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Hi Andrew,

Thanks for the suggestion

Hi,
First of all, I would like to thank the entire Ngene team for providing a great experimental design software, an easy to understand user's manual and such a quick and responsive forum.
I successfully completed the Stated Choice study for my thesis and the results look good.
I am currently ...

Hi,

I read chapter 7 of the NGENE manual and understood that efficient designs are based on minimizing the AVC matrix. Hence orthogonality need not necessarily hold.

I developed an efficient design using prior betas, eff(mnl,d) and 3 alternatives (1 status quo & 2 hypothetical). Each alternative ...

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.

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 ...

Thanks a lot sir.

It makes sense now as to why i was getting undefined estimate (0 by 0 form)

I tried the procedure suggested by you. I was able to generate an efficient MNL pivot design using the script1. However, when i tried to evaluate the design.ngd (from script1) in script2 with mixed logit command and estimates, I am getting an undefined mean Bayesian RP-Panel estimate .

I continued ...

Thank you sir.

I tried to incorporate the suggestions received from you.

However, I am getting an runtime error stating --> Error: Only one ';fisher' property can be specified at this time.

Can you please let me know how should I resolve the issue.


Following is my script:


Design

;alts(Cat1 ...

Thank you very much sir.

So, I should use the following script:

;eff = mnl_model(mnl,d)
;eff = mxl_model(rppanel,d)
;rdraws = gauss(3)
;rep = 500
;model(mnl_model):
U(alt1) = b1[0.1] * x1[1,2,3] + ... --> Beta estimates from MNL
;model(mxl_model):
U(alt1) = b1[n,0. 15,0.05] * x1[1,2,3 ...

My apologies for posting the thread in a wrong forum.

It makes sense that we cannot compare the D-errors given that I used a different modelling technique.

Is there a way I can check that the newly obtained prior values will be more efficient than the previously used priors?



Also, can you ...