You example is NOT self contained, which means that any potential
respondent must guess what you mean by "a function with a variable of
almost 200,000". The following clarifies this:
> start0 <- rep(1, 200000)
> msLE2 <- function(x)sum(x^2)
> nlminb(start=start0, msLE2, control = list(x.tol = .001))
Error in vector("double", length) : vector size specified is too large
"traceback()" reveals that this error message was generated in by
'vector("double", length)', where length = 130 + (n * (n + 27))/2), and
n = length(start) = 200,000 in this case. This is 20e9 double precision
numbers or 160 GB. This suggests you need to rethink what you are
trying to do.
In my opinion, in any problem with more than a fairly small number
of unknowns, e.g., 3 or 12 depending on the complexity of the problem,
the vast majority of the unknowns will be better estimated by
considering them as different samples from some abstract population and
trying to estimate first the hyperparameters of that population and then
the individuals conditioned on the hyperparameters. The most general
tools for that kind of thing in R are in the 'nlme' and 'lme4'
packages. To understand those, I highly recommend Pinheiro and Bates
(2000) Mixed-Effects Models in S and S-PLUS (Springer). If your
observations can not reasonably be considered by mixed-effects models
with normal errors, a second reference is Gelman and Hill (2006) Data
Analysis Using Regression and Multilevel/Hierarchical Models (Cambridge
University Press). If neither of those seem adequate to your problem, I
suggest you consider using the "RSiteSearch.function" in the RSiteSearch
package to look for other capabilities in R related to your particular
application.
Hope this helps.
Spencer Graves
David Winsemius wrote:
On May 30, 2009, at 2:19 PM, popo UBC wrote:
Hello everyone!
When I use "nlminb" to minimize a function with a variable of almost
200,000
dimension, I got the following error.
nlminb(start=start0, msLE2, control = list(x.tol = .001))
Error in vector("double", length) : vector size specified is too large
I had the following setting
options(expressions=60000)
options(object.size=10^15)
That would do nothing on my machine, but then you may have a different
(unspecified) OS. You may have unrealistic expectations. 10^15 seems a
bit optimistic to me, even if you were supplying that number in a
manner that R would recognize.
?mem.limits # should give you information specific to your OS.
If you use Windoze, try also:
http://cran.r-project.org/bin/windows/base/rw-FAQ.html#There-seems-to-be-a-limit-on-the-memory-it-uses_0021
http://cran.r-project.org/bin/windows/base/rw-FAQ.html#How-do-I-install-R-for-Windows_003f
I have no idea about what might be wrong. Any suggestion is highly
appreciated!!
And we have no idea what sort of setup you have. You could, of
course, read the specifics for your OS in the Installation Guide:
cran.r-project.org/doc/manuals/R-admin.pdf
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