Hello
I have a matrix aaa like this:
aa1 aa2 aa3
[1,] 8371.417 27613.57 1170.466
[2,] 14317.999 42421.82 3423.934
[3,] 22026.466 59874.14 8103.084
[4,] 33884.986 84506.34 19176.764
[5,] 57954.968 129824.30 56097.310
where each column represents an empirical distribution of random variates
(normally the number of rows would be, say, 10,000 or 100,000) and a matrix bbb
of percentiles like this:
bbb
[,1] [,2] [,3]
[1,] 0.70092980 0.8144194 0.6200732
[2,] 0.77968803 0.5804948 0.5463661
[3,] 0.01509415 0.9313509 0.8611973
[4,] 0.22654757 0.6183386 0.4962867
[5,] 0.36548835 0.6608696 0.3062784
What I'd like to do is to apply the quantiles in the three columns of bbb to
the columns of aaa independently, so as to obtain a matrix ccc such that, for
example, ccc[3,1]=quantile(aaa[,1],bbb[3,1]). THe complete matrix is:
ccc = quantile(aaa[,1],bbb[1,1]), quantile(aaa[,2],bbb[1,2]),
quantile(aaa[,3],bbb[1,3])
quantile(aaa[,1],bbb[2,1]), quantile(aaa[,2],bbb[2,2]),
quantile(aaa[,3],bbb[2,3])
quantile(aaa[,1],bbb[3,1]), quantile(aaa[,2],bbb[3,2]),
quantile(aaa[,3],bbb[3,3])
quantile(aaa[,1],bbb[4,1]), quantile(aaa[,2],bbb[4,2]),
quantile(aaa[,3],bbb[4,3])
quantile(aaa[,1],bbb[5,1]), quantile(aaa[,2],bbb[5,2]),
quantile(aaa[,3],bbb[5,3])
Now if I only two vectors, it would be enough for me to define
ccc=quantile(aaa,bbb). However, if I do this when aaa and bbb are matrix,
quantile(aaa,bbb) does something else -- it gives the percentiles bbb of the
whole set of matrix elements rather than considering separately the quantiles
of the different columns.
I suspect I'll need to use "apply" in some form but have been able to come up
with the correct form.
As you might have imagined, this arises in the context of simulating random
variables from different distribution in empirical form that are correlated
through a copula.
Thanks in advance for your help!
Pietro
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