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:rkoen...@uiuc.edu] 
Sent: Monday, July 11, 2011 12:48 PM
To: Prew, Paul
Cc: r-help@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    rkoen...@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
> 
> 
> 
> 
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