Statalist


[Date Prev][Date Next][Thread Prev][Thread Next][Date Index][Thread Index]

Re: st: -margins- vs -adjust-


From   Jeph Herrin <junk@spandrel.net>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: -margins- vs -adjust-
Date   Wed, 04 Nov 2009 22:02:29 -0500

Thank you for the clarifications, which are very helpful.
I apologize for any confusion that arose from my referring
to predictive margins as marginal effects; what I did not
see was a way to get predictive margins from -margins-
without using factor notation.

cheers,
Jeph


Jeff Pitblado, StataCorp LP wrote:
Jeph Herrin <junk@spandrel.net> noticed that -adjust- has been superseeded by
the -margins- command in Stata 11:

After getting an error with -adjust-, I discovered that it
had been deprecated in version 11. The online help says:

"adjust has been superseded by margins.  margins can do everything
  that adjust did and more."

However, as far as I can tell, this is not the case. I could use
-adjust- with models that did not include factor variables; however,
with -margins- there seems to be no option to find marginal effects
for independent variables that are not i.vars.

Is this true? Or is there a way to fool -margins- into working with
non-factor variables? Apologies if this has come up before, I could
not find anything on the list or FAQ.

If it is true, this is a complete nusiance. I do not normally
include dichotomous variables as factor variables, and am not
interested in starting now. Moreover, I have estimation results
from -xtmelogit- models that ran for days; am I going to have
to re-estimate with -i.female- instead of -female- to get marginal
effects? (-adjust- gives errors). That, or calculate my own
marginal effects from scratch.

It isn't clear what Jeph means by "marginal effects" related to the -adjust-
command.

Martin Weiss <martin.weiss1@gmx.de> gave two examples using the -dydx()-
option of -margins- to compute marginal effects using a -logit- model fit.
The first used the 'i.' operator on a factor variable (named 'race') in the
-logit- model fit, the second treated 'race' as a standard variable.

Peter Lachenbruch <Peter.Lachenbruch@oregonstate.edu> noted that Martin's
examples were fitting two different models.  Note -margins- computed the
marginal effect of 'race' differently, depending of whether it was specfied as
a factor variable (with the 'i.' operator) or not.

Jeph replied to Martin's post using a regular variable in the -margins-
varlist, such as

	. margins smoke
	factor smoke not found in e(b)
	r(111);

The variables in -margins- varlist specification are interpreted as factor
variables, identifying the predictive margins of interest.  Thus the above
example is requesting the predictive margins for the -smoke- factor variable.

I imagine that Jeph might be referring to the -by()- option of -adjust-.  If
that is the case, then we just need to translate between -adjust- and
-margins- syntax.

Here are a few examples based on Martin's first post to this thread (I've
included the Stata output from these examples following my signature):

	. webuse lbw
	. logit low age lwt race smoke ptl ht ui

Compute the mean prediction, Pr(low), over each level of smoking status,
adjusting for the mean of each predictor within smoking status:

	. adjust, pr by(smoke)

translates to

	. margins, over(smoke) at((means) *)

Compute the mean prediction, Pr(low), over each level of smoking status,
adjusting for the overall mean of each predictor (except smoking status):

	. adjust age lwt race ptl ht ui, pr by(smoke)

translates to

	. margins, over(smoke) at((omeans) * (asobserved) smoke)

Note that Jeph will get the same results from -margins- when the -logit-
fit treated -smoke- (and -race-) as factor variables.

Verkuilen, Jay <JVerkuilen@gc.cuny.edu> posted an example where -adjust-
allows variables in the -by()- option that were not in the model fit.
The -over()- option of -margins- allows this too.

Philip Ender <ender97@gmail.com> used Jay's -logit- model fit to show that
-margins- with the -if- clause produces similar point and standard error
estimates, but that the confidence intervals are different.

The -pr- confidence intervals from -adjust- are computed by transforming the
end-points of the CI limits from the linear prediction.
-margins- computes the CI limits using the normal approximation is valid.

