> Dear all, > > I fit independent GLMs for a 2x2 factorial problem on the data matrix of > size 9500 x 12 (genes x arrays) and get 9500 observed t-values using the > apply() function. Now, I wish to get the permutated p-values. Therefore > I random sample the class labels and perform the glm fitting to get the > t-values from which I can get the p-values. This is done using a for() > loop. Is there a more efficient way to do this. Each loop currently > takes 5 minutes approximately. > Do you use glm.fit()? by calling directly glm.fit() is faster than calling glm(); glm() itself uses glm.fit(); see ?glm.fit and you can extract the t-values from the returned object.
I'm not able to tell something about your next questions, sorry. best, vito > More importantly I need to repeat this at least 1000 times which > requires 3-4 days but the process halts after some time. > > To isolate the problem, I rewrote the script with 10 chunks of 100 > loops. The first 2 chunk runs fine and the results are ok but on the > third (sometimes fourth, fifth or sixth) chunk, I get the following > error message: > > Error in FUN(newX[, i], ...) : subscript out of bounds > Execution halted > > Does R have a "time out" when I use 'R --no-save < script.file' on the > UNIX platform ? > > I have checked with my system administrator and according to him there > is no upper limit to process time. I have explicitly removed every > unneccassary object at the end of each loop to keep the reserve memory. > I have tried the same on Windows and different chunks sizes and > different machines. Sometime it runs fine to completion and when it dies > it does not appear systematic. > > Now I am reduced to writing scripts with chunks of 100 loops and then > collecting the chunks that were successful. I have to repeat the 1000 > loops for many many different experiments and it is getting very > tedious. > > If you have any idea or had similar experience, please let me know. > Thank you. > > > Regards, Adai. > > ______________________________________________ > [EMAIL PROTECTED] mailing list > https://www.stat.math.ethz.ch/mailman/listinfo/r-help ______________________________________________ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help