Hello, I?m wondering if anyone can offer advice on the out-of-memory error I?m getting. I?m using R2.12.2 on Windows XP, Platform: i386-pc-mingw32/i386 (32-bit). I am using the quantreg package, trying to perform a quantile regression on a dataframe that has 11,254 rows and 5 columns.> object.size(subsetAudit.dat)450832 bytes> str(subsetAudit.dat)'data.frame': 11253 obs. of 5 variables: $ Satisfaction : num 0.64 0.87 0.78 0.75 0.83 0.75 0.74 0.8 0.89 0.78 ... $ Return : num 0.84 0.92 0.91 0.89 0.95 0.81 0.9 0.87 0.95 0.88 ... $ Recommend : num 0.53 0.64 0.58 0.58 0.62 0.6 0.56 0.7 0.64 0.65 ... $ Cust.Clean : num 0.75 0.85 0.72 0.72 0.81 0.79 0.79 0.8 0.78 0.75 ... $ CleanScore.Cycle1: num 96.7 83.3 93.3 86.7 96.7 96.7 90 80 81.7 86.7 ... rq(subsetAudit.dat$Satisfaction ~ subsetAudit.dat$CleanScore.Cycle1, tau = -1) ERROR: cannot allocate vector of size 2.8 Gb I don?t know much about computers ? software, hardware, algorithms ? but does this mean that the quantreg package is creating some massive intermediate vector as it performs the rq function? Quantile regression is something I?m just starting to explore, but believe it involves ordering data prior to the regression, which could be a huge job when using 11,000 records. Does bigmemory have functionality to help me with this? Thank you, Paul Paul Prew ? Statistician 651-795-5942 ? fax 651-204-7504 Ecolab Research Center ? Mail Stop ESC-F4412-A 655 Lone Oak Drive ? Eagan, MN 55121-1560 CONFIDENTIALITY NOTICE: This e-mail communication and any attachments may contain proprietary and privileged information for the use of the designated recipients named above. Any unauthorized review, use, disclosure or distribution is prohibited. If you are not the intended recipient, please contact the sender by reply e-mail and destroy all copies of the original message.
Paul, Yours is NOT a large problem, but it becomes a large problem when you ask for ALL the distinct QR solutions by specifying tau = -1. You probably don't want to see all these solutions, I suspect that only tau = 1:19/20 or so would suffice. Try this, and see how it goes. Roger url: www.econ.uiuc.edu/~roger Roger Koenker email rkoenker at uiuc.edu Department of Economics vox: 217-333-4558 University of Illinois fax: 217-244-6678 Urbana, IL 61801 On Jul 11, 2011, at 12:39 PM, Prew, Paul wrote:> Hello, I?m wondering if anyone can offer advice on the out-of-memory error I?m getting. I?m using R2.12.2 on Windows XP, Platform: i386-pc-mingw32/i386 (32-bit). > > I am using the quantreg package, trying to perform a quantile regression on a dataframe that has 11,254 rows and 5 columns. > >> object.size(subsetAudit.dat) > 450832 bytes > >> str(subsetAudit.dat) > 'data.frame': 11253 obs. of 5 variables: > $ Satisfaction : num 0.64 0.87 0.78 0.75 0.83 0.75 0.74 0.8 0.89 0.78 ... > $ Return : num 0.84 0.92 0.91 0.89 0.95 0.81 0.9 0.87 0.95 0.88 ... > $ Recommend : num 0.53 0.64 0.58 0.58 0.62 0.6 0.56 0.7 0.64 0.65 ... > $ Cust.Clean : num 0.75 0.85 0.72 0.72 0.81 0.79 0.79 0.8 0.78 0.75 ... > $ CleanScore.Cycle1: num 96.7 83.3 93.3 86.7 96.7 96.7 90 80 81.7 86.7 ... > > rq(subsetAudit.dat$Satisfaction ~ subsetAudit.dat$CleanScore.Cycle1, tau = -1) > > ERROR: cannot allocate vector of size 2.8 Gb > > I don?t know much about computers ? software, hardware, algorithms ? but does this mean that the quantreg package is creating some massive intermediate vector as it performs the rq function? Quantile regression is something I?m just starting to explore, but believe it involves ordering data prior to the regression, which could be a huge job when using 11,000 records. Does bigmemory have functionality to help me with this? > > Thank you, > Paul > > > > > > > Paul Prew ? Statistician > 651-795-5942 ? fax 651-204-7504 > Ecolab Research Center ? Mail Stop ESC-F4412-A > 655 Lone Oak Drive ? Eagan, MN 55121-1560 > > > > > CONFIDENTIALITY NOTICE: > This e-mail communication and any attachments may contain proprietary and privileged information for the use of the designated recipients named above. > Any unauthorized review, use, disclosure or distribution is prohibited. > If you are not the intended recipient, please contact the sender by reply e-mail and destroy all copies of the original message. > > ______________________________________________ > R-help at 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.
