Fred, I haven't thought through this completely (and Michael and
Marteen have already provided great explanations), but as I was
thinking about your example I calculated the adjusted probabilities
holding each variable constant at its mean and it seems to produces
the same marginal results as the adjust command. I never have used
adjust without specifying any variables, so I'm not sure about this
exactly. Tim
. 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
------------------------------------------------------------------------------
. predict p
(option p assumed; Pr(low))
. adjust,pr
--------------------------------------------------------------------------------------------------------
Dependent variable: low Command: logistic
Variables left as is: age, lwt, smoke, _Irace_2, _Irace_3
--------------------------------------------------------------------------------------------------------
----------------------
All | pr
----------+-----------
| .289227
----------------------
Key: pr = Probability
. /*generate linear predictor, holding predictors constant at mean*/
.
.
. foreach var of varlist low age lwt _Irace* smoke {
2. qui sum `var'
3. local mean`var'=r(mean)
4. }
. gen x1=_b[_cons]+(`meanage'*_b[age])+(`mean_Irace_2'*_b[_Irace_2])+(`mean_Irace_3'*_b[_Irace_3])+(`mea
nlwt'*_b[lwt])+(`meansmoke'*_b[smoke])
.
. /*generate odds*/
.
. gen odds3=exp(x1)
. /*generate probability*/
.
. gen p3=odds3/(odds3+1)
. list p3 in 1
+----------+
| p3 |
|----------|
1. | .2892265 |
+----------+
. sum p3
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------------
p3 | 189 .2892265 0 .2892265 .2892265
On 12/23/06, Fred Wolfe <[email protected]> wrote:
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
[email protected]
*
* For searches and help try:
* http://www.stata.com/support/faqs/res/findit.html
* http://www.stata.com/support/statalist/faq
* http://www.ats.ucla.edu/stat/stata/
*
* For searches and help try:
* http://www.stata.com/support/faqs/res/findit.html
* http://www.stata.com/support/statalist/faq
* http://www.ats.ucla.edu/stat/stata/
