Dear Statalisters,
I’m conducting a choice experiment with five attributes, were two attributes have four continuous levels, and two attributes have two levels included as a dummy, and one attribute had three levels where I recoded this into three dummies and using one level in my model. I also added an attribute for price ranging in four levels. I run the mixlogitwtp command by A.R. Hole to estimate my model in WTP space. I received a error message: Warning: Convergence not achieved.
I started by declaring my data to be panel data, since each individual (id) are given seven choice questions with two alternatives:
CODE:
cmset id question alternatives
OUTPUT:
Panel data: Panels id and time question
Case ID variable: _caseid
Alternatives variable: alternatives
Panel by alternatives variable: _panelaltid (strongly balanced)
Time variable: fråga, 1 to 7
Delta: 1 unit
Note: Data have been xtset.
Then generated negprice= (-1)*price, to be able to corporate it into the log normal distribution.
CODE:
list id _caseid choice x1 x2 x3 x4 x5 negprice if id==9
OUTPUT
CODE:
mixlogitwtp choice, group(_caseid) price(negprice) id(id) rand( x1 x2 x3 x4 x5) nrep(1000) burn(100) technique(bhhh) iterate(1000)
I was wondering if my results could still be used, even if it did not converge, but I wanted to see if anybody had a solution to this problem?
Thank you,
Amanda
I’m conducting a choice experiment with five attributes, were two attributes have four continuous levels, and two attributes have two levels included as a dummy, and one attribute had three levels where I recoded this into three dummies and using one level in my model. I also added an attribute for price ranging in four levels. I run the mixlogitwtp command by A.R. Hole to estimate my model in WTP space. I received a error message: Warning: Convergence not achieved.
I started by declaring my data to be panel data, since each individual (id) are given seven choice questions with two alternatives:
CODE:
cmset id question alternatives
OUTPUT:
Panel data: Panels id and time question
Case ID variable: _caseid
Alternatives variable: alternatives
Panel by alternatives variable: _panelaltid (strongly balanced)
Time variable: fråga, 1 to 7
Delta: 1 unit
Note: Data have been xtset.
Then generated negprice= (-1)*price, to be able to corporate it into the log normal distribution.
CODE:
list id _caseid choice x1 x2 x3 x4 x5 negprice if id==9
OUTPUT
id | _caseid | choice | x1 | x2 | x3 | x4 | x5 | negprice |
9 | 57 | 0 | 260 | 0 | 1 | 0 | 12 | -90 |
9 | 57 | 1 | 330 | 1 | 0 | 0 | 28 | -25 |
9 | 58 | 0 | 130 | 0 | 0 | 0 | 12 | -130 |
9 | 58 | 1 | 130 | 1 | 1 | 0 | 7 | -25 |
9 | 59 | 0 | 260 | 1 | 1 | 1 | 18 | -65 |
9 | 59 | 1 | 330 | 0 | 0 | 0 | 12 | -65 |
9 | 60 | 0 | 260 | 1 | 0 | 1 | 7 | -25 |
9 | 60 | 1 | 260 | 0 | 0 | 0 | 28 | -90 |
9 | 61 | 0 | 560 | 0 | 1 | 0 | 28 | -25 |
9 | 61 | 1 | 560 | 0 | 0 | 1 | 28 | -90 |
9 | 62 | 0 | 130 | 1 | 0 | 0 | 7 | -90 |
9 | 62 | 1 | 130 | 0 | 1 | 1 | 12 | -65 |
9 | 63 | 0 | 330 | 0 | 0 | 0 | 28 | -65 |
9 | 63 | 1 | 330 | 1 | 1 | 0 | 18 | -130 |
mixlogitwtp choice, group(_caseid) price(negprice) id(id) rand( x1 x2 x3 x4 x5) nrep(1000) burn(100) technique(bhhh) iterate(1000)
Iteration 0: log likelihood = -669.98139 | ||||||
Iteration 1: log likelihood = -666.86419 (backed up) | ||||||
Iteration 2: log likelihood = -662.24822 | ||||||
Iteration 3: log likelihood = -657.09689 | ||||||
Iteration 4: log likelihood = -656.78398 | ||||||
Iteration 5: log likelihood = -655.64698 | ||||||
Iteration 6: log likelihood = -654.85472 | ||||||
Iteration 7: log likelihood = -653.50478 | ||||||
Iteration 8: log likelihood = -653.3682 | ||||||
Iteration 9: log likelihood = -653.30287 | ||||||
Iteration 10: log likelihood = -653.28295 | ||||||
Iteration 11: log likelihood = -653.24267 | ||||||
Iteration 12: log likelihood = -653.22792 | ||||||
Iteration 13: log likelihood = -653.21128 | ||||||
