[R] Linear models over large datasets

Fri Aug 17 19:53:25 CEST 2007

```The simplest trick is to use the QR decomposition:

The OLS solution (X'X)^{-1}X'y can be easily computed as:
qr.solve(X, y)

Here is an illustration:

> set.seed(123)
> X <- matrix(round(rnorm(100),1),20,5)
> b <- c(1,1,2,2,3)
> y <- X %*% b + rnorm(20)
>
> ans1 <- solve(t(X)%*%X,t(X)%*%y)
> ans2 <- qr.solve(X,y)
> all.equal(ans1,ans2)
[1] TRUE

Ravi.
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Assistant Professor, The Center on Aging and Health

Division of Geriatric Medicine and Gerontology

Johns Hopkins University

Ph: (410) 502-2619

Fax: (410) 614-9625

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-----Original Message-----
From: r-help-bounces at stat.math.ethz.ch
[mailto:r-help-bounces at stat.math.ethz.ch] On Behalf Of dave fournier
Sent: Friday, August 17, 2007 12:43 PM
To: r-help at stat.math.ethz.ch
Subject: [R] Linear models over large datasets

>Its actually only a few lines of code to do this from first principles.
>The coefficients depend only on the cross products X'X and X'y and you
>can build them up easily by extending this example to read files or
>a database holding x and y instead of getting them from the args.
>Here we process incr rows of builtin matrix state.x77 at a time
>building up the two cross productxts, xtx and xty, regressing
>Income (variable 2) on the other variables:

>mylm <- function(x, y, incr = 25) {
>	start <- xtx <- xty <- 0
>	while(start < nrow(x)) {
>	    idx <- seq(start + 1, min(start + incr, nrow(x)))
>	    x1 <- cbind(1, x[idx,])
>	    xtx <- xtx + crossprod(x1)
>	    xty <- xty + crossprod(x1, y[idx])
>	    start <- start + incr
>	}
>	solve(xtx, xty)
>}

>mylm(state.x77[,-2], state.x77[,2])

>On 8/16/07, Alp ATICI <alpatici at gmail.com> wrote:
> I'd like to fit linear models on very large datasets. My data frames
> are about 2000000 rows x 200 columns of doubles and I am using an 64
> "R Data Import/Export" guide. My primary issue is although my data
> represented in ascii form is 4Gb in size (therefore much smaller
> considered in binary), R consumes about 12Gb of virtual memory.
>
> What exactly are my options to improve this? I looked into the biglm
> package but the problem with it is it uses update() function and is
> therefore not transparent (I am using a sophisticated script which is
> hard to modify). I really liked the concept behind the  LM package
> here: http://www.econ.uiuc.edu/~roger/research/rq/RMySQL.html
> But it is no longer available. How could one fit linear models to very
> large datasets without loading the entire set into memory but from a
> file/database (possibly through a connection) using a relatively
> simple modification of standard lm()? Alternatively how could one
> improve the memory usage of R given a large dataset (by changing some
> default parameters of R or even using on-the-fly compression)? I don't
> mind much higher levels of CPU time required.
>
>
> ______________________________________________
> R-help at stat.math.ethz.ch mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
>
If your design matrix X is very well behaved this approach may work for
you. Often just doing solve(X'X,y) will fail for numerical reasons. The
right way to do it is tofactor the matrix X  as

X = A * B

where B is 200x200 in your case and A is  2000000 x 200 with
A'*A = I  (That is A is orthogonal.)

so  X'*X = B' *B  and you use

solve(B'*B,y);

To find A and B you can use modified Gram-Schmidt which is very easy to
program and works well when you wish to store the columns of X on a hard
disk and just read in a bit at a time. Some people claim that modifed
Gram-Schmidt is unstable but it has always worked well for me.
In any event you can check  to ensure that A'*A = I and
X=A*B

Cheers,

Dave

--
David A. Fournier
P.O. Box 2040,
Sidney, B.C. V8l 3S3