Re: st: How to make a code faster - alternatives to egen var = concat(vars) ?,

[Antoine Terracol <terracol@univ-paris1.fr>]

This might not be the most efficient way, but it works:

capture program drop mymean
program define mymean, byable(recall)
syntax varname
marksample touse
local groupname ""
foreach var of local _byvars {
	local a =`var'[_byn1()]
	if "`a'"=="." {
			local a "missing"
			}
	local groupname "`groupname'`a'"
	}
su `varlist' if `touse'
scalar mean`groupname'=r(mean)
end

scalar drop _all
sysuse auto, clear
bysort foreign rep78 : mymean price
scalar dir


Antoine


On 17/06/2010 16:56, Tiago V. Pereira wrote:
> Thank you so much again, Antoine!
>
> Yes, this is a very efficient way! However, I could not figure out how I
> can save the combination of the categorical variables that a specific
> meanX refers to.
>
> For example, the commands
>
>   sysuse auto, clear
>   bysort foreign rep78 : mymean price
>   scalar dir
>
> show a list of scalars containing the mean of the ith combination, but I
> don't know if the mean10 refers to the combination  "foreign = Foreign,
> rep78 =4"   or  "foreign = Foreign, rep78 = 5"
>
>
> [Actually I do in this specific case if I take a look at each value from
> the output.]
>
> Nevertheless, assuming a very large number of categorical variables
> (n>10), I cannot write a loop and say that mean2451 refers to the
> combination x1==0 x2==0 x3==2 x4==0 x5==2 and x6==1. I want to summarize
> the mean of this combination in group 1 and generate a separate variable
> for group 2.
>
>
> for example
>
> bysort x1 x2 x3 x4 x5 x6 : mymean score if group==1
>
> */ yes, -mymean- needs further amendments to have this option
>
> replace score = mean2451 if group==2&x1==0&x2==0&x3==2&x4==0&x5==2&and&x6==1
>
>
> So, In this case I know that mean2451 comes from the combination
> x1==0&x2==0&x3==2&x4==0&x5==2&and&x6==1 from group 1 and I replace its
> value for all subjects from group 2 having an identical combination.
>
>
> This is getting tough, but you have any additional tips, I will be really
> very grateful!
>
> Thanks again.
>
> Tiago
>
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> > Tiago,
> >
> > You would have to define a -byable- -program-, such as:
> >
> > capture program drop mymean
> > program define mymean, byable(recall)
> > syntax varname
> > marksample touse
> > su `varlist' if `touse'
> > local a= _byindex()
> > scalar mean`a'=r(mean)
> > end
> >
> > sysuse auto, clear
> > bysort foreign rep78 : mymean price
> > scalar dir
> >
> > Antoine
> >
> > On 17/06/2010 15:47, Tiago V. Pereira wrote:
> > > Thanks, Antoine!
> > >
> > > But for each combinations, I want to save a local containing the
> > > r(mean).
> > > Is it possible to do that using -bysort-?
> > >
> > > Tiago
> > >
> > >
> > > ---------------
> > > Dear statalisters,
> > >
> > > I am working on a stata code, and I need some advice.
> > >
> > > I have n categorical variables that assumes values equal to 0, 1 or 2.
> > > My
> > > objective is to summarize a continuous variable (say, age) by all
> > > possible
> > > combinations of these categorical variables.
> > >
> > >
> > > For example, suppose I have 5 categorical variables (x1, x2, x3, x4 and
> > > x5):
> > >
> > >
> > > sum age if x1==0&x2==0&x3==0&x4==0&x5==0
> > >
> > > then
> > >
> > > sum age if x1==0&x2==0&x3==0&x4==0&x5==1
> > >
> > > then
> > >
> > > sum age if x1==0&x2==0&x3==0&x4==0&x5==2
> > >
> > > and so forth.
> > >
> > >
> > > What I am doing is the following: (1) I generate a string of the
> > > categorical variables
> > >
> > > egen combination = concat(x1 x2 x3 x4 x5)
> > >
> > > (2) convert them to numeric
> > >
> > > encode combination, gene (y)
> > >
> > > and loop over the values of the new variable y to summarize the
> > > continuous
> > > variable
> > >
> > > forvalues i = 1/`some_max_value' {
> > >
> > > sum age if y=`i'
> > >
> > > }
> > >
> > > This naïve solution works very well for small samples (_N<1000) and
> > > small
> > > number of categorical variables (5 to 7). But when I need investigate in
> > > a
> > > larger sample with a larger number of categorical variables, this code
> > > is
> > > highly inefficient (e.g. slow).
> > >
> > > Do you have any suggestions to make this procedure faster in larger data
> > > sets?
> > >
> > > Thanks in advance!
> > >
> > > Tiago
> > >
> > >
> > >
> > >
> > >
> > >
> > > *
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