[Limdep Nlogit List] Modelling attribute non-attendance (ANA) in NLOGIT 6
Richard Yao
rickyyao at gmail.com
Fri May 1 07:21:36 AEST 2026
Dear members,
I hope you are all doing well.
I am writing to seek your advice on modelling attribute non-attendance
(ANA) in NLOGIT 6.
I have included my code and output below. While the code appears to run
correctly, the results seem unusual—the estimated coefficients are
identical across all four latent classes.
I have previously implemented ANA models for an ERE paper using an earlier
version of NLOGIT, where the results behaved as expected.
I would greatly appreciate any guidance you may be able to provide.
Many thanks in advance.
Best regards,
Richard
N = 509
|-> LOAD;file="C:\Users\yaor\CE_LCM_Data_8Apr2026_n_509.lpj"$
Project file contained 8958 observations.
|-> NLOGIT
;lhs = chosen,cset,altij
;choices=sq,a,b
;maxit=200 ? panel's length & Max. num. of iterations
;lcm = a_sbr_1, a_uni_hi, a_100abo, a1_comn, a3_drun, a7_worn, p1_lat,
s4_ind, i3_bre
;pts = 4 ? specifies it is a LCM and the number of classes
;pds=times ? specifies the log-likelihood with the product
operator,taking the product of the probabilities from 6 choice sets
;rst=
beh1,beh2,bmn1,bmn2,bpol1,bpol2,bcost, baltd, ? Full attendance
beh1,beh2,0, 0, bpol1,bpol2,0 , 0 , ? Ignoring boat
efficiency, cost, & baltd
0 ,0 ,bmn1,0 ,0 ,0 ,bcost, baltd, ? Ignoring beh1, beh2,
bmn2, bpol1, bpol2
0 ,0 ,0 ,0 ,0 ,0 ,0 , 0 ? Ignoring all attributes
;parameters ? estimate individual specific parameters
; Check Data
;model:
U(sq)= beh1*e_h1 + beh2*e_h2 + bmn1*mn1 + bmn2*mn2 +
bpol1*pol1 + bpol2*pol2 + bcost*cost /
U(a) = b_altd*alt_d + beh1*e_h1 + beh2*e_h2 + bmn1*mn1 + bmn2*mn2 +
bpol1*pol1 + bpol2*pol2 + bcost*cost /
U(b) = b_altd*alt_d + beh1*e_h1 + beh2*e_h2 + bmn1*mn1 + bmn2*mn2 +
bpol1*pol1 + bpol2*pol2 + bcost*cost $
+----------------------------------------------------------+
| Inspecting the data set before estimation. |
| These errors mark observations which will be skipped. |
| Row Individual = 1st row then group number of data block |
+----------------------------------------------------------+
No bad observations were found in the sample
Iterative procedure has converged
Normal exit: 6 iterations. Status=0, F= .2438144D+04
-----------------------------------------------------------------------------
Discrete choice (multinomial logit) model
Dependent variable Choice
Log likelihood function -2438.14357
Estimation based on N = 2986, K = 8
Inf.Cr.AIC = 4892.3 AIC/N = 1.638
---------------------------------------
Log likelihood R-sqrd R2Adj
ASCs only model must be fit separately
Use NLOGIT ;...;RHS=ONE$
Note: R-sqrd = 1 - logL/Logl(constants)
Warning: Model does not contain a full
set of ASCs. R-sqrd is problematic. Use
model setup with ;RHS=one to get LogL0.
