On Feb 19, 2012, at 11:46 AM, li li wrote:
Actually I still get an error for the case of equal correlation.
No. You need to read the error message for meaning.
Below is the code
m <- 100000
n <- 500
m1 <- 0.5*m
mu <- c(rep(2, m1), rep(0, m-m1))
rho <- 0.5
x.mod1 <- matrix(rnorm(n*m, sd=sqrt(1-rho)), nrow=n, ncol=m)+rnorm(n,
sd=sqrt(rho))+t(replicate(n,mu))
Error: cannot allocate vector of size 381.5 Mb
You don't have enough space in your machine (whatever it might be).
--
David.
R(173,0xa0a7b540) malloc: *** mmap(size=400003072) failed (error
code=12)
*** error: can't allocate region
*** set a breakpoint in malloc_error_break to debug
R(173,0xa0a7b540) malloc: *** mmap(size=400003072) failed (error
code=12)
*** error: can't allocate region
*** set a breakpoint in malloc_error_break to debug
R(173,0xa0a7b540) malloc: *** mmap(size=400003072) failed (error
code=12)
*** error: can't allocate region
*** set a breakpoint in malloc_error_break to debug
ÔÚ 2012Äê2ÔÂ19ÈÕ ÉÏÎç10:53£¬li li <hannah....@gmail.com>дµÀ£º
Petr,
Thanks for the help. That certainly makes sense and solves my
current
problem. But, in general, for arbitrary covariance matrix (instead
of the
special equi-correlation case), it there a way to generate numbers
from
multivariate normal when the dimension is very high?
Thanks.
Hannah
ÔÚ 2012Äê2ÔÂ19ÈÕ ÉÏÎç5:01£¬Petr Savicky <savi...@cs.cas.cz>дµÀ£º
On Sat, Feb 18, 2012 at 06:00:53PM -0500, li li wrote:
Dear all,
I need to generate numbers from multivariate normal with large
dimensions
(5,000,000).
Below is my code and the error I got from R. Sigma in the code is
the
covariance
matrix. Can anyone give some idea on how to take care of this
error.
Thank
you.
Hannah
m <- 5000000
m1 <- 0.5*m
rho <- 0.5
Sigma <- rho* matrix(1, m, m)+diag(1-rho, m)
Error in matrix(1, m, m) : too many elements specified
Hi.
The matrix of dimension m times m does not fit into memory,
since it requires 8*m^2 = 2e+14 bytes = 2e+05 GB.
Generating a multivariate normal with a covariance matrix
with 1 on the diagonal and rho outside of the diagonal may
be done also as follows.
m <- 10 # can be 5000000
rho <- 0.5
# single vector
x <- rnorm(1, sd=sqrt(rho)) + rnorm(m, sd=sqrt(1 - rho))
# several vectors
a <- t(replicate(10000, rnorm(1, sd=sqrt(rho)) + rnorm(m,
sd=sqrt(1 -
rho))))
# check the sample covariance matrix if m is not too large
sigma <- cov(a)
range(diag(sigma)) # elements on the diagonal
[1] 0.9963445 1.0196015
diag(sigma) <- NA
range(sigma, na.rm=TRUE) # elements outside of the diagonal
[1] 0.4935129 0.5162836
Generating several vectors using replicate() may not be efficient.
The following can be used instead.
n <- 10000
a <- matrix(rnorm(n*m, sd=sqrt(1 - rho)), nrow=n, ncol=m) + rnorm(n,
sd=sqrt(rho))
Note that the size of "a" is n times m and it should fit into the
memory.
Hope this helps.
Petr Savicky.
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______________________________________________
R-help@r-project.org mailing list
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PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
David Winsemius, MD
West Hartford, CT
______________________________________________
R-help@r-project.org mailing list
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PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
