[Limdep Nlogit List] Conditional (posterior) class probabilities for panel ordered latent class model

[Richard Yao]

Dear Everyone,

Greetings from New Zealand!

I am currently running panel ordered latent logit class models on NLOGIT 6
and would like to calculate the conditional (posterior) class
probabilities.

I have used the NLOGIT code below and was able to estimate the panel latent
class model but unfortunately, I could not find the conditional (posterior)
class probabilities in the "CLASSP_I" MATRIX.

I would highly appreciate any thoughts/advice.

Best wishes,

Richard Yao, PhD
SCION, Private Bag 3020
Rotorua 3046, New Zealand
P: 64 7 343 5747;  M: 64 21 0247 3234

SAMPLE   ; All$
SETPANEL ; Group = pid ; Pds = csnum $

OLOGIT
; Lhs = rank_inv
; Rhs = one, broad_ar, targ_ar, targ_grd, remo_inf, ster_ins, effect
; list
; Partial effects $

OLOGIT
; Lhs = rank_inv
; Rhs = one, broad_ar, targ_ar, targ_grd, remo_inf, ster_ins, effect
; Partial effects
; LCM
; Pts = 3
; Parameters (Save posterior results)
; Panel $


|-> OLOGIT
    ; Lhs = rank_inv
    ; Rhs = one, broad_ar, targ_ar, targ_grd, remo_inf, ster_ins, effect
    ; Partial effects
    ; LCM
    ; Pts = 3
    ; Parameters (Save posterior results)
    ; List
    ; Panel $
Line search at iteration 68 does not improve the function
Exiting optimization

-----------------------------------------------------------------------------
Latent Class / Panel OrdProbs Model
Dependent variable             RANK_INV
Log likelihood function    -77528.46129
Estimation based on N =  63984, K =  35
Inf.Cr.AIC  = 155126.9 AIC/N =    2.424
Unbalanced panel has   1333 individuals
Latent class model with 3 latent classes
Ordered probability model
Ordered LOGIT probability model
LHS variable = values 0,1,..., 5
--------+--------------------------------------------------------------------
        |                  Standard            Prob.      95% Confidence
RANK_INV|  Coefficient       Error       z    |z|>Z*         Interval
--------+--------------------------------------------------------------------
        |Model parameters for latent class 1.............................
Constant|   -3.52320***      .23606   -14.93  .0000    -3.98586  -3.06053
BROAD_AR|    5.95097***      .12678    46.94  .0000     5.70248   6.19946
 TARG_AR|    6.92984***      .11222    61.75  .0000     6.70989   7.14979
TARG_GRD|    7.33345***      .10946    67.00  .0000     7.11891   7.54799
REMO_INF|    6.63923***      .11302    58.74  .0000     6.41772   6.86075
STER_INS|    6.33949***      .12028    52.71  .0000     6.10375   6.57523
  EFFECT|    5.53569***      .09779    56.61  .0000     5.34403   5.72734
  Mu(01)|    3.99266***      .23141    17.25  .0000     3.53910   4.44622
  Mu(02)|    5.43495***      .17091    31.80  .0000     5.09997   5.76994
  Mu(03)|    6.73958***      .20804    32.40  .0000     6.33183   7.14734
  Mu(04)|    8.21072***      .21450    38.28  .0000     7.79031   8.63114
        |Model parameters for latent class 2.............................
Constant|   -2.52192***      .29340    -8.60  .0000    -3.09697  -1.94688
BROAD_AR|    3.26588***      .08805    37.09  .0000     3.09332   3.43845
 TARG_AR|    7.12458***      .10202    69.84  .0000     6.92463   7.32453
TARG_GRD|    8.48640***      .09783    86.74  .0000     8.29465   8.67815
REMO_INF|    8.53274***      .09303    91.72  .0000     8.35039   8.71508
STER_INS|    8.39927***      .09673    86.83  .0000     8.20968   8.58886
  EFFECT|    4.11553***      .10494    39.22  .0000     3.90985   4.32122
  Mu(01)|    4.38768***      .31560    13.90  .0000     3.76912   5.00624
  Mu(02)|    6.49755***      .24170    26.88  .0000     6.02382   6.97128
  Mu(03)|    7.90108***      .28617    27.61  .0000     7.34020   8.46195
  Mu(04)|    9.34575***      .33196    28.15  .0000     8.69512   9.99638
        |Model parameters for latent class 3.............................
Constant|    1.08374***      .33059     3.28  .0010      .43581   1.73168
BROAD_AR|   -1.86830***      .06969   -26.81  .0000    -2.00489  -1.73171
 TARG_AR|     .13114**       .06415     2.04  .0409      .00541    .25687
TARG_GRD|    1.16636***      .06240    18.69  .0000     1.04407   1.28866
REMO_INF|    1.81549***      .06787    26.75  .0000     1.68247   1.94851
STER_INS|    1.05045***      .05081    20.67  .0000      .95086   1.15005
  EFFECT|    1.41528***      .08621    16.42  .0000     1.24631   1.58425
  Mu(01)|    1.24356***      .38216     3.25  .0011      .49453   1.99259
  Mu(02)|    2.20918***      .18116    12.19  .0000     1.85412   2.56424
  Mu(03)|    3.12403***      .38240     8.17  .0000     2.37453   3.87352
  Mu(04)|    4.21616***      .39565    10.66  .0000     3.44069   4.99163
        |Estimated prior probabilities for class membership..............
Class1Pr|     .61720***      .01381    44.68  .0000      .59012    .64427
Class2Pr|     .22585***      .01209    18.67  .0000      .20214    .24955
Class3Pr|     .15696***      .01012    15.51  .0000      .13713    .17679
--------+--------------------------------------------------------------------
***, **, * ==>  Significance at 1%, 5%, 10% level.
Model was estimated on Apr 21, 2021 at 10:07:48 AM
-----------------------------------------------------------------------------

RP cannot compute ME for ORDERED when J > 9..