Root Likelihood:Geom. Mean of P^ .4420
---------------------------------------
Response data are given as ind. choices
Number of obs.= 2986, skipped 0 obs
--------+--------------------------------------------------------------------
| Standard Prob. 95% Confidence
CHOSEN| Coefficient Error z |z|>Z* Interval
--------+--------------------------------------------------------------------
BEH1|1| 1.10202*** .08669 12.71 .0000 .93211 1.27193
BEH2|1| 1.30316*** .06514 20.01 .0000 1.17549 1.43082
BMN1|1| .79582*** .08215 9.69 .0000 .63481 .95682
BMN2|1| .35273*** .06870 5.13 .0000 .21808 .48737
BPOL1|1| .61010*** .09302 6.56 .0000 .42779 .79241
BPOL2|1| .47487*** .06612 7.18 .0000 .34528 .60445
BCOST|1| -.00079*** .00012 -6.43 .0000 -.00103 -.00055
B_ALTD|1| .05026 .10817 .46 .6422 -.16176 .26228
--------+--------------------------------------------------------------------
***, **, * ==> Significance at 1%, 5%, 10% level.
Model was estimated on Apr 10, 2026 at 05:47:01 PM
-----------------------------------------------------------------------------
Iterative procedure has converged
Normal exit: 49 iterations. Status=0, F= .2121261D+04
-----------------------------------------------------------------------------
Latent Class Logit Model
Dependent variable CHOSEN
Log likelihood function -2121.26079
Restricted log likelihood -3280.45629
Chi squared [ 38](P= .000) 2318.39102
Significance level .00000
McFadden Pseudo R-squared .3533641
Estimation based on N = 2986, K = 38
Inf.Cr.AIC = 4318.5 AIC/N = 1.446
---------------------------------------
Log likelihood R-sqrd R2Adj
No coefficients -3280.4563 .3534 .3492
Constants only can be computed directly
Use NLOGIT ;...;RHS=ONE$
At start values -2460.3355 .1378 .1323
Note: R-sqrd = 1 - logL/Logl(constants)
Root Likelihood:Geom. Mean of P^ .4914
Warning: Model does not contain a full
set of ASCs. R-sqrd is problematic. Use
model setup with ;RHS=one to get LogL0.
---------------------------------------
Response data are given as ind. choices
Number of latent classes = 4
Average Class Probabilities
.282 .400 .150 .168
LCM model with panel has 509 groups
Variable number of obs./group =TIMES
Number of obs.= 2986, skipped 0 obs
--------+--------------------------------------------------------------------
| Standard Prob. 95% Confidence
CHOSEN| Coefficient Error z |z|>Z* Interval
--------+--------------------------------------------------------------------
|Random utility parameters in latent class -->> 1...............
BEH1|1| 1.92247*** .13203 14.56 .0000 1.66369 2.18125
BEH2|1| 2.54316*** .11962 21.26 .0000 2.30872 2.77761
BMN1|1| 1.37009*** .19790 6.92 .0000 .98221 1.75797
BMN2|1| 1.52454*** .19107 7.98 .0000 1.15005 1.89903
BPOL1|1| 1.38487*** .15370 9.01 .0000 1.08363 1.68611
BPOL2|1| 1.15980*** .10383 11.17 .0000 .95629 1.36331
BCOST|1| -.00570*** .00074 -7.71 .0000 -.00715 -.00425
B_ALTD|1| 2.56508*** .41237 6.22 .0000 1.75686 3.37331
|Random utility parameters in latent class -->> 2...............
BEH1|2| 1.92247*** .13203 14.56 .0000 1.66369 2.18125
BEH2|2| 2.54316*** .11962 21.26 .0000 2.30872 2.77761
BMN1|2| 0.0 .....(Fixed Parameter).....
BMN2|2| 0.0 .....(Fixed Parameter).....
BPOL1|2| 1.38487*** .15370 9.01 .0000 1.08363 1.68611
BPOL2|2| 1.15980*** .10383 11.17 .0000 .95629 1.36331
BCOST|2| 0.0 .....(Fixed Parameter).....
B_ALTD|2| 0.0 .....(Fixed Parameter).....
|Random utility parameters in latent class -->> 3...............
BEH1|3| 0.0 .....(Fixed Parameter).....
BEH2|3| 0.0 .....(Fixed Parameter).....
BMN1|3| 1.37009*** .19790 6.92 .0000 .98221 1.75797
BMN2|3| 0.0 .....(Fixed Parameter).....
BPOL1|3| 0.0 .....(Fixed Parameter).....
BPOL2|3| 0.0 .....(Fixed Parameter).....
BCOST|3| -.00570*** .00074 -7.71 .0000 -.00715 -.00425
B_ALTD|3| 2.56508*** .41237 6.22 .0000 1.75686 3.37331
|Random utility parameters in latent class -->> 4...............
BEH1|4| 0.0 .....(Fixed Parameter).....
BEH2|4| 0.0 .....(Fixed Parameter).....
BMN1|4| 0.0 .....(Fixed Parameter).....
BMN2|4| 0.0 .....(Fixed Parameter).....
