A problem about "nlminb"

Sat, May 30, 2009 1:53 PM
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:
A problem about "nlminb"

Thread (4 messages)
