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Re: st: decrease ll nested models sem
From
Volker Lang <[email protected]>
To
[email protected]
Subject
Re: st: decrease ll nested models sem
Date
Mon, 31 Mar 2014 15:23:23 +0200
Thanks Jeff Pitblado,
your feedback also helped me to solve my problems regarding model comparisons.
Best regards
Volker
Zitat von "Jeff Pitblado, StataCorp LP" <[email protected]>:
Volker Lang <[email protected]> is using -sem- and noticed that the
log-likelihood decreased when adding an exogenous variable.
I'm fitting a log-linear regression model using "sem".
(It shall later be extended to a path model with robust s.e.'s,
therefore "sem".)
Question:
Why does the log likelihood in comparison of the two nested models
estimated with "sem" decrease?
(It does not happen if I use a different estimation command, e.g.,
"sureg .. , isure".
Also, in this case coefficients and s.e.'s estimated by "sem" and
"sureg .. , isure" are identical,
so why does the log likelihood differ between these estimation
commands at all?)
Here is the output:
. sem (grade_l <- infov_l)
..
Iteration 1: log likelihood = -175.8425
Structural equation model Number of obs = 1281
Estimation method = ml
Log likelihood = -175.8425
..
. sem (grade_l <- infov_l ued_l)
..
Iteration 1: log likelihood = -513.0155
Structural equation model Number of obs = 1281
Estimation method = ml
Log likelihood = -513.0155
..
. sureg (grade_l infov_l), isure
Iteration 1: tolerance = 7.234e-17
..
. di e(ll)
-677.17747
. sureg (grade_l infov_l ued_l), isure
Iteration 1: tolerance = 1.447e-16
..
. di e(ll)
-656.64981
Notice that the reported log-likelhoods between the "equivalent" -sem- and
-sureg, isure- models do not agree.
Model 1: grade_l <- infov_l
sem: -175.8425
sureg, isure: -677.17747
Model 1: grade_l <- infov_l ued_l
sem: -513.0155
sureg, isure: -656.64981
This hints that -sem- is computing a difference log-likelihood from -sureg-.
In fact, -sem-'s log-likelihood is not conditional on the observed exogenous
variables while -sureg, isure-'s is.
If Volker intends to make direct comparisons between the reported
log-likelihoods from -sem- output, such as the -lrtest- command, then all
observed exogenous variables must be present in the fitted models; otherwise,
the likelihoods are not comparable.
Here is a silly example using the auto data. We will use -lrtest-
to test the
significance of the -trunk- coefficient.
. sysuse auto
# constrained model
. sem (mpg <- turn trunk@0)
. est store sem0
# full model
. sem (mpg <- turn trunk)
. est store sem1
. lrtest sem0 sem1
We can easily verify/validate the results from -lrtest- by using -regress-:
. regress mpg turn
. est store reg0
. regress mpg turn trunk
. est store reg1
. lrtest reg0 reg1
--Jeff
[email protected]
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