A bit kludgey but how about:
dimMat <- matrix(0, 1000, 4)
for(i in 1:1000){
while(sum(dimMat[i, ] <- sample(c(0,1), 4, replace = TRUE, prob = c(.3, .7)))==0)
dimMat[i, ] <- sample(c(0,1), 4, replace = TRUE, prob = c(.3, .7))
}
table(rowSums(dimMat))
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On Wed, 9 Apr 2014, ken knoblauch wrote:
Doran, Harold <HDoran <at> air.org> writes:
I am trying to generate a binary matrix where
row
in the matrix is guaranteed to have at
least one 1.
Ideally, I would like most rowSums to be equal
2 or 3
with some 1s and some 4s. But,
rowSums cannot be equal
to 0.
I can tinker with the vector of probability weights,
but in
so (in the way I am doing it) this
causes for
more rowSums to be equal to 4 than I ideally would
, but this never helps to guarantee a
rowSum will not be
equal to 0. I could post-hoc tinker with any rows
are all
0, but seems like that may be just
inefficient.
Below is sample code, any ideas on how to best
tackle this?
Harold
dimMat <- matrix(0, 1000, 4)
for(i in 1:1000){
dimMat[i, ] <- sample(c(0,1), 4, replace = TRUE, prob = c(.3, .7))
}
table(rowSums(dimMat))
Wht don't you sample from the distribution of row sums
for each row and then distribute that many 1's randomly
among the columns.
Ken
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