Many thanks Martin. Stata matsize limitation does not let me run your proposal since I have too many observations.
If svmat could be used in the way, that the value of the 1x1 matrix could be written into row `I' of an existing variable instead of creating for each matrix a new variable, then the problem could be overcome. But I do not find a code for doing so, that does not use egen or sum - and both of those are just too time consuming.
Many thanks again for your help!
Sylke
-----Original Message-----
From: [email protected] [mailto:[email protected]] On Behalf Of Martin Weiss
Sent: 19 June 2009 17:55
To: [email protected]
Subject: st: AW: Big set of 1x1 matrices into scalar - how to do fast?
<>
Why no tdo this in one fell swoop?
*************
clear*
/*fake matrices */
matrix input b = /*
*/ (1,8,7,6,8,1,32,4,3,2)
matrix input Xi1 = /*
*/ (3\3\3\3\3\3\3\3\3\3)
matrix input Xi2 = /*
*/ (9\3\1\3\3\2\3\3\3\4)
matrix input Xi3 = /*
*/ (9\3\4\3\10\2\3\3\3\0)
/*construct one big
matrix */
forv i=1/3{
mat Xi=(nullmat(Xi),Xi`i')
}
/*multiply in one fell swoop */
mat c=b*Xi
matrix d=c'
svmat d
list, noo
*************
HTH
Martin
-----Ursprüngliche Nachricht-----
Von: [email protected]
[mailto:[email protected]] Im Auftrag von Schnepf S.V.
Gesendet: Freitag, 19. Juni 2009 18:32
An: [email protected]
Betreff: st: Big set of 1x1 matrices into scalar - how to do fast?
Dear Statalist users,
forvalues i = 1(1)3641 {
mat c`i'= b*Xi`i''
capture drop c
quietly gen c= el(c`i',1,1)
quietly sum c
quietly replace result=r(mean) in `i'
drop c
}
For a large set of individuals (here 3641), I have to multiply two vectors,
with b and Xi`I' being a 1x10 vector. As a result I receive for each
individual a 1x1 matrix, just containing 1 value.
I now would like to produce one single scalar variable, that for each `I'
row contains the number of c`I'.
I have found a way how to do that, given above. But my simple solution is
incredibly slow.
Could you help?
Many thanks
Sylke V. Schnepf
Ps. Many thanks for the help on the program for programming a weighted
regression, very helpful.
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