st: svy subpop option and e(sample)

[Richard Williams <richardwilliams.ndu@gmail.com>]

I've just noticed that the e(sample) option does not work the way I 
expect it to when using svy and the subpop option. Specifically, 
e(sample) codes everyone in the population as 1, whether they were in 
the subpopulation specified or not. I guess I can sort of kind of see 
a rationale for doing this (the whole population is used to compute 
the standard errors) but it has the potential to screw up your 
post-estimation analysis if you only wanted to do things with (what 
you thought) was the subpopulation you expected.

The following illustrates this. There are only 1086 cases in the 
subpopulation selected, but probabilities are computed for all 10,000 
cases. That is, coefficients computed using only the black 
subpopulation are used to compute probabilities for the entire population:

. webuse nhanes2f, clear

. svy, subpop(black): ologit health age female
(running ologit on estimation sample)

Survey: Ordered logistic regression

Number of strata   =        30                 Number of obs      =      10000
Number of PSUs     =        60                 Population size    =  113285074
                                               Subpop. no. of obs =       1086
                                               Subpop. size       =   11189236
                                               Design df          =         30
                                               F(   2,     29)    =      29.87
                                               Prob > F           =     0.0000

------------------------------------------------------------------------------
             |             Linearized
      health |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         age |  -.0452349   .0063482    -7.13   0.000    -.0581996   -.0322703
      female |  -.3975887   .1336441    -2.97   0.006    -.6705263   -.1246511
-------------+----------------------------------------------------------------
       /cut1 |  -4.427029   .2976634   -14.87   0.000    -5.034939   -3.819119
       /cut2 |   -2.97326   .2848889   -10.44   0.000    -3.555081   -2.391439
       /cut3 |  -1.347426   .2497407    -5.40   0.000    -1.857465   -.8373876
       /cut4 |   -.214417   .2857434    -0.75   0.459    -.7979829    .3691488
------------------------------------------------------------------------------
Note: 1 stratum omitted because it contains no subpopulation members.

. predict p1 p2 p3 p4 p5 if e(sample)
(option pr assumed; predicted probabilities)
(337 missing values generated)

. sum p1 p2 p3 p4 p5

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
          p1 |     10000    .1362895    .0853605   .0286835   .3358028
          p2 |     10000    .2341256    .0876537   .0835067   .3482424
          p3 |     10000    .3367498    .0391877   .2327486   .3854614
          p4 |     10000    .1639109    .0712129   .0549047   .2749383
          p5 |     10000    .1289243    .0859123   .0284552   .3339704

I think one solution is to change the predict command to something like

predict p51 p52 p53 p54 p55 if e(sample) & black
predict p61 p62 p63 p64 p65 if e(sample) & `=e(subpop)'

But, are there others? Preferably simpler ones? And is this good 
behavior for e(sample) in the first place?

-------------------------------------------
Richard Williams, Notre Dame Dept of Sociology
OFFICE: (574)631-6668, (574)631-6463
HOME:   (574)289-5227
EMAIL:  Richard.A.Williams.5@ND.Edu
WWW:    http://www.nd.edu/~rwilliam

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