Dear all,
I ran a multinomial logit with constraints and after that I used the command
"predict" to get the predicted probabilities. Stata result was: same
probabilities for 3 alternatives. Do you know any other command to predict the
probabilities when mlogit with constraints is used?
I apologize for messing up the archives.
Hoping to get helped now,
Best
mlogit av20 time cost1 if (location>1|location<6|location>6)&purpose==1,
cons(1-3) basecat(1)
Iteration 0: log likelihood = -479.42809
Iteration 1: log likelihood = -259.55111
Iteration 2: log likelihood = -256.78928
Iteration 3: log likelihood = -256.74805
Iteration 4: log likelihood = -256.74804
Multinomial logistic regression Number of obs = 324
LR chi2(-1) = 445.36
Prob > chi2 = .
Log likelihood = -256.74804 Pseudo R2 = 0.4645
( 1) [2]time - [3]time = 0
( 2) [2]cost1 - [3]cost1 = 0
( 3) [2]_cons - [3]_cons = 0
( 4) [3]time - [5]time = 0
( 5) [3]cost1 - [5]cost1 = 0
( 6) [3]_cons - [5]_cons = 0
( 7) [4]time - [5]time = 0
( 8) [4]cost1 - [5]cost1 = 0
( 9) [4]_cons - [5]_cons = 0
------------------------------------------------------------------------------
av20 | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
2 |
time | -.015983 .0048032 -3.33 0.001 -.0253971 -.006569
cost1 | -.0072383 .0013861 -5.22 0.000 -.009955 -.0045216
_cons | (dropped)
-------------+----------------------------------------------------------------
3 |
time | -.015983 .0048032 -3.33 0.001 -.0253971 -.006569
cost1 | -.0072383 .0013861 -5.22 0.000 -.009955 -.0045216
_cons | (dropped)
-------------+----------------------------------------------------------------
4 |
time | -.015983 .0048032 -3.33 0.001 -.0253971 -.006569
cost1 | -.0072383 .0013861 -5.22 0.000 -.009955 -.0045216
_cons | (dropped)
-------------+----------------------------------------------------------------
5 |
time | -.015983 .0048032 -3.33 0.001 -.0253971 -.006569
cost1 | -.0072383 .0013861 -5.22 0.000 -.009955 -.0045216
_cons | -.9755993 .3746739 -2.60 0.009 -1.709947 -.241252
------------------------------------------------------------------------------
(av20==1 is the base outcome)
. predict p1 p2 p3 p4 p5 if e(sample)
(option pr assumed; predicted probabilities)
(69 missing values generated)
. sum p1-p5
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------------
p1 | 324 .7788082 .0914454 .5147507 .9966482
p2 | 324 .0655002 .0270792 .0009926 .1436939
p3 | 324 .0655002 .0270792 .0009926 .1436939
p4 | 324 .0655002 .0270792 .0009926 .1436939
p5 | 324 .0246914 .0102079 .0003742 .0541678
----------------------------------------------------------------
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