[R] RNORM matrix based on CSV file values for MEAN and SD

[dcoakley]

This should (hopefully) be a pretty simple task. What I'd like to do is read
in a csv file containing means and standard deviations for a large number of
'n' parameters (up to 2000). The list would be in the following format (see
attached read.csv):

Paramter(1), mean, standard dev.,
Paramter(2), mean, standard dev.,
Paramter(3), mean, standard dev.,
...
Paramter(n), mean, standard dev.,


Based on the above csv file, I would then like to generate a large sample
matrix for 's' samples, using the rnorm function. The matrix will be in the
following format:

1,0,0, P1(1), P2(1), P3(1), ... Pn(1)
2,0,0, P1(2), P2(2), P3(2), ... Pn(2)
....
s,0,0, P1(s), P2(s), P3(s), ... Pn(s)

The first column contains the Row number. Taking s=30000, we would have rows
numbered 1 to 30,000.

The second and third column are fixed values - 0

The forth and subsequent columns contain values from the rnorm distribution
for each parameter. P1(1) is the first value generated for the first
parameter, P1(2) is the second value generated and so forth. P2(1) is the
first value generated for the second parameter, P2(2) is the second value
generated and so forth.  Pn(1) is the first value generated for the n'th
parameter, Pn(2) is the second value generated and so forth.

Again the number of rows depends on 's', the number of samples.

Therefore, I will be generating a fairly large matrix. This could be a
1,000,000 x 2,000 matrix. However, due to memory constraints, it may be
necessary to break this down into smaller sub-matrices where I limit the
number of rows. Firstly, is this possible in r, and secondly, can anyone
help suggest a method for creating such a matrix.

I'd really appreciate any help on this. Thank you.



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