BPOL1|4| 0.0 .....(Fixed Parameter).....
BPOL2|4| 0.0 .....(Fixed Parameter).....
BCOST|4| 0.0 .....(Fixed Parameter).....
B_ALTD|4| 0.0 .....(Fixed Parameter).....
|This is THETA(01) in class probability model....................
_ONE|1| -1.46308 1.59698 -.92 .3596 -4.59310 1.66694
_A_SBR|1| .33726 .41054 .82 .4114 -.46739 1.14191
_A_UNI|1| .30410 .38328 .79 .4275 -.44713 1.05532
_A_100|1| .57732 .38364 1.50 .1324 -.17459 1.32924
_A1_CO|1| 1.16015 .98067 1.18 .2368 -.76193 3.08224
_A3_DR|1| -1.43785* .81865 -1.76 .0790 -3.04237 .16667
_A7_WO|1| .79912 1.25996 .63 .5259 -1.67036 3.26860
_P1_LA|1| -.72897 .92499 -.79 .4307 -2.54192 1.08399
_S4_IN|1| .86017 .89804 .96 .3382 -.89997 2.62030
_I3_BR|1| 1.07985 1.33400 .81 .4182 -1.53474 3.69445
|This is THETA(02) in class probability model....................
_ONE|2| -5.12052** 2.08257 -2.46 .0139 -9.20228 -1.03875
_A_SBR|2| .76384 .48705 1.57 .1168 -.19075 1.71844
_A_UNI|2| .60173 .44629 1.35 .1776 -.27297 1.47644
_A_100|2| .52510 .45145 1.16 .2448 -.35972 1.40992
_A1_CO|2| 1.28256 1.11903 1.15 .2517 -.91069 3.47581
_A3_DR|2| -2.90244*** 1.05502 -2.75 .0059 -4.97024 -.83464
_A7_WO|2| .96414 1.58739 .61 .5436 -2.14709 4.07537
_P1_LA|2| -2.39047** 1.01637 -2.35 .0187 -4.38252 -.39842
_S4_IN|2| 1.17789 1.05349 1.12 .2635 -.88690 3.24269
_I3_BR|2| 6.66912*** 1.86589 3.57 .0004 3.01204 10.32620
|This is THETA(03) in class probability model....................
_ONE|3| -1.69662 2.22571 -.76 .4459 -6.05893 2.66569
_A_SBR|3| 1.25179* .70877 1.77 .0774 -.13736 2.64095
_A_UNI|3| .39719 .49943 .80 .4264 -.58167 1.37604
_A_100|3| -.03146 .53379 -.06 .9530 -1.07768 1.01475
_A1_CO|3| -.24277 1.32231 -.18 .8543 -2.83445 2.34892
_A3_DR|3| -.89400 1.05243 -.85 .3956 -2.95673 1.16872
_A7_WO|3| 2.82413 1.85429 1.52 .1278 -.81021 6.45848
_P1_LA|3| -1.28405 1.20520 -1.07 .2867 -3.64619 1.07809
_S4_IN|3| -.14264 1.11107 -.13 .8978 -2.32030 2.03503
_I3_BR|3| -.06089 1.83862 -.03 .9736 -3.66452 3.54274
|This is THETA(04) in class probability model....................
_ONE|4| 0.0 .....(Fixed Parameter).....
_A_SBR|4| 0.0 .....(Fixed Parameter).....
_A_UNI|4| 0.0 .....(Fixed Parameter).....
_A_100|4| 0.0 .....(Fixed Parameter).....
_A1_CO|4| 0.0 .....(Fixed Parameter).....
_A3_DR|4| 0.0 .....(Fixed Parameter).....
_A7_WO|4| 0.0 .....(Fixed Parameter).....
_P1_LA|4| 0.0 .....(Fixed Parameter).....
_S4_IN|4| 0.0 .....(Fixed Parameter).....
_I3_BR|4| 0.0 .....(Fixed Parameter).....
--------+--------------------------------------------------------------------
***, **, * ==> Significance at 1%, 5%, 10% level.
Fixed parameter ... is constrained to equal the value or
had a nonpositive st.error because of an earlier problem.
Model was estimated on Apr 10, 2026 at 05:47:07 PM
-----------------------------------------------------------------------------
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