I am trying to use a linear filter to reduce loops and thereby increase the speed of an existing program. However, while the "filter" function (stats package) should have reduced the looping by about 30-fold, the time to complete the program remained about the same. This surprised me, because I had made an analogous change to a Matlab version of the same program using Matlab's "filter" function, and that change made the program run about 9 times as fast.
In R, is there another function that would be more efficient than "filter" in the stats package? Any advice would be appreciated, as I would hate to see Matlab win this speed battle. Relevant line from R code: evTemp=filter(lambda*tempterm,1-lambda,method="recursive") #lambda=scalar between 0 and 1 (representing learning rate in a reinforcement learning model) #tempterm=vector with from 1 to 150 elements (representing reinforcement value data) Thank you. Anthony Bishara Department of Psychology College of Charleston http://bisharaa.people.cofc.edu/ ______________________________________________ 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.
