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Re: st: Computation of ATT in logistic regression
From
Tim Wade <[email protected]>
To
[email protected]
Subject
Re: st: Computation of ATT in logistic regression
Date
Mon, 22 Nov 2010 13:39:08 -0500
I think something like this can be accomplished using the -margins-
command, posting the results and then using -lincon-. Note the
estimates from the logistic model differs from the linear model, but
not by much. See the Stata Reference Manual, -margins-, Example 10.
sysuse auto.dta
xtile mpgcat=mpg, nq(2)
logistic foreign i.mpgcat price
margins mpgcat, post coeflegend
lincom _b[2.mpgcat]-_b[1bn.mpgcat]
*linear model for risk difference estimation
glm foreign mpgcat price, link(identity) fam(binomial)
Output below:
. sysuse auto.dta
(1978 Automobile Data)
. xtile mpgcat=mpg, nq(2)
. logistic foreign i.mpgcat price
Logistic regression Number of obs = 74
LR chi2(2) = 12.96
Prob > chi2 = 0.0015
Log likelihood = -38.553753 Pseudo R2 = 0.1439
------------------------------------------------------------------------------
foreign | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
2.mpgcat | 8.354628 5.601601 3.17 0.002 2.244991 31.09136
price | 1.000155 .0001045 1.48 0.139 .9999497 1.000359
------------------------------------------------------------------------------
. margins mpgcat, post coeflegend
Predictive margins Number of obs = 74
Model VCE : OIM
Expression : Pr(foreign), predict()
------------------------------------------------------------------------------
| Margin Legend
-------------+----------------------------------------------------------------
mpgcat |
1 | .1174213 _b[1bn.mpgcat]
2 | .5022026 _b[2.mpgcat]
------------------------------------------------------------------------------
. lincom _b[2.mpgcat]-_b[1bn.mpgcat]
( 1) - 1bn.mpgcat + 2.mpgcat = 0
------------------------------------------------------------------------------
| Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
(1) | .3847812 .0984683 3.91 0.000 .1917869 .5777756
------------------------------------------------------------------------------
. glm foreign mpgcat price, link(identity) fam(binomial)
Iteration 0: log likelihood = -38.096154
Iteration 1: log likelihood = -37.994297
Iteration 2: log likelihood = -37.986895
Iteration 3: log likelihood = -37.986878
Iteration 4: log likelihood = -37.986878
Generalized linear models No. of obs = 74
Optimization : ML Residual df = 71
Scale parameter = 1
Deviance = 75.97375649 (1/df) Deviance = 1.070053
Pearson = 74 (1/df) Pearson = 1.042254
Variance function: V(u) = u*(1-u) [Bernoulli]
Link function : g(u) = u [Identity]
AIC = 1.107753
Log likelihood = -37.98687824 BIC = -229.6149
------------------------------------------------------------------------------
| OIM
foreign | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
mpgcat | .3954284 .0937052 4.22 0.000 .2117696 .5790872
price | .0000299 .0000153 1.95 0.051 -1.19e-07 .0000599
_cons | -.4888139 .1646307 -2.97 0.003 -.8114841 -.1661437
------------------------------------------------------------------------------
Coefficients are the risk differences.
Hope this helps, Tim
On Mon, Nov 22, 2010 at 11:16 AM, Astrid Kiil <[email protected]> wrote:
> Does anybody know how to compute the average treatment effect for the treated (ATT) of an explanatory variables included in a logistic regression in Stata?
>
> (I want to do so in order to obtain a regression estimate that is comparable to the ATT obtained by propensity score matching, performed by e.g. psmatch2 or nnmatch).
>
> Thanks for your consideration.
> / Astrid
>
>
>
> *
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>
*
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