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st: asclogit vs mixlogit
From
elisabetta capobianco <[email protected]>
To
[email protected]
Subject
st: asclogit vs mixlogit
Date
Tue, 4 Dec 2012 17:41:52 +0100
Dear Statalist,
I have done an asclogit and a mixlogit.
What does it means that mixlogit coefficients are bigger than asclogit ones?
And is there a command to evaluate the preference and have the
coefficient for a product that is both bio and origine and trasport?
Thank you for your help.
Elisabetta
. asclogit choice bio origine transport price, case(caseid)
alternatives (alt) nocons
note: 58 cases (232 obs) dropped due to no positive outcome per case
note: variable origine has 634 cases that are not
alternative-specific: there is no within-case variability
note: variable transport has 499 cases that are not
alternative-specific: there is no within-case variability
Iteration 0: log likelihood = -1877.1686
Iteration 1: log likelihood = -1839.1132
Iteration 2: log likelihood = -1835.7507
Iteration 3: log likelihood = -1835.7428
Iteration 4: log likelihood = -1835.7428
Alternative-specific conditional logit Number of obs = 8152
Case variable: caseid Number of cases = 2038
Alternative variable: alt Alts per case: min = 4
avg = 4.0
max = 4
Wald chi2(4) = 1301.46
Log likelihood = -1835.7428 Prob > chi2 = 0.0000
choice Coef. Std. Err. z P>z [95% Conf. Interval]
alt
bio 1.847154 .0646688 28.56 0.000 1.720406 1.973903
origine 1.370387 .0966343 14.18 0.000 1.180987 1.559787
transport .9962071 .072193 13.80 0.000 .8547113 1.137703
price -.1202996 .0101546 -11.85 0.000 -.1402023 -.100397
. mixlogit choice, rand( bio origine transport mprice ) group( scen)
id( id) ln(1)
Iteration 0: log likelihood = -1828.7774 (not concave)
Iteration 1: log likelihood = -1649.1054
Iteration 2: log likelihood = -1633.1579 (not concave)
Iteration 3: log likelihood = -1619.7809
Iteration 4: log likelihood = -1604.6355
Iteration 5: log likelihood = -1598.4351
Iteration 6: log likelihood = -1596.8481
Iteration 7: log likelihood = -1596.8413
Iteration 8: log likelihood = -1596.8413
Mixed logit model Number of obs = 8152
LR chi2(4) = 477.80
Log likelihood = -1596.8413 Prob > chi2 = 0.0000
choice Coef. Std. Err. z P>z [95% Conf. Interval]
Mean
bio 2.908033 .2187272 13.30 0.000 2.479336 3.336731
origine 1.745905 .1539406 11.34 0.000 1.444187 2.047623
transport 1.359802 .1314814 10.34 0.000 1.102103 1.6175
mprice -2.277098 .1584069 -14.37 0.000 -2.58757 -1.966627
SD
bio 2.307505 .1780501 12.96 0.000 1.958533 2.656477
origine .9528679 .1653407 5.76 0.000 .6288061 1.27693
transport 1.114283 .1307342 8.52 0.000 .8580486 1.370517
mprice .998506 .0878646 11.36 0.000 .8262945 1.170717
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