On 5/12/2010 3:34 PM, Jack Siegrist 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
You don't mention replicate(), which I would consider. For example: replicate(10, t.test(rnorm(20))$p.value) [1] 0.2622419 0.1538739 0.9759340 [4] 0.7413474 0.1541895 0.4321595 [7] 0.5800549 0.7329299 0.9625038 [10] 0.1315875 If you write a function that does the data generating, model fitting, and p-value extraction, then replicate can run that function many times. I don't know how the timing compares, but I like the simplicity and readability of replicate(). hope this helps, Chuck > 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 -- Chuck Cleland, Ph.D. NDRI, Inc. (www.ndri.org) 71 West 23rd Street, 8th floor New York, NY 10010 tel: (212) 845-4495 (Tu, Th) tel: (732) 512-0171 (M, W, F) fax: (917) 438-0894 ______________________________________________ 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.
