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st: RE: Foreach and macros

From   "Nick Cox" <>
To   <>
Subject   st: RE: Foreach and macros
Date   Mon, 20 Oct 2008 16:41:32 +0100

The sting in Joao's problem is from wanting a regression of each variable in turn on the others: once the response is selected, it must be removed from the list of potential predictors. 

Try (I've changed the notation so that local macro y contains the name of the response) 

local vars "read write rw"
foreach y of local vars {
	local x : list vars - y 
	svy: regress `y' `x'

For more, see -help macrolists-. 


Joao Ricardo F. Lima

I'm trying to test for collinearity in survey regression. I have many
variables. Then, I was thinking how to use foreach. Looking at UCLA
Stata FAQ, I can do:

use, clear
generate rw = read*write
svyset [pw=socst], strata(ses)
svy: regress read write rw
svy: regress write read rw
svy: regress rw write read

I generated a local macro and used the foreach command. However, the
example below is wrong because I have the variable read in both sides
of regress...

local vars "read write rw"
foreach x of local vars {
        svy: regress `x' `vars'

Survey: Linear regression

Number of strata   =         3                  Number of obs      =       200
Number of PSUs     =       200                  Population size    =     10481
                                                Design df          =       197
                                                F(   0,    197)    =         .
                                                Prob > F           =         .
                                                R-squared          =    1.0000

             |             Linearized
        read |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
        read |          1   1.05e-15  9.5e+14   0.000            1           1
       write |   7.26e-15   9.17e-16     7.92   0.000     5.45e-15    9.07e-15
          rw |  -1.45e-16   1.83e-17    -7.93   0.000    -1.81e-16   -1.09e-16
       _cons |  -3.91e-13   5.18e-14    -7.55   0.000    -4.93e-13   -2.89e-13
(running regress on estimation sample)

I read Nick's columm on Stata Journal, they are very useful and clear,
but I still don´t know how to solve this problem.

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