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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.
/*** Begin ***/
/* Simulate some data */
clear
clear mata
set obs 100
// create a 20x5 matrix
egen cols = seq(), from(1) to(5)
sort cols
egen rows = seq(), from(1) to(20)
gen var3 = runiform()
/* Mata solution - I - not flexible */
sort cols rows /* note sort order */
qui su rows
scalar define sMvrows = r(max)
qui su cols
scalar define sMvcols = r(max)
mata:
vA = st_data(.,"var3")
mB = colshape(vA,st_numscalar("sMvrows"))'
mB
end
/* Mata solution - II - Allan's solution modified */
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++) {
y[x[k,1],x[k,2]] = x[k,3]
}
return(y)
}
mC = st_data(.,("rows", "cols", "var3"))
mD = sparse(mC)
mD
/* Check the two solutions are equivalent */
asserteq(mD, mB)
end
/*** End ***/
On Sat, May 9, 2009 at 7:13 PM, Amadou DIALLO <[email protected]> wrote:
> Hi Allan,
>
> This is my 3 cents solution (have not get the time to put it in a
> formal mata programm, just tested on the command line). I hope it can
> lead you to what you need to achieve if you play around a bit with it.
>
> Best regards.
>
> Amadou.
>
>
> clear
>
> // suppose you have 3 variables x, y, z, with number of observations
> of x+y = number of observations of z
>
> set obs 10
>
> g x=.
> replace x=1 in 1
> replace x=2 in 2
>
> g y=.
> replace y =1 in 1
> replace y =2 in 2
> replace y =3 in 3
> replace y =4 in 4
> replace y =5 in 5
>
> //g z = round(uniform(),1)
>
> g z=.
> replace z =1 in 1
> replace z =2 in 2
> replace z =3 in 3
> replace z =4 in 4
> replace z =5 in 5
> replace z =6 in 6
> replace z =7 in 7
> replace z =8 in 8
> replace z =9 in 9
> replace z =10 in 10
>
> // tomata x y z, missing // Don't know why not working
> tomata x in 1/2
> tomata y in 1/5
> tomata z
>
> mata:
>
> c = rows(x)
> d = cols(y')
> M=J(c,d,.)
> for(i=1;i<=d;i++) {
> M[1,i] = z[i]
> k=d+i
> M[c,i] = z[k]
> }
> M
> z
> end
>
>
>
> 2009/5/9, Glenn Goldsmith <[email protected]>:
>> Hi Allan,
>>
>> It seems like what you're after is more-or-less the same as converting from
>> sparse matrix storage, so the mata code here should do the trick:
>>
>> http://www.stata.com/statalist/archive/2009-04/msg00142.html
>>
>> You just need to get your variables into a mata matrix to start with and you
>> should be away:
>>
>> mata : st_view(x=.,.,("var2","var1","var3"))
>>
>> NB: Note the inverted order of var1 and var2. This is because the code for
>> -sparse()- assumes that the first variable provides the row index, and the
>> second variable provides the column index.
>>
>> HTH,
>>
>> Glenn.
>>
>> Allan Joseph Medwick <[email protected]> wrote:
>>
>> Hi, All,
>>
>> I have three variables (var1, var2, var3). I would like to create a
>> matrix where the values of var1 are the columns (ascending), the
>> values of var2 are the rows (also ascending), and the values of var3
>> are the elements in the matrix. I know there must be a user defined
>> procedure out there to do this, but I haven't been able to find it.
>>
>> Thanks,
>> Allan
>>
>>
>> *
>> * For searches and help try:
>> * http://www.stata.com/help.cgi?search
>> * http://www.stata.com/support/statalist/faq
>> * http://www.ats.ucla.edu/stat/stata/
>>
>
>
> --
> ---
>
> Amadou B. DIALLO, PHD
> Development Economist
> Director, Center for Research and Training on Adult Education
> Mayotte, FRANCE
> www.aprosasoma.org
> +262639693250
> *
> * For searches and help try:
> * http://www.stata.com/help.cgi?search
> * http://www.stata.com/support/statalist/faq
> * http://www.ats.ucla.edu/stat/stata/
>
--
To every ω-consistent recursive class κ of formulae there correspond
recursive class signs r, such that neither v Gen r nor Neg(v Gen r)
belongs to Flg(κ) (where v is the free variable of r).
*
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