Iteration 14: log likelihood = -653.19447 | ||||||
Iteration 15: log likelihood = -653.18469 | ||||||
Iteration 16: log likelihood = -653.17412 | ||||||
Iteration 17: log likelihood = -653.17223 | ||||||
Iteration 18: log likelihood = -653.15394 | ||||||
Iteration 19: log likelihood = -653.15157 | ||||||
Iteration 20: log likelihood = -653.11819 | ||||||
Iteration 21: log likelihood = -653.11664 | ||||||
Iteration 22: log likelihood = -653.09851 | ||||||
Iteration 23: log likelihood = -653.0953 | ||||||
Iteration 24: log likelihood = -653.05923 | ||||||
Iteration 25: log likelihood = -653.04855 | ||||||
Iteration 26: log likelihood = -653.04038 | ||||||
Iteration 27: log likelihood = -653.03079 | ||||||
Iteration 28: log likelihood = -653.02477 | ||||||
Iteration 29: log likelihood = -653.01427 | ||||||
Iteration 30: log likelihood = -653.00891 | ||||||
Iteration 31: log likelihood = -652.99678 | ||||||
Iteration 32: log likelihood = -652.99036 | ||||||
Iteration 33: log likelihood = -652.97525 | ||||||
Iteration 34: log likelihood = -652.96686 | ||||||
Iteration 35: log likelihood = -652.96144 | ||||||
Iteration 36: log likelihood = -652.96081 | ||||||
Iteration 37: log likelihood = -652.64558 | ||||||
Iteration 38: log likelihood = -652.59462 | ||||||
Iteration 39: log likelihood = -652.55809 | ||||||
Iteration 40: log likelihood = -652.54935 | ||||||
Iteration 41: log likelihood = -652.53877 | ||||||
Iteration 42: log likelihood = -652.53268 | ||||||
Iteration 43: log likelihood = -652.52968 | ||||||
Iteration 44: log likelihood = -652.52941 | ||||||
Iteration 45: log likelihood = -652.52768 (backed up) | ||||||
Iteration 46: log likelihood = -652.48455 | ||||||
Iteration 47: log likelihood = -652.47941 | ||||||
Iteration 48: log likelihood = -652.47597 | ||||||
Iteration 49: log likelihood = -652.4758 (backed up) | ||||||
Iteration 50: log likelihood = -652.47578 (backed up) | ||||||
Iteration 51: log likelihood = -652.47576 (backed up) | ||||||
Iteration 52: log likelihood = -652.47576 (backed up) | ||||||
Iteration 53: log likelihood = -652.47532 | ||||||
Iteration 54: log likelihood = -652.47507 (backed up) | ||||||
Iteration 55: log likelihood = -652.47437 (backed up) | ||||||
Iteration 56: log likelihood = -652.47418 (backed up) | ||||||
Iteration 57: log likelihood = -652.47411 (backed up) | ||||||
Iteration 58: log likelihood = -652.47409 (backed up) | ||||||
Iteration 59: log likelihood = -652.47408 (backed up) | ||||||
Iteration 60: log likelihood = -652.47408 (backed up) | ||||||
Iteration 61: log likelihood = -652.47408 (backed up) | ||||||
,....., | ||||||
Iteration 1000: log likelihood = -652.47408 (backed up) | ||||||
convergence not achieved | ||||||
Mixed logit model in WTP space | Number of obs = 2,122 | |||||
Wald chi2(6) = 2140.99 | ||||||
Log likelihood = -652.47408 | Prob > chi2 = 0.0000 | |||||
OPG | ||||||
choice | Coefficient | std. err. | z | P>z | [95% conf. | interval] |
Mean | ||||||
x1 | 2.020137 | .5630591 | 3.59 | 0.000 | .9165613 | 3.123712 |
x2 | -112.2809 | 26.868 | -4.18 | 0.000 | -164.9413 | -59.62062 |
x3 | 193.0917 | 45.25788 | 4.27 | 0.000 | 104.3879 | 281.7955 |
x4 | 229.0887 | 42.99712 | 5.33 | 0.000 | 144.8159 | 313.3615 |
x5 | 9.790245 | 3.151583 | 3.11 | 0.002 | 3.613255 | 15.96723 |
negprice | -5.43188 | .3157098 | -17.21 | 0.000 | -6.05066 | -4.8131 |
SD | ||||||
x1 | .2036296 | 4.910859 | 0.04 | 0.967 | -9.421478 | 9.828737 |
x2 | -68.31679 | 40.64345 | -1.68 | 0.093 | -147.9765 | 11.34291 |
x3 | 55.66972 | 48.15007 | 1.16 | 0.248 | -38.70268 | 150.0421 |
x4 | 175.8745 | 56.2576 | 3.13 | 0.002 | 65.61161 | 286.1374 |
x5 | 15.9116 | 4.383527 | 3.63 | 0.000 | 7.320043 | 24.50315 |
negprice | 1.322363 | .4069266 | 3.25 | 0.001 | .5248016 | 2.119925 |
Warning: Convergence not achieved. | ||||||
The sign of the estimated standard deviations is irrelevant: interpret them as being positive |
Thank you,
Amanda