You should profile (Rprof) your code to see where it is spending it time; this will point you to what needs to be optimized.
On Wed, May 12, 2010 at 3:34 PM, Jack Siegrist <jack...@eden.rutgers.edu>wrote: > > We are doing a power analysis by generating noisy data sets according to a > model, fitting the model to the data, and extracting a p-value. What is the > best way to do this many times? We are just using for loops and it is too > slow because we are repeating the analysis for many parameterizations. I > can > think of several ways to do this: > > for loop > sapply > using the plyr package > using the lme4 package > > Someone told me that the apply functions are barely any faster than for > loops, so what is the best way, in general, to approach this type of > problem > in R-style? > Could someone point to a discussion of the comparative time efficiencies of > these and other appropriate methods? > > I'm not looking for specific code, just sort of a list of the common > approaches with information about efficiency. > > Thanks, > > Jack > -- > View this message in context: > http://r.789695.n4.nabble.com/vectorize-a-power-analysis-tp2196647p2196647.html > Sent from the R help mailing list archive at Nabble.com. > > ______________________________________________ > 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<http://www.r-project.org/posting-guide.html> > and provide commented, minimal, self-contained, reproducible code. > -- Jim Holtman Cincinnati, OH +1 513 646 9390 What is the problem that you are trying to solve? [[alternative HTML version deleted]] ______________________________________________ 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.
