----- Von: r-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org] Im Auftrag von Jörg Schaber Gesendet: Wednesday, September 23, 2009 10:06 AM An: r-h...@stat.math.ethz.ch Betreff: [R] memory problems for fixed effect models Hi, I am trying to fit a simple two-way fixed effect linear model (o ~ y + s - 1). However, my problem is large (length(o)=79333). I am already using slm.fit with a dense design matrix (ddm), but still: fit <- slm.fit(ddm,o) Error: cannot allocate vector of size 9.1 Gb Is there a way to redefine the model or any other trick such that I do not run into these memory problems? Running a mixed effect model, like lme(o ~ y - 1 ,random = ~ 1 | s, method="REML", contrasts=list(s=("contr.sum")),na.action=na.exclude) poses no problem, but I want to have both effects fixed. Thanks, joerg ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.