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Re: st: Memory requirements for factor variables

From   Partha Deb <>
Subject   Re: st: Memory requirements for factor variables
Date   Mon, 03 May 2010 09:40:12 -0400


I'll look at -fese-, etc. but to answer your question - yes, I do mean to create dummies based on an OR condition, over 9 categorical variables to be precise. Each is a variable that contains codes for disease categories, and an individual may present with more than one disease. Also, I need the coefficients on those dummies (a la the coefficients in a hedonic regression) so I can't partial them out.



Austin Nichols wrote:
I think you want to model your code on -fese- (ssc desc fese) or
-felsdvreg- or -felsdvregdm- (findit felsdvreg).  But can you give a
more germane example?  Do you really mean to create dummies based on
an OR condition over 4 categorical variables (testing whether any of
the four is a given level)?  Do you need estimates for your 500
dummies, or do you just want to partial them out of the regression?
The second is much easier than the first.

forvalues i=1/100 {
  gen byte ID`i' = (D1==`i' | D2==`i' | D3==`i' | D4==`i')

On Mon, May 3, 2010 at 9:23 AM, Partha Deb <> wrote:
Federico - that is definitely a solution I hadn't thought of.  But, I do
worry that the "simple" formula for the OLS estimate may not be optimal
given the size of the dataset and potential scaling issues.  I'm still
holding out for a slick answer from the Stata gurus, but I might end up
using yours.  Thanks.


Federico Belotti wrote:

I think there is no way to do that in stata. An alternative could be mata.
Clearly, you have to write down the ado for your econometric model. An
example using OLS is below.



******  do *******
clear all
set mem 10m
set more off

set seed 123456

set obs 100000

real matrix factor_reg(rows,cols,d1,d2,d3,d4,x,y) {

       D = J(rows,cols,0)
       for(i=1;i<=cols;i++) {
               for(j=1;j<=rows;j++) {
                       if (d1[j]==i | d2[j]==i | d3[j]==i | d4[j]==i)
       X = x,D,J(100000,1,1)
       Y = y
       beta = invsym(X'X)*(X'Y)

gen x = rnormal()
gen u = rnormal()
gen int d = int(_n/1000)
gen int d1 = int(_n/1100)
gen int d2 = int(_n/1200)
gen int d3 = int(_n/1300)
gen int d4 = int(_n/1400)


gen y = x + u


regress y x i.d

sum d

mata: factor_reg(100000,100,d1,d2,d3,d4,x,y)

forvalues i=1/`r(max)' {

gen byte Id`i' = (d1==`i' | d2==`i' | d3==`i' | d4==`i')


regress y x Id*


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Partha Deb
Professor of Economics
Hunter College
ph:  (212) 772-5435
fax: (212) 772-5398

Emancipate yourselves from mental slavery
None but ourselves can free our minds.
	- Bob Marley

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