# [Nlogit] MNL error message on IIA test

#### questionsfun

##### New Member
[Nlogitv4] MNL error message on IIA test

Hello,

I am using Nlogit software to do MNL. I created a DCE experiment about smartwatch and collected some data from my friends. I only have 18 responses but each person went through 10 or 11 choice sets. Due to my small sample size, I am not surprised that most of my parameters are not significant. I tried to run IIA test just to see if it runs. And I got error messages. I wonder if it is because I have too few responses or my codes are wrong. I really appreciate your advice, thanks in advance!

Here is some basic info about my DCE experiment before I show you the codes and results.
• Each respondent went through 10 or 11 choice sets
• Each choice set has 4 options: samsung, apple and google smartwatches and none option (this is an alternative specific design as the options are branded/labelled)
• Each product has 4 attributes:
[*]Phone compatibility (2 levels)
[*]Whether it measures health metrics (2 levels)
[*]Whether the watch band is detachable (2 levels)
[*]Price (4 levels)​

Here are the codes:
nlogit
;lhs= choice, cset, altij
;choices = sams, apple, google, none
;ias=sams
;model:
U(sams) = sams + scomp*compd + shealth*healthd + sband*bandd + sp*price/
U(apple) = apple + acomp*compd + ahealth*healthd + aband*bandd + ap*price/
U(google)= google + gcomp*compd + ghealth*healthd + gband*bandd + gp*price$Please note: the code ';ias = sams' is used to remove the samsung observations to test IIA Here are the results and error messages: | Discrete choice and multinomial logit models| +---------------------------------------------+ +------------------------------------------------------+ |WARNING: Bad observations were found in the sample. | |Found 34 bad observations among 179 individuals. | |You can use ;CheckData to get a list of these points. | +------------------------------------------------------+ Hessian is not positive definite at start values. Error 803: Hessian is not positive definite at start values. B0 is too far from solution for Newton method. Switching to BFGS as a better solution method. Normal exit from iterations. Exit status=0. Error 585: Matrix being moved is too large for target. +---------------------------------------------+ | Discrete choice (multinomial logit) model | | Maximum Likelihood Estimates | | Model estimated: Oct 16, 2013 at 11:20:33AM.| | Dependent variable Choice | | Weighting variable None | | Number of observations 145 | | Iterations completed 14 | | Log likelihood function -132.7923 | | Number of parameters 15 | | Info. Criterion: AIC = 2.03851 | | Finite Sample: AIC = 2.06418 | | Info. Criterion: BIC = 2.34645 | | Info. Criterion:HQIC = 2.16364 | | R2=1-LogL/LogL* Log-L fncn R-sqrd RsqAdj | | Constants only. Must be computed directly. | | Use NLOGIT ;...; RHS=ONE$ |
| Chi-squared[12] = 23.11360 |
| Prob [ chi squared > value ] = .02678 |
| Response data are given as ind. choice. |
| Number of obs.= 179, skipped 34 bad obs. |
+---------------------------------------------+
| Could not carry Hausman test for IIA. |
| Difference matrix is not positive definite. |

+---------------------------------------------+
| Notes No coefficients=> P(i,j)=1/J(i). |
| Constants only => P(i,j) uses ASCs |
| only. N(j)/N if fixed choice set. |
| N(j) = total sample frequency for j |
| N = total sample frequency. |
| These 2 models are simple MNL models. |
| R-sqrd = 1 - LogL(model)/logL(other) |
| nJ = sum over i, choice set sizes |
+---------------------------------------------+
+--------+--------------+----------------+--------+--------+
|Variable| Coefficient | Standard Error |b/St.Er.|P[|Z|>z]|
+--------+--------------+----------------+--------+--------+
SAMS | .000000 1.00000000 .000 1.0000
SCOMP | .000000 ......(Fixed Parameter).......
SHEALTH | .000000 ......(Fixed Parameter).......
SBAND | .000000 ......(Fixed Parameter).......
SP | .000000 .01271025 .000 1.0000
APPLE | 1.57287869 ......(Fixed Parameter).......
ACOMP | -.09410830 .00378327 -24.875 .0000
AHEALTH | .50683557 .01689470 30.000 .0000
ABAND | .68988054 ......(Fixed Parameter).......
AP | -.00417422 ......(Fixed Parameter).......