Hello, I am a rather unexperienced r-user (learned the language 1 month ago) and run into the following problem using a local computer with 6 cores & 24 GB RAM and R 2.15 64-bit. I didn't install any additional packages
1. Via the read.table command I load a data table (with different data types) which is about 730 MB large 2. I add 2 calculated columns 3. I split the dataset by 5 criteria 4. I run the lm command on the split with the calculated columns as the variables The RAM consumption goes rapidly up and stays at 24 GB for a couple of minutes. The result: Error: cannot allocate vector size of 5.0 Mb In addition: There ware 50 or more warnings (use warnings() to see the first 50) --> Reached total allocation of 24559Mb My code works perfectly fine for a smaller dataset. I am surprised about the errors as the CPU should do all the work with the lm calculations and the output cannot be that large, can it??? (I cannot check the object size of the lm object due to the error) Right now I am running only 1 linear model, but actually I wanted to run 6! Is Windows putting some restrictions on R regarding the RAM usage? Can I change any settings? A RAM upgrade is not an option. Do I need to use a different R package instead (bigmemory?)? Thanks in advance for your help!! -- View this message in context: http://r.789695.n4.nabble.com/lm-Regression-takes-24-GB-RAM-Error-message-tp4660434.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.
