D-efficient design (unlabelled alternatives)

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D-efficient design (unlabelled alternatives)

Postby Peter_C » Thu Aug 13, 2020 10:06 pm

Dear Michiel!

I would like to create a D-efficient design for three unlabelled alternatives.
I used the following syntax in Ngene:

design
;alts = alt1, alt2, alt3
;rows = 16
;block = 2
;eff = (mnl,d)
;con
;model:
U(alt1) = a1[0.001] + b2[-0.001] * price[10,18,26,30] + b3[-0.001] * invcost[200,300,700,1000] + b4[0.001] * env[1,2,3,4] + b5.dummy[-0.001] * conv[0,1] /
U(alt2) = a2[0.001] + b2*price + b3*invcost + b4*env + b5*conv /
U(alt3) = b2*price + b3*invcost + b4*env + b5*conv
$

I got the following results:

D error 0.009803
A error 0.180855
B estimate 86.613607
S estimate 1511311.361419


Is it possible to got that very large S estimate result (It can be caused by my relatively small prior values?)?

Furthermore in my results I found two problematistic choice tasks (due to dominant alternatives).
What is the best way/practice what I can do with these choice tasks?

Thank you very much for you answer!

Best regards,
Peter
Peter_C
 
Posts: 10
Joined: Sat Jun 20, 2020 12:16 am

Re: D-efficient design (unlabelled alternatives)

Postby Michiel Bliemer » Fri Aug 14, 2020 10:15 am

Hi Peter,

1. S-estimates are only meaningful if your priors come from a pilot study. Your priors are chosen just for sign (I think) so they are not meaningful, and therefore the S-estimates have no meaning and should be ignored.

2. You can tell Ngene to check for dominance by using ;alts = alt1*, alt2*, alt3*. This will not work with the default swapping algorithm because dominance checks impose heavy constraints, so you will need to swap to the modified Federov algorithm. This algorithm does not guarantee attribute level balance, so it is best to impose some constraints on the appearance of each attribute level.

For example:

Code: Select all
design
;alts = alt1*, alt2*, alt3*
;rows = 16
;block = 2
;eff = (mnl,d)
;alg = mfederov(candidates = 1000)
;con
;model:
U(alt1) = a1[0.001]
        + b2[-0.001] * price[10,18,26,30](2-6,2-6,2-6,2-6)
        + b3[-0.001] * invcost[200,300,700,1000](2-6,2-6,2-6,2-6)
        + b4[0.001] * env[1,2,3,4](2-6,2-6,2-6,2-6)
        + b5.dummy[-0.001] * conv[0,1](6-10,6-10)
        /
U(alt2) = a2[0.001] + b2*price + b3*invcost + b4*env + b5*conv /
U(alt3) = b2*price + b3*invcost + b4*env + b5*conv
$


Michiel
Michiel Bliemer
 
Posts: 1040
Joined: Tue Mar 31, 2009 4:13 pm

Re: D-efficient design (unlabelled alternatives)

Postby Peter_C » Sat Aug 15, 2020 3:20 am

Thank you very much for your help!
Peter_C
 
Posts: 10
Joined: Sat Jun 20, 2020 12:16 am


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