Avoiding another loop

Sorry, I can now see why I needed to provide more information.

I am trying to use simulation to estimate a likelihood function 
that cannot be evaluated explicitly:  a multinomial probit model
with a spatail autocorrelation parameter for each latent dependent
variable.  "myfunction" generates a single latent observation (for
all latent 
dependent variables less one) for a parameter guess.  Clearly, I
need
to do this many times to get an accurate estimate of the likelihood.

Is a loop the best way to do this?

Thanks much,



Michael J. Roberts
Resource Economics Division, PMT
USDA-ERS
202-694-5557

Prof Brian D Ripley <ripley at stats.ox.ac.uk> 01/29 1:16 PM >>>

Hello,

Is there a fancy way I can use a function (like apply)
to avoid a the follwoing loop, or otherwise make this more
efficient?  It fills a 3-d array, one matrix at a time.
"nsize" is big, and efficiency is important.

for (i in 1:nsize) simy[,,i] <- myfunction(p1,p2, ...)

You will need to tell us a lot more. At present it appears that you
are
doing the same calculation nsize times, which is of course easy to
avoid.

Avoiding loops in R is by no mean always useful or effective.  There
are
myths about that go back to long-forgotten versions of S, in which
for
loops were  very much to be avoided.   Up to a point *vectorization*
is
worthwhile, the point being where handling large vectors becones
expensive.

Brian D. Ripley,                  ripley at stats.ox.ac.uk 
Professor of Applied Statistics,  http://www.stats.ox.ac.uk/~ripley/

University of Oxford,             Tel:  +44 1865 272861 (self)
1 South Parks Road,                     +44 1865 272860 (secr)
Oxford OX1 3TG, UK                Fax:  +44 1865 272595

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