Thanks a lot Bill & Bert. Hi Bill,
Sorry i was wrong on number of records, actually, i am using two dimensional data of 250K records each. And regarding CPU usage, it was the elapsed time. Infact, i have pined one core to run R. Thanks & Regards Sasi On Mon, Nov 16, 2015 at 2:04 PM, William Dunlap <wdun...@tibco.com> wrote: > You cannot do a linear regression with one column of data - there must > be at least one response column and one predictor. By default, lm > throws in a constant term which gives you a second predictor. If your > predictor is categorical, you get a new column for all but the first > unique value in it. > > lm() deals only with double precision data, at 8 bytes/number. Thus > 250k numbers occupies 2 million bytes. Your three columns (in the > non-categorical-predictor case) take up 6 million bytes, > > lm()'s output contains several columns the size of the response > variable: residuals, effects, and fitted.values. It also contains the > QR decomposition of the design matrix (the size of all the predictor > columns together). > > There are also some temporary variables generated in the course of the > computation. > > So your observed 40 MB memory usage seems reasonable. > > Use the object.size() function to see how big objects are and str() to > look at their structure. > > My laptop with a 2.5 GHz Intel i7 processor takes a quarter second to > fit a simple linear model with one numeric predictor and a constant > term. 6 seconds sounds slow. Is that cpu or elapsed time (use > system.time() to see)? > > > > Bill Dunlap > TIBCO Software > wdunlap tibco.com > > > On Mon, Nov 16, 2015 at 12:25 PM, Sasikumar Kandhasamy > <ckms...@gmail.com> wrote: > > Hi All, > > > > I have couple of clarifications on R run-time performance. I have R-3.2.2 > > package compiled for MIPS64 and am running it on my linux machine with > > mips64 processor (core speed 1.5GHz) and observing the following > behaviors, > > > > 1. Applying "linear regression model" (lm) on 1MB of data (contains 1 > > column of 250K records) takes ~6 seconds to complete. Anyidea, is it an > > expected behavior or not? If not, can you please the suggestions or > options > > to improve if we have any? > > > > 2. Also, the R process runtime virtual memory is increased by 40MB after > > applying the linear model on 1MB data. Is it also expected behavior? If > it > > is expected, can you please share the insight of memory usage? > > > > Thanks in advance. > > > > Regards > > Sasi > > > > [[alternative HTML version deleted]] > > > > ______________________________________________ > > R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see > > 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. > [[alternative HTML version deleted]] ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see 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.
