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Re-re-post: Stata 11 - Factor variables in a regression command
From
Ricardo Basurto <[email protected]>
To
statalist <[email protected]>
Subject
Re-re-post: Stata 11 - Factor variables in a regression command
Date
Sat, 1 May 2010 01:48:42 -0400
Not the best way to start posting to StataList, is it? I am
re-arranging my message hoping that at least that way my question
won't be cut out. (If anyone has suggestions on how to successfully
submit messages from within Gmail, I would appreciate those as well.)
--------------------------------------------------------------------------------------------------------------------------------------------------------
I am having trouble understanding the difference between a regression
that uses a cross operator (#) and one that uses a cross factorial
operator (##).
For example, below is the output I get from running two different
regressions. From the log-likelihood ratio, chi2, etc, it seems clear
to me that both commands are fitting the same regression model. Also,
I can reproduce the second regression by fitting a regression with
dummies for a=1, b=1, and a variable equal to the multiplication of
those two dummies; however, I just can't figure out what exact model
is being fitted in the first regression. Can anyone explain this?
Thank you,
Ricardo
REGRESSION #1:
. logistic y a#b
Logistic regression Number of obs = 19670
LR chi2(3) = 7.71
Prob > chi2 = 0.0525
Log likelihood = -1473.1898 Pseudo R2 = 0.0026
----------------------------------------------------------------------------
y | Odds Ratio Std. Err. z P>|z| [95% Conf. Int.]
-----------+----------------------------------------------------------------
a#b |
0 1 | 1.567419 .2804138 2.51 0.012 1.1038 2.2256
1 0 | 1.447424 .2588797 2.07 0.039 1.0194 2.0551
1 1 | 1.211988 .2246236 1.04 0.300 .84283 1.7428
----------------------------------------------------------------------------
REGRESSION #2
. logistic y a##b
Logistic regression Number of obs = 19670
LR chi2(3) = 7.71
Prob > chi2 = 0.0525
Log likelihood = -1473.1898 Pseudo R2 = 0.0026
----------------------------------------------------------------------------
y | Odds Ratio Std. Err. z P>|z| [95% Conf. Int.]
-----------+----------------------------------------------------------------
1.a | 1.447424 .2588797 2.07 0.039 1.0194 2.0551
1.b | 1.567419 .2804138 2.51 0.012 1.1038 2.2256
|
a#b |
1 1 | .5342167 .1302597 -2.57 0.010 .33125 .86152
----------------------------------------------------------------------------
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