Sampling a matrix with different probability distributions

The sample function has a prob argument that can be used to sample with
unequal probabilities.  It sounds like you can just pass in the species
abundance vector to prob and it will do what you want.

It might be that my question is even more basic than it sounds:  I have
tried what you say, but I may just be writing it wrong as I get an error
message.  I wrote:

reduced.M <- matrix(table( factor( sample(rep(M.index,M),800), M.index
prob=pla)),nr=5)

but I get and error message saying that the prob argument is "unused"".  I
have also tried prob=unif, or directly prob=c(10, 9, 6, 5, 3), but I get
the same error message. Any hints as to how I am passing the prob argument
wrong? This is probably too basic...

Thanks, Silvia.
-- 
Gregory (Greg) L. Snow Ph.D.
Statistical Data Center
Intermountain Healthcare
greg.snow at imail.org
801.408.8111

-----Original Message-----
From: r-help-bounces at r-project.org [mailto:r-help-bounces at r-
project.org] On Behalf Of Silvia Lomascolo
Sent: Tuesday, May 05, 2009 9:52 AM
To: r-help at r-project.org
Subject: [R] Sampling a matrix with different probability distributions


I need to sample a matrix according to different distributions, instead
of
just randomly.  Here is some code that will hopefully clarify what I
need:

I have a matrix M of 1287 interactions between species in rows and
species
in columns, according to their abundance:

pla<- c(10, 9, 6, 5, 3) #abundance of pla species
pol<- c(14, 10, 9, 4, 2) #abundance of pol species
M<-pla%*%t(pol) #matrix of 1287 interactions according to pla and pol
abundance
M
     [,1] [,2] [,3] [,4] [,5]
[1,]  140  100   90   40   20
[2,]  126   90   81   36   18
[3,]   84   60   54   24   12
[4,]   70   50   45   20   10
[5,]   42   30   27   12    6

Thanks to help from people in this forum, I was able to randomly sample
800
interactions from matrix M and obtain a subset of the interactions in a
smaller matrix called reduced.M:

M.index <- 1:length(M)
reduced.M <- matrix(table( factor( sample(rep(M.index,M),800),
M.index)),nr=5)
reduced.M

     [,1] [,2] [,3] [,4] [,5]
[1,]   77   62   56   25   15
[2,]   83   53   51   21   11
[3,]   57   34   28   18   10
[4,]   51   31   21   14    4
[5,]   27   21   19    6    5

Now I need to sample again, not randomly, but according to different
distributions.  For example, I need to sample according to the
abundance of
species pla, (pla vector written above).  The result should be that I
sample
my first row more intensely than my second row, and the last row should
be
the least intensely sampled, in proportion to my row species abundance.
In
the same token, I want to sample with a uniform distribution as well.
How
do I do this?

Thanks, as usual! Silvia.
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