Thank you, Roger, that was my problem. Specifying tau = 1:19/20 worked fine. Regards, Paul Paul Prew | Statistician 651-795-5942?? |?? fax 651-204-7504 Ecolab Research Center | Mail Stop ESC-F4412-A 655 Lone Oak Drive | Eagan, MN 55121-1560 -----Original Message----- From: Roger Koenker [mailto:rkoenker at uiuc.edu] Sent: Monday, July 11, 2011 12:48 PM To: Prew, Paul Cc: r-help at r-project.org help Subject: Re: [R] quantile regression: out of memory error Paul, Yours is NOT a large problem, but it becomes a large problem when you ask for ALL the distinct QR solutions by specifying tau = -1. You probably don't want to see all these solutions, I suspect that only tau = 1:19/20 or so would suffice. Try this, and see how it goes. Roger url: www.econ.uiuc.edu/~roger Roger Koenker email rkoenker at uiuc.edu Department of Economics vox: 217-333-4558 University of Illinois fax: 217-244-6678 Urbana, IL 61801 On Jul 11, 2011, at 12:39 PM, Prew, Paul wrote:> Hello, I?m wondering if anyone can offer advice on the out-of-memory error I?m getting. I?m using R2.12.2 on Windows XP, Platform: i386-pc-mingw32/i386 (32-bit). > > I am using the quantreg package, trying to perform a quantile regression on a dataframe that has 11,254 rows and 5 columns. > >> object.size(subsetAudit.dat) > 450832 bytes > >> str(subsetAudit.dat) > 'data.frame': 11253 obs. of 5 variables: > $ Satisfaction : num 0.64 0.87 0.78 0.75 0.83 0.75 0.74 0.8 0.89 0.78 ... > $ Return : num 0.84 0.92 0.91 0.89 0.95 0.81 0.9 0.87 0.95 0.88 ... > $ Recommend : num 0.53 0.64 0.58 0.58 0.62 0.6 0.56 0.7 0.64 0.65 ... > $ Cust.Clean : num 0.75 0.85 0.72 0.72 0.81 0.79 0.79 0.8 0.78 0.75 ... > $ CleanScore.Cycle1: num 96.7 83.3 93.3 86.7 96.7 96.7 90 80 81.7 86.7 ... > > rq(subsetAudit.dat$Satisfaction ~ subsetAudit.dat$CleanScore.Cycle1, tau = -1) > > ERROR: cannot allocate vector of size 2.8 Gb > > I don?t know much about computers ? software, hardware, algorithms ? but does this mean that the quantreg package is creating some massive intermediate vector as it performs the rq function? Quantile regression is something I?m just starting to explore, but believe it involves ordering data prior to the regression, which could be a huge job when using 11,000 records. Does bigmemory have functionality to help me with this? > > Thank you, > Paul > > > > > > > Paul Prew ? Statistician > 651-795-5942 ? fax 651-204-7504 > Ecolab Research Center ? Mail Stop ESC-F4412-A > 655 Lone Oak Drive ? Eagan, MN 55121-1560 > > > > > CONFIDENTIALITY NOTICE: > This e-mail communication and any attachments may contain proprietary and privileged information for the use of the designated recipients named above. > Any unauthorized review, use, disclosure or distribution is prohibited. > If you are not the intended recipient, please contact the sender by reply e-mail and destroy all copies of the original message. > > ______________________________________________ > R-help at 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.CONFIDENTIALITY NOTICE: This e-mail communication and any attachments may contain proprietary and privileged information for the use of the designated recipients named above. Any unauthorized review, use, disclosure or distribution is prohibited. If you are not the intended recipient, please contact the sender by reply e-mail and destroy all copies of the original message.
Using tau = -1 is causing rq() to try and estimate all possible quantiles and store the results. With 11253 observations this would be a formidable feat. Try estimating the model with say tau = 1:99/100 to give a more tractable number of estimates. Brian Brian S. Cade, PhD U. S. Geological Survey Fort Collins Science Center 2150 Centre Ave., Bldg. C Fort Collins, CO 80526-8818 email: brian_cade@usgs.gov tel: 970 226-9326 From: "Prew, Paul" <Paul.Prew@ecolab.com> To: "r-help@R-project.org" <r-help@r-project.org> Date: 07/11/2011 11:42 AM Subject: [R] quantile regression: out of memory error Sent by: r-help-bounces@r-project.org Hello, I?m wondering if anyone can offer advice on the out-of-memory error I?m getting. I?m using R2.12.2 on Windows XP, Platform: i386-pc-mingw32/i386 (32-bit). I am using the quantreg package, trying to perform a quantile regression on a dataframe that has 11,254 rows and 5 columns.> object.size(subsetAudit.dat)450832 bytes> str(subsetAudit.dat)'data.frame': 11253 obs. of 5 variables: $ Satisfaction : num 0.64 0.87 0.78 0.75 0.83 0.75 0.74 0.8 0.89 0.78 ... $ Return : num 0.84 0.92 0.91 0.89 0.95 0.81 0.9 0.87 0.95 0.88 ... $ Recommend : num 0.53 0.64 0.58 0.58 0.62 0.6 0.56 0.7 0.64 0.65 ... $ Cust.Clean : num 0.75 0.85 0.72 0.72 0.81 0.79 0.79 0.8 0.78 0.75 ... $ CleanScore.Cycle1: num 96.7 83.3 93.3 86.7 96.7 96.7 90 80 81.7 86.7 ... rq(subsetAudit.dat$Satisfaction ~ subsetAudit.dat$CleanScore.Cycle1, tau = -1) ERROR: cannot allocate vector of size 2.8 Gb I don?t know much about computers ? software, hardware, algorithms ? but does this mean that the quantreg package is creating some massive intermediate vector as it performs the rq function? Quantile regression is something I?m just starting to explore, but believe it involves ordering data prior to the regression, which could be a huge job when using 11,000 records. Does bigmemory have functionality to help me with this? Thank you, Paul Paul Prew ? Statistician 651-795-5942 ? fax 651-204-7504 Ecolab Research Center ? Mail Stop ESC-F4412-A 655 Lone Oak Drive ? Eagan, MN 55121-1560 CONFIDENTIALITY NOTICE: \ This e-mail communication an...{{dropped:20}}