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st: asclogit vs mixlogit


From   elisabetta capobianco <elisabetta.capobianco@gmail.com>
To   statalist@hsphsun2.harvard.edu
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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