Optimising panel mixed logit design_Syntax errors

[neeraj85]

Hi,

I've been trying to optimise panel mixed logit design using the MNL design. The design comprises of 3 alternatives (2 hypothetical and 1 status quo)

Following are the steps carried out by me:
1. Generated a design using MNL. This created a design with a name d347.ngd
2. Evaluated the efficiency of d347.ngd for panel mixed logit

ISSUE: The execution terminates suddenly after few minutes and throws a message to contact choice metrics.

Following is the script whose run is ending abruptly:

;alts(Cat7) = alt1*, alt2*, alt3*
;alts(Cat8) = alt1*, alt2*, alt3*
;alts(Cat9) = alt1*, alt2*, alt3*
;alts(Cat10) = alt1*, alt2*, alt3*
;alts(Cat11) = alt1*, alt2*, alt3*
;alts(Cat12) = alt1*, alt2*, alt3*

;alg = eval(d347.ngd)
;rows = 10
;eff = fish2(rpecpanel,d,mean)
;rdraws = gauss(3)
;bdraws = gauss(3)
;rep = 1000
;fisher(fish2) = design1(Cat7[0.191], Cat8[0.163], Cat9[0.207], Cat10[0.289], Cat11[0.134], Cat12[0.016])

;model(Cat7):
U(alt1) = b90[n,(n,-0.2746,0.0544),(n,0.1923,0.0526)] * tt.ref[7.5] + b91[n,(n,-0.1228,0.0379),(n,0.1843,0.0449)] * tts.ref[2] + b92[n,(n,-0.1382,0.0654),(n,0.2310,0.0770)] * sn.ref[5] + b93[(n,-1.5784,0.3706)] * vr.ref[0.85] + s1[ec,0.2]/
U(alt2) = b90[n,(n,-0.2746,0.0544),(n,0.1923,0.0526)] * tt.piv[-20%, -10%, 0%, 10%, 20%] + b91[n,(n,-0.1228,0.0379),(n,0.1843,0.0449)] * tts.piv[-50%, -25%, 0%, 25%, 50%] + b92[n,(n,-0.1382,0.0654),(n,0.2310,0.0770)] * sn.piv[-50%, -25%, 0%, 25%, 50%] + b93[(n,-1.5784,0.3706)] * vr.piv[-25%, -12.5%, 0%, 12.5%, 25%] + s1 + s2[ec,0.5]/
U(alt3) = b90[n,(n,-0.2746,0.0544),(n,0.1923,0.0526)] * tt.piv[-20%, -10%, 0%, 10%, 20%] + b91[n,(n,-0.1228,0.0379),(n,0.1843,0.0449)] * tts.piv[-50%, -25%, 0%, 25%, 50%] + b92[n,(n,-0.1382,0.0654),(n,0.2310,0.0770)] * sn.piv[-50%, -25%, 0%, 25%, 50%] + b93[(n,-1.5784,0.3706)] * vr.piv[-25%, -12.5%, 0%, 12.5%, 25%] + s2

;model(Cat8):
U(alt1) = b90[n,(n,-0.2746,0.0544),(n,0.1923,0.0526)] * tt.ref[22.5] + b91[n,(n,-0.1228,0.0379),(n,0.1843,0.0449)] * tts.ref[6] + b92[n,(n,-0.1382,0.0654),(n,0.2310,0.0770)] * sn.ref[12] + b93[(n,-1.5784,0.3706)] * vr.ref[2.45] + s1[ec,0.2]/
U(alt2) = b90[n,(n,-0.2746,0.0544),(n,0.1923,0.0526)] * tt.piv[-20%, -10%, 0%, 10%, 20%] + b91[n,(n,-0.1228,0.0379),(n,0.1843,0.0449)] * tts.piv[-50%, -25%, 0%, 25%, 50%] + b92[n,(n,-0.1382,0.0654),(n,0.2310,0.0770)] * sn.piv[-50%, -25%, 0%, 25%, 50%] + b93[(n,-1.5784,0.3706)] * vr.piv[-25%, -12.5%, 0%, 12.5%, 25%] + s1 + s2[ec,0.5]/
U(alt3) = b90[n,(n,-0.2746,0.0544),(n,0.1923,0.0526)] * tt.piv[-20%, -10%, 0%, 10%, 20%] + b91[n,(n,-0.1228,0.0379),(n,0.1843,0.0449)] * tts.piv[-50%, -25%, 0%, 25%, 50%] + b92[n,(n,-0.1382,0.0654),(n,0.2310,0.0770)] * sn.piv[-50%, -25%, 0%, 25%, 50%] + b93[(n,-1.5784,0.3706)] * vr.piv[-25%, -12.5%, 0%, 12.5%, 25%] + s2

