st: Predict and adjust

[Fred Wolfe <fwolfe@arthritis-research.org>]

I know this has come up on the Stata list before, but in somewhat 
different form. I wonder if someone could explain the following to me.

The manual and the FAQ state that the results after predict and 
adjust are the same for the following circumstances, and they site 
this analysis that I have rerun.

. sysuse auto, clear
(1978 Automobile Data)

. regress mpg weight length foreign

      Source |       SS       df       MS              Number of obs =      74
-------------+------------------------------           F(  3,    70) =   48.10
       Model |   1645.2889     3  548.429632           Prob > F      =  0.0000
    Residual |  798.170563    70  11.4024366           R-squared     =  0.6733
-------------+------------------------------           Adj R-squared =  0.6593
       Total |  2443.45946    73  33.4720474           Root MSE      =  3.3767

------------------------------------------------------------------------------
         mpg |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      weight |  -.0043656   .0016014    -2.73   0.008    -.0075595   -.0011718
      length |  -.0827432   .0547942    -1.51   0.136    -.1920267    .0265403
     foreign |  -1.707904    1.06711    -1.60   0.114    -3.836188    .4203806
       _cons |   50.53701   6.245835     8.09   0.000     38.08009    62.99394
------------------------------------------------------------------------------

. predict p, xb

. su p

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
           p |        74     21.2973    4.747442   10.12822   30.51564

. su mpg

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
         mpg |        74     21.2973    5.785503         12         41

. adjust

---------------------------------------------------------------------------
     Dependent variable: mpg     Command: regress
   Variables left as is: weight, length, foreign
---------------------------------------------------------------------------

----------------------
      All |         xb
----------+-----------
          |    21.2973
----------------------
     Key:  xb  =  Linear Prediction


The above shows that adjust and predict produce the same results.

However, when I use the logistic regression example from page 20 
(Reference A-J) for the adjust command, predict and adjust give 
different results, as shown below. Can someone help me to understand 
why this is so and what I might do to obtain the same results using adjust.

Thanks,

Fred



. use http://www.stata-press.com/data/r9/lbw,clear
(Hosmer & Lemeshow data)

. xi: logistic low age lwt i.race smoke,nolog
i.race            _Irace_1-3          (naturally coded; _Irace_1 omitted)

Logistic regression                               Number of obs   =        189
                                                  LR chi2(5)      =      20.08
                                                  Prob > chi2     =     0.0012
Log likelihood = -107.29639                       Pseudo R2       =     0.0856

------------------------------------------------------------------------------
         low | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         age |   .9777443   .0334083    -0.66   0.510     .9144097    1.045466
         lwt |   .9875761    .006305    -1.96   0.050     .9752956    1.000011
    _Irace_2 |   3.425372   1.771281     2.38   0.017     1.243215    9.437768
    _Irace_3 |     2.5692   1.069301     2.27   0.023     1.136391    5.808555
       smoke |   2.870346    1.09067     2.77   0.006        1.363    6.044672
------------------------------------------------------------------------------

. tab low

birthweight |
     <2500g |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |        130       68.78       68.78
          1 |         59       31.22      100.00
------------+-----------------------------------
      Total |        189      100.00

. predict p
(option p assumed; Pr(low))

. su p

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
           p |       189    .3121693    .1481592   .0471794   .7063822


. adjust,pr

-------------------------------------------------------------------------------
     Dependent variable: low     Command: logistic
   Variables left as is: age, lwt, smoke, _Irace_2, _Irace_3
-------------------------------------------------------------------------------

----------------------
      All |         pr
----------+-----------
          |    .289227
----------------------
     Key:  pr  =  Probability


Fred Wolfe
National Data Bank for Rheumatic Diseases
Wichita, Kansas
Tel +1 316 263 2125
fwolfe@arthritis-research.org


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