My advice would be to use the profiler 'Rprof()' --- you may find that the loop is not really the problem. In my experience, there's relatively little difference between 'lapply' and a 'for' loop, although 'lapply' can be faster at times.
-roger On Mon, Jul 6, 2009 at 4:26 AM, Thorn Thaler<thot...@sbox.tugraz.at> wrote: > High everybody, > > currently I'm writinig a package that, for a given family of variance > functions depending on a parameter theta, say, computes the extended quasi > likelihood (eql) function for different values of theta. > > The computation involves a couple of calls of the 'glm' routine. What I'm > doing now is to call 'lapply' for a list of theta values and a function, > that constructs a family object for the particular choice of theta, computes > the glm and uses the results to get the eql. Not surprisingly the function > is not very fast. Depending on the size of the parameter space under > consideration it takes a couple of minutes until the function finishes. > Testing ~1000 Parameters takes about 5 minutes on my machine. > > I know that loops in R are slow more often than not. Thus, I thought using > 'lapply' is a better way. But anyways, it is just another way of a loop. > Besides, it involves some overhead for the function call and hence i'm not > sure wheter using 'lapply' is really the better choice. > > What I like to know is to figure out, where the bottleneck lies. > Vectorization would help, but since I don't think that there is vectorized > 'glm' function, which is able to handle a vector of family objects. I'm not > aware if there is any choice aside from using a loop. > > So my questions: > - how can I figure out where the bottleneck lies? > - is 'lapply' always superior to a loop in terms of execution time? > - are there any 'evil' commands that should be avoided in a loop, for they > slow down the computation? > - are there any good books, tutorials about how to profile R code > efficiently? > > TIA 4 ur help, > > Thorn > > ______________________________________________ > R-devel@r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-devel > -- Roger D. Peng | http://www.biostat.jhsph.edu/~rpeng/ ______________________________________________ R-devel@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-devel