;model(Cat9):
U(alt1) = b90[n,(n,-0.2746,0.0544),(n,0.1923,0.0526)] * tt.ref[45] + b91[n,(n,-0.1228,0.0379),(n,0.1843,0.0449)] * tts.ref[11] + b92[n,(n,-0.1382,0.0654),(n,0.2310,0.0770)] * sn.ref[20] + b93[(n,-1.5784,0.3706)] * vr.ref[5.3] + s1[ec,0.2]/
U(alt2) = b90[n,(n,-0.2746,0.0544),(n,0.1923,0.0526)] * tt.piv[-20%, -10%, 0%, 10%, 20%] + b91[n,(n,-0.1228,0.0379),(n,0.1843,0.0449)] * tts.piv[-50%, -25%, 0%, 25%, 50%] + b92[n,(n,-0.1382,0.0654),(n,0.2310,0.0770)] * sn.piv[-50%, -25%, 0%, 25%, 50%] + b93[(n,-1.5784,0.3706)] * vr.piv[-25%, -12.5%, 0%, 12.5%, 25%] + s1 + s2[ec,0.5]/
U(alt3) = b90[n,(n,-0.2746,0.0544),(n,0.1923,0.0526)] * tt.piv[-20%, -10%, 0%, 10%, 20%] + b91[n,(n,-0.1228,0.0379),(n,0.1843,0.0449)] * tts.piv[-50%, -25%, 0%, 25%, 50%] + b92[n,(n,-0.1382,0.0654),(n,0.2310,0.0770)] * sn.piv[-50%, -25%, 0%, 25%, 50%] + b93[(n,-1.5784,0.3706)] * vr.piv[-25%, -12.5%, 0%, 12.5%, 25%] + s2

;model(Cat10):
U(alt1) = b90[n,(n,-0.2746,0.0544),(n,0.1923,0.0526)] * tt.ref[75] + b91[n,(n,-0.1228,0.0379),(n,0.1843,0.0449)] * tts.ref[19] + b92[n,(n,-0.1382,0.0654),(n,0.2310,0.0770)] * sn.ref[30] + b93[(n,-1.5784,0.3706)] * vr.ref[12.0] + s1[ec,0.2]/
U(alt2) = b90[n,(n,-0.2746,0.0544),(n,0.1923,0.0526)] * tt.piv[-20%, -10%, 0%, 10%, 20%] + b91[n,(n,-0.1228,0.0379),(n,0.1843,0.0449)] * tts.piv[-50%, -25%, 0%, 25%, 50%] + b92[n,(n,-0.1382,0.0654),(n,0.2310,0.0770)] * sn.piv[-50%, -25%, 0%, 25%, 50%] + b93[(n,-1.5784,0.3706)] * vr.piv[-25%, -12.5%, 0%, 12.5%, 25%] + s1 + s2[ec,0.5]/
U(alt3) = b90[n,(n,-0.2746,0.0544),(n,0.1923,0.0526)] * tt.piv[-20%, -10%, 0%, 10%, 20%] + b91[n,(n,-0.1228,0.0379),(n,0.1843,0.0449)] * tts.piv[-50%, -25%, 0%, 25%, 50%] + b92[n,(n,-0.1382,0.0654),(n,0.2310,0.0770)] * sn.piv[-50%, -25%, 0%, 25%, 50%] + b93[(n,-1.5784,0.3706)] * vr.piv[-25%, -12.5%, 0%, 12.5%, 25%] + s2

