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| From | "Feiveson, Alan H. (JSC-SK311)" <alan.h.feiveson@nasa.gov> |
| To | "statalist@hsphsun2.harvard.edu" <statalist@hsphsun2.harvard.edu> |
| Subject | st: margins for subset of model coefficients w/o constant term |
| Date | Thu, 9 Feb 2012 08:35:26 -0600 |
Hi - I thought I could use -margins- to get the equivalent of repeated calls to -lincom- for a subset of model coefficients without the constant term. The problem is I can't use "at" to make the constant term zero because the implied variable that _b[_cons] is multiplying is all ones. So I thought I would "trick" Stata by forming a variable of all ones and fitting the model with no constant. But then in some cases I get a "non-estimable" response or no standard errors. Yet the same combination in -lincom- works fine. Here's my example:
**** The original estimation ****
. xtmixed vo2max phase1 day1 ||isub: ,nolog
Mixed-effects ML regression Number of obs = 55
Group variable: isub Number of groups = 7
Obs per group: min = 6
avg = 7.9
max = 10
Wald chi2(2) = 20.85
Log likelihood = -15.261456 Prob > chi2 = 0.0000
------------------------------------------------------------------------------
vo2max | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
phase1 | -.4503613 .126299 -3.57 0.000 -.6979027 -.2028199
day1 | .0013801 .0010304 1.34 0.180 -.0006394 .0033996
_cons | 2.820505 .2052827 13.74 0.000 2.418158 3.222851
------------------------------------------------------------------------------
------------------------------------------------------------------------------
Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval]
-----------------------------+------------------------------------------------
isub: Identity |
sd(_cons) | .5272873 .1457558 .306728 .9064447
-----------------------------+------------------------------------------------
sd(Residual) | .2552611 .0260635 .2089645 .3118149
------------------------------------------------------------------------------
LR test vs. linear regression: chibar2(01) = 62.63 Prob >= chibar2 = 0.0000
**** Refit with "one" and no constant ****
. gen one = 1
. xtmixed vo2max phase1 day1 one,noc ||isub: ,nolog
Mixed-effects ML regression Number of obs = 55
Group variable: isub Number of groups = 7
Obs per group: min = 6
avg = 7.9
max = 10
Wald chi2(3) = 194.90
Log likelihood = -15.261456 Prob > chi2 = 0.0000
------------------------------------------------------------------------------
vo2max | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
phase1 | -.4503613 .126299 -3.57 0.000 -.6979027 -.2028199
day1 | .0013801 .0010304 1.34 0.180 -.0006394 .0033996
one | 2.820505 .2052827 13.74 0.000 2.418158 3.222851
------------------------------------------------------------------------------
------------------------------------------------------------------------------
Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval]
-----------------------------+------------------------------------------------
isub: Identity |
sd(_cons) | .5272873 .1457558 .306728 .9064447
-----------------------------+------------------------------------------------
sd(Residual) | .2552611 .0260635 .2089645 .3118149
------------------------------------------------------------------------------
LR test vs. linear regression: chibar2(01) = 62.63 Prob >= chibar2 = 0.0000
**** now try -margins- ****
. margins, at( phase1=1 day1= (50 100) one = 0)
Adjusted predictions Number of obs = 55
Expression : Linear prediction, fixed portion, predict()
1._at : phase1 = 1
day1 = 50
one = 0
2._at : phase1 = 1
day1 = 100
one = 0
------------------------------------------------------------------------------
| Delta-method
| Margin Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
_at |
1 | -.3813569 . . . . .
2 | -.3123525 . . . . .
------------------------------------------------------------------------------
**** -margins gives point estimates, but no standard errors; yet -lincom- is fine ****
. lincom _b[phase1]*1 + _b[day1]*50
( 1) [vo2max]phase1 + 50*[vo2max]day1 = 0
------------------------------------------------------------------------------
vo2max | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
(1) | -.3813569 .0886184 -4.30 0.000 -.5550458 -.207668
------------------------------------------------------------------------------
. lincom _b[phase1]*1 + _b[day1]*100
( 1) [vo2max]phase1 + 100*[vo2max]day1 = 0
------------------------------------------------------------------------------
vo2max | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
(1) | -.3123525 .0711577 -4.39 0.000 -.4518189 -.172886
------------------------------------------------------------------------------
In this example, I got point estimates, and no standard errors. In other more complicated models, I couldn't even get point estimates because the linear combinations I specified were considered "non-estimable" - yet they were clearly estimable with -lincom,-. For simpler models, (not shown) this method did work.
Any suggestions for
1) How to use -margins- to do what I want without the "ones" trick?
2) Why the "ones" trick doesn't always work.
Thanks
Al Feiveson
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