Hi, I am about to write functions for multivariate kernel densitiy estimation with mixed categorical and continuous date (accoring to Jeff Racine and Qi Li), and the leave-one-out window esitmation needs a lot of computation. I am now optimizing the code performance and therefore fhe following questions:
As R uses call-by-value for functions, is it computational expensive to pass large matrices in function arguments? (i.e. are they really copied and does this need much computing time?) Is it maybe better to work with locally visible variables and nested functions in the optimized code? I have already used Rprof (and I could speed up the code a lot by the information from Rprof), but it does not tell me about that. Thank you for your hints! Axel ________________________________________ Fraunhofer Institut fuer Arbeitswirtschaft und Organisation (IAO) Dipl. Inf. Axel Benz Nobelstr. 12 D-70569 Stuttgart Germany Tel. +49(0)7119702289 Fax. +49(0)7119702192 mail: mailto:[EMAIL PROTECTED] www: http://www.vis.iao.fhg.de ________________________________________ [[alternative HTML version deleted]] ______________________________________________ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help