;model(Cat11):
U(alt1) = b90[n,(n,-0.2746,0.0544),(n,0.1923,0.0526)] * tt.ref[105] + b91[n,(n,-0.1228,0.0379),(n,0.1843,0.0449)] * tts.ref[26] + b92[n,(n,-0.1382,0.0654),(n,0.2310,0.0770)] * sn.ref[42] + b93[(n,-1.5784,0.3706)] * vr.ref[19.3] + s1[ec,0.2]/
U(alt2) = b90[n,(n,-0.2746,0.0544),(n,0.1923,0.0526)] * tt.piv[-20%, -10%, 0%, 10%, 20%] + b91[n,(n,-0.1228,0.0379),(n,0.1843,0.0449)] * tts.piv[-50%, -25%, 0%, 25%, 50%] + b92[n,(n,-0.1382,0.0654),(n,0.2310,0.0770)] * sn.piv[-50%, -25%, 0%, 25%, 50%] + b93[(n,-1.5784,0.3706)] * vr.piv[-25%, -12.5%, 0%, 12.5%, 25%] + s1 + s2[ec,0.5]/
U(alt3) = b90[n,(n,-0.2746,0.0544),(n,0.1923,0.0526)] * tt.piv[-20%, -10%, 0%, 10%, 20%] + b91[n,(n,-0.1228,0.0379),(n,0.1843,0.0449)] * tts.piv[-50%, -25%, 0%, 25%, 50%] + b92[n,(n,-0.1382,0.0654),(n,0.2310,0.0770)] * sn.piv[-50%, -25%, 0%, 25%, 50%] + b93[(n,-1.5784,0.3706)] * vr.piv[-25%, -12.5%, 0%, 12.5%, 25%] + s2

;model(Cat12):
U(alt1) = b90[n,(n,-0.2746,0.0544),(n,0.1923,0.0526)] * tt.ref[135] + b91[n,(n,-0.1228,0.0379),(n,0.1843,0.0449)] * tts.ref[34] + b92[n,(n,-0.1382,0.0654),(n,0.2310,0.0770)] * sn.ref[50] + b93[(n,-1.5784,0.3706)] * vr.ref[21.0] + s1[ec,0.2]/
U(alt2) = b90[n,(n,-0.2746,0.0544),(n,0.1923,0.0526)] * tt.piv[-20%, -10%, 0%, 10%, 20%] + b91[n,(n,-0.1228,0.0379),(n,0.1843,0.0449)] * tts.piv[-50%, -25%, 0%, 25%, 50%] + b92[n,(n,-0.1382,0.0654),(n,0.2310,0.0770)] * sn.piv[-50%, -25%, 0%, 25%, 50%] + b93[(n,-1.5784,0.3706)] * vr.piv[-25%, -12.5%, 0%, 12.5%, 25%] + s1 + s2[ec,0.5]/
U(alt3) = b90[n,(n,-0.2746,0.0544),(n,0.1923,0.0526)] * tt.piv[-20%, -10%, 0%, 10%, 20%] + b91[n,(n,-0.1228,0.0379),(n,0.1843,0.0449)] * tts.piv[-50%, -25%, 0%, 25%, 50%] + b92[n,(n,-0.1382,0.0654),(n,0.2310,0.0770)] * sn.piv[-50%, -25%, 0%, 25%, 50%] + b93[(n,-1.5784,0.3706)] * vr.piv[-25%, -12.5%, 0%, 12.5%, 25%] + s2 $


Is it because I am having 3 parameters as random? I am even running the same script but with 2 random parameters. However, its still running and hasn't terminated yet.

Thanks a lot in advance

Neeraj

[Michiel Bliemer]

If my quick calculation is correct, you are asking Ngene to evaluate the design using more than 531,000,000 draws. Clearly, it is not possible to keep so many numbers in memory on a normal conputer. Carefully consider reducing the total number of draws by reducing the number of Bayesian priors and random parameters, error components, and Gaussian abscissas.

[neeraj85]

Thanks a lot sir.

Can you please let me know how you calculated the number of draws from the given information.
This will give me further insights on carefully adjusting the script.

[Michiel Bliemer]

You have 5 random coefficients and 7 Bayesian priors, each with 3 Gaussian abscissas using a panel size of 1000 yields 3^7 * 3^5 * 1000 draws for each of the models.