with currently how I applied weights, running a simple MNL model with only subset of the variables with v0.2.1 and 0.2.7 are shown below.
The MNL and nested logit model are based on the same dataset, but the nested logit model break the alternative 3 into three sub alternatives:3, 30 and 31.
running version 0.2.1 gave:
Model name : Apollo_try_1
Model description : Simple MNL model on mode choice
Model run at : 2022-08-29 17:36:09
Estimation method : bfgs
Model diagnosis : successful convergence
Number of individuals : 1222
Number of observations : 1222
Number of cores used : 1
Model without mixing
LL(start) : -1779.875
LL(0) : -113609.3
LL(final) : -59454.34
Rho-square (0) : 0.4767
Adj.Rho-square (0) : 0.4766
AIC : 118932.7
BIC : 118994
Estimated parameters : 12
Time taken (hh:mm:ss) : 00:00:12.39
pre-estimation : 00:00:0.48
estimation : 00:00:5.68
post-estimation : 00:00:6.24
Iterations : 29
Min abs eigenvalue of Hessian : 14.26168
Estimates:
Estimate s.e. t.rat.(0) p(1-sided) Rob.s.e. Rob.t.rat.(0) p(1-sided)
asc_4 -3.30300 0.119217 -27.706 0.000 NaN NaN NaN
asc_0 -4.36605 0.119418 -36.561 0.000 NaN NaN NaN
asc_3&30&31 -0.80870 0.138610 -5.834 2.700e-09 NaN NaN NaN
asc_2 -3.10955 0.156354 -19.888 0.000 NaN NaN NaN
asc_1 0.00000 NA NA NA NA NA NA
b_tt_1 -0.27468 0.004899 -56.067 0.000 NaN NaN NaN
b_tt_0 -0.10238 0.002996 -34.177 0.000 NaN NaN NaN
b_tt_3&30&31 -0.34850 0.003397 -102.577 0.000 NaN NaN NaN
b_tt_4 -0.17065 0.009093 -18.766 0.000 NaN NaN NaN
b_tt_2 -0.12850 0.001374 -93.536 0.000 NaN NaN NaN
b_c -0.25201 0.014738 -17.099 0.000 NaN NaN NaN
b_acc_t_o -0.12103 0.003042 -39.781 0.000 NaN NaN NaN
b_acc_t_d -0.06632 0.003518 -18.850 0.000 NaN NaN NaN
running Version 0.2.7 gave me:
Model name : Apollo_try_1
Model description : Simple MNL model on mode choice
Model run at : 2022-08-29 17:22:47
Estimation method : bfgs
Model diagnosis : successful convergence
Number of individuals : 1222
Number of rows in database : 1222
Number of modelled outcomes : 1222
Number of cores used : 1
Model without mixing
LL(start) : -1779.88
LL(0) : -113609.3
LL(C) : Not applicable
LL(final) : -59454.13
Rho-square (0) : 0.4767
Adj.Rho-square (0) : 0.4766
AIC : 118932.3
BIC : 118993.6
Estimated parameters : 12
Time taken (hh:mm:ss) : 00:00:4.11
pre-estimation : 00:00:0.45
estimation : 00:00:2.29
post-estimation : 00:00:1.37
Iterations : 35
Unconstrained optimisation.
Estimates:
Estimate s.e. t.rat.(0) p(1-sided) Rob.s.e. Rob.t.rat.(0) p(1-sided)
asc_4 -3.30304 NA NA NA NA NA NA
asc_0 -4.36604 NA NA NA NA NA NA
asc_3&30&31 -0.80870 NA NA NA NA NA NA
asc_2 -3.10954 NA NA NA NA NA NA
asc_1 0.00000 NA NA NA NA NA NA
b_tt_1 -0.27468 NA NA NA NA NA NA
b_tt_0 -0.10238 NA NA NA NA NA NA
b_tt_3&30&31 -0.34850 NA NA NA NA NA NA
b_tt_4 -0.17065 NA NA NA NA NA NA
b_tt_2 -0.12850 NA NA NA NA NA NA
b_c -0.25201 NA NA NA NA NA NA
b_acc_t_o -0.12103 NA NA NA NA NA NA
b_acc_t_d -0.06632 NA NA NA NA NA NA
what I am hoping to do:
I asked a question about applying weights in prediction (
http://www.apollochoicemodelling.com/fo ... f=16&t=386). 0.2.7 was recommended to solve the problem I asked, but v0.2.7 gave me estimation result with NA s.e., t statistics and p values ( as shown above).
Thank you for your help!