Since your dependent var is not confined between 0 and 1, like in a binary var, your log likelihood is positive. So, this happens when you have a continuous dependent var.
Mansour
>>> "Kieran McCaul" <[email protected]> 10/17/07 10:12 AM >>>
Hi everyone,
I'm running some mixed models of growth in aortic diameter over time.
I've run a series of models similar to this one, where a_size is the
diameter in mm. I've also repeated all of these models, using the log
of a_size as the dependent variable.
gen ln_size = ln(a_size)
Examples of two such models are shown below.
My question is, why am I getting positive log likelihoods for the second
model that uses ln_size as the dependent variable?
I'm getting positive log likelihoods for all of the models that I run on
ln_size.
. xi:xtmixed a_size i.opg_1181gc*years || id: years , cov(unstruct)
mle var
i.opg_1181gc _Iopg_1181g_1-3 (naturally coded; _Iopg_1181g_1
omitted)
i.opg_1~c*years _IopgXyears_# (coded as above)
Performing EM optimization:
Performing gradient-based optimization:
Iteration 0: log likelihood = -7914.8871
Iteration 1: log likelihood = -7914.8867
Computing standard errors:
Mixed-effects ML regression Number of obs =
3557
Group variable: id Number of groups =
627
Obs per group: min =
2
avg =
5.7
max =
14
Wald chi2(5) =
640.10
Log likelihood = -7914.8867 Prob > chi2 =
0.0000
------------------------------------------------------------------------
------
a_size | Coef. Std. Err. z P>|z| [95% Conf.
Interval]
-------------+----------------------------------------------------------
------
_Iopg_1181~2 | -.0194946 .5571894 -0.03 0.972 -1.111566
1.072577
_Iopg_1181~3 | .7094095 .5990142 1.18 0.236 -.4646367
1.883456
years | 1.402274 .1243886 11.27 0.000 1.158477
1.646071
_IopgXyear~2 | -.0719107 .1479563 -0.49 0.627 -.3618998
.2180784
_IopgXyear~3 | .1316498 .1595169 0.83 0.409 -.1809976
.4442971
_cons | 34.729 .4675183 74.28 0.000 33.81268
35.64531
------------------------------------------------------------------------
------
------------------------------------------------------------------------
------
Random-effects Parameters | Estimate Std. Err. [95% Conf.
Interval]
-----------------------------+------------------------------------------
------
id: Unstructured |
var(years) | 1.669906 .132318 1.429702
1.950467
var(_cons) | 26.87369 1.577405 23.95325
30.15021
cov(years,_cons) | 3.654421 .3432374 2.981688
4.327154
-----------------------------+------------------------------------------
------
var(Residual) | 1.519602 .0462945 1.431522
1.613101
------------------------------------------------------------------------
------
LR test vs. linear regression: chi2(3) = 7130.28 Prob > chi2 =
0.0000
Note: LR test is conservative and provided only for reference.
. xi:xtmixed ln_size i.opg_1181gc*years || id: years , cov(unstruct)
mle var
i.opg_1181gc _Iopg_1181g_1-3 (naturally coded; _Iopg_1181g_1
omitted)
i.opg_1~c*years _IopgXyears_# (coded as above)
Performing EM optimization:
Performing gradient-based optimization:
Iteration 0: log likelihood = 5058.5355
Iteration 1: log likelihood = 5058.5375
Iteration 2: log likelihood = 5058.5375
Computing standard errors:
Mixed-effects ML regression Number of obs =
3557
Group variable: id Number of groups =
627
Obs per group: min =
2
avg =
5.7
max =
14
Wald chi2(5) =
657.50
Log likelihood = 5058.5375 Prob > chi2 =
0.0000
------------------------------------------------------------------------
------
ln_size | Coef. Std. Err. z P>|z| [95% Conf.
Interval]
-------------+----------------------------------------------------------
------
_Iopg_1181~2 | -.0003805 .0146312 -0.03 0.979 -.0290571
.0282961
_Iopg_1181~3 | .0189981 .0157294 1.21 0.227 -.011831
.0498272
years | .0345352 .0030077 11.48 0.000 .0286403
.0404301
_IopgXyear~2 | -.0016794 .0035764 -0.47 0.639 -.0086891
.0053302
_IopgXyear~3 | .0025355 .0038578 0.66 0.511 -.0050257
.0100968
_cons | 3.539662 .0122765 288.33 0.000 3.515601
3.563724
------------------------------------------------------------------------
------
------------------------------------------------------------------------
------
Random-effects Parameters | Estimate Std. Err. [95% Conf.
Interval]
-----------------------------+------------------------------------------
------
id: Unstructured |
var(years) | .000947 .0000819 .0007994
.0011218
var(_cons) | .0185358 .0010865 .0165241
.0207924
cov(years,_cons) | .0017135 .0002094 .0013031
.0021239
-----------------------------+------------------------------------------
------
var(Residual) | .0010365 .0000323 .0009751
.0011019
------------------------------------------------------------------------
------
LR test vs. linear regression: chi2(3) = 6880.49 Prob > chi2 =
0.0000
Note: LR test is conservative and provided only for reference.
______________________________________________
Kieran McCaul MPH PhD
WA Centre for Health & Ageing (M573)
University of Western Australia
Level 6, Ainslie House
48 Murray St
Perth 6000
email: [email protected]
http://myprofile.cos.com/mccaul
_______________________________________________
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