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 and provide commented, minimal, self-contained, reproducible code.
