<>
Good call on the missing values front.
The other thing to potentially be wary of is that missing values in your
rows or cols variables will cause the -sparse()- code to do odd things. A
possible fix is below, but it may be easiest just to ensure there aren't any
missing values in your initial x matrix.
*********************************************************
mata:
real matrix sparse(real matrix x)
{
real matrix y
real scalar k
y = J(colmax(x[,1]),colmax(x[,2]),.)
for (k=1; k<=rows(x); k++) {
if (x[k,1]!=. & x[k,2]!=.) y[x[k,1],x[k,2]] = x[k,3]
}
return(y)
}
end
**********************************************************
Tirthankar Chakravarty <[email protected]> wrote:
<>
<>
Many apologies. Read Glenn for Allan.
T
On Sat, May 9, 2009 at 10:38 PM, Tirthankar Chakravarty
<[email protected]> wrote:
> <>
> <>
> 1) Amadou's solution can be streamlined considerably by replacing the
> first part as in the code included below.
>
> 2) The line:
> tomata x y z, missing
> does not work because -missing- is not a valid option for -tomata- (Gould,
SSC).
>
> 3) Allan's solution is efficient, but you might want to watch out for
> missing values in your original data. So, a slight modification is
> included below.
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