--Jeff
jpitblado@stata.com

*** Stata output from the preceeding examples:

. webuse lbw, clear
(Hosmer & Lemeshow data)

. logit low age lwt race smoke ptl ht ui

Iteration 0: log likelihood = -117.336 Iteration 1: log likelihood = -102.61679 Iteration 2: log likelihood = -102.17476 Iteration 3: log likelihood = -102.17373 Iteration 4: log likelihood = -102.17373
Logistic regression                               Number of obs   =        189
                                                  LR chi2(7)      =      30.32
                                                  Prob > chi2     =     0.0001
Log likelihood = -102.17373                       Pseudo R2       =     0.1292

------------------------------------------------------------------------------
         low |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         age |  -.0332327   .0357713    -0.93   0.353    -.1033431    .0368777
         lwt |  -.0120948   .0066158    -1.83   0.068    -.0250617     .000872
        race |   .4462621   .2150445     2.08   0.038     .0247827    .8677415
       smoke |   .9255414   .3980923     2.32   0.020     .1452947    1.705788
         ptl |   .5397383   .3469187     1.56   0.120    -.1402099    1.219686
          ht |   1.799337   .6872194     2.62   0.009     .4524118    3.146262
          ui |   .7148045   .4634311     1.54   0.123    -.1935037    1.623113
       _cons |  -.1029665   1.284609    -0.08   0.936    -2.620754    2.414821
------------------------------------------------------------------------------

. . // Compute the mean prediction, Pr(low), over each level of smoking status,
. // adjusting for the mean of each predictor within smoking status:
. adjust, pr by(smoke)

-------------------------------------------------------------------------------
     Dependent variable: low     Equation: low     Command: logit
   Variables left as is: age, lwt, race, ptl, ht, ui
-------------------------------------------------------------------------------

----------------------
smoked    |
during    |
pregnancy |         pr
----------+-----------
        0 |     .22079
        1 |    .395668
----------------------
     Key:  pr  =  Probability

. margins, over(smoke) at((means) *)

Adjusted predictions                              Number of obs   =        189
Model VCE    : OIM

Expression   : Pr(low), predict()
over         : smoke
at           : 0.smoke
                   age             =    23.42609 (mean)
                   lwt             =    130.9043 (mean)
                   race            =    2.095652 (mean)
                   smoke           =           0 (mean)
                   ptl             =    .1217391 (mean)
                   ht              =    .0608696 (mean)
                   ui              =    .1304348 (mean)
               1.smoke
                   age             =    22.94595 (mean)
                   lwt             =    128.1351 (mean)
                   race            =    1.459459 (mean)
                   smoke           =           1 (mean)
                   ptl             =    .3108108 (mean)
                   ht              =    .0675676 (mean)
                   ui              =    .1756757 (mean)

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       smoke |
          0  |   .2207901   .0417458     5.29   0.000     .1389698    .3026104
          1  |   .3956685   .0609789     6.49   0.000     .2761519     .515185
------------------------------------------------------------------------------

. . // Compute the mean prediction, Pr(low), over each level of smoking status,
. // adjusting for the overall mean of each predictor (except smoking status):
. adjust age lwt race ptl ht ui, pr by(smoke)

-------------------------------------------------------------------------------
     Dependent variable: low     Equation: low     Command: logit
 Covariates set to mean: age = 23.238095, lwt = 129.82011, race = 1.8465608,
                         ptl = .1957672, ht = .06349206, ui = .14814815
-------------------------------------------------------------------------------

----------------------
smoked    |
during    |
pregnancy |         pr
----------+-----------
        0 |    .214918
        1 |    .408544
----------------------
     Key:  pr  =  Probability

. margins, over(smoke) at((omeans) * (asobserved) smoke)

Predictive margins                                Number of obs   =        189
Model VCE    : OIM

Expression   : Pr(low), predict()
over         : smoke
at           : 0.smoke
                   age             =     23.2381 (omean)
                   lwt             =    129.8201 (omean)
                   race            =    1.846561 (omean)
                   ptl             =    .1957672 (omean)
                   ht              =    .0634921 (omean)
                   ui              =    .1481481 (omean)
               1.smoke
                   age             =     23.2381 (omean)
                   lwt             =    129.8201 (omean)
                   race            =    1.846561 (omean)
                   ptl             =    .1957672 (omean)
                   ht              =    .0634921 (omean)
                   ui              =    .1481481 (omean)

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       smoke |
          0  |   .2149182   .0435287     4.94   0.000     .1296035    .3002328
          1  |   .4085436   .0662837     6.16   0.000     .2786299    .5384573
------------------------------------------------------------------------------

<end>
*
*   For searches and help try:
*   http://www.stata.com/help.cgi?search
*   http://www.stata.com/support/statalist/faq
*   http://www.ats.ucla.edu/stat/stata/

*
*   For searches and help try:
*   http://www.stata.com/help.cgi?search
*   http://www.stata.com/support/statalist/faq
*   http://www.ats.ucla.edu/stat/stata/



© Copyright 1996–2014 StataCorp LP   |   Terms of use   |   Privacy   |   Contact us   |   What's new   |   Site index