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From |
"Nick Cox" <[email protected]> |

To |
<[email protected]> |

Subject |
st: RE: RE: cycling through individual indices |

Date |
Thu, 18 Sep 2008 12:07:29 +0100 |

```
gen whatever = 0
qui forval j = 1/624 {
replace whatever = whatever + (`j' <= Agemonth) * (Var_`j' ==
<magic_number>)
}
-----Original Message-----
From: [email protected]
[mailto:[email protected]] On Behalf Of Nick Cox
Sent: 17 September 2008 17:57
To: [email protected]
Subject: st: RE: cycling through individual indices
(You should start new threads, please; not reply to irrelevant previous
ones.)
I wouldn't rule out the -reshape- solution. A -reshape- may be slow but
once it's done, it's done.
Otherwise I think you can avoid loops over observations.
-egen, anycount()- is in essence a wrapper for a loop over variables, as
-viewsource _ganycount.ado- will show. So, you too can loop over
variables. You just need to customise your loop.
gen whatever = 0
qui forval j = 1/624 {
replace whatever = whatever + (whatever <= Agemonth) * (Var_`j'
== <magic_number>)
}
So (whatever <= Agemonth) evaluates to 1 or 0 as the case may be, and in
particular annihilates invalid terms. This operation is automatically
vectorised.
Another possibility is just to clean up your data first:
qui forval j = 1/624 {
replace Var_`j' = . if `j' > Agemonth
}
Nick
[email protected]
Johannes Geyer
I have Stata 10.1 IC and I try to create individual specific sums in a
large dataset. The problem is a bit complicated and I have to cycle
through all individuals and variables using the "in" qualifier. I am
curious if anyone has an idea how to solve this problem more
efficiently.
Here is the problem:
The data are in wide format and look like
ID Agemonth Var_1 Var_2... ...Var_623
Var_624
1 532 2 2 14 14
2 345 7 7 14 Mis
3 236 3 3 Mis Mis
4 267 2 2 12 12
and so forth; there are about 50,000 observations. "Agemonth" indicates
the observation period which is individual specific: "1" means January
of
the year after the person turned 14, "2" is February and so forth. That
means e.g. "ID" 1 was observed 532 months after the year he/she turned
14.
The index of the variables indicate the same time index. Thus, person 1
was observed from Var_1 until Var_532. Unfortunately, that does not mean
that Var_533 or even Var_623 is missing but it may have a value like in
the example above.
Var_# has a number of distinct values and I need to sum them up in each
case. If I had no invalid observations I could type
egen sum1 = anycount(Var_*), values(1)
However, then I count also invalid observations.
I ended up with looping through individuals (~50,000) and variables
(624),
summing up one by one but I really doubt that this is the "best"
solution
(and hope that it is not):
*******************
#d;
gen sum1 = 0;
sort ID;
gen index = _n;
qui sum index;
forvalues indis = `r(min)'/`r(max)' {;
di "`indis'";
forvalues f = 1/624 {;
if `f' <=Agemonth in `indis' {;
qui replace sum1 = sum1 + (Var_`f' == 1) in
`indis';
};
};
};
*******************
Another possibilty would be to have the data in long format - however,
since I have so many periods it takes a while to reshape the data, even
in
portions. I tried that with a 10% sample and "reshape" took more than
one
hour (maybe I have to ask for a better computer...).
*
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* http://www.stata.com/help.cgi?search
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* http://www.ats.ucla.edu/stat/stata/
```

**References**:**st: RE: analogues of macros in Mata?***From:*"Nick Cox" <[email protected]>

**st: cycling through individual indices***From:*Johannes Geyer <[email protected]>

**st: RE: cycling through individual indices***From:*"Nick Cox" <[email protected]>

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