by Dana » Mon May 25, 2020 10:31 am
Hi Michiel,
Thank you very much for your swift response. It is really helpful to hear that we could use these priors to estimate a Bayesian efficient design. It is also helpful to hear your insights on whether to use our pilot study coefficients as priors. There were a few coefficients that did not have the correct sign. These are the problematic attributes that we are considering revising or removing. I have pasted the Nlogit output from our MNL model below - we are considering removing ATT_Win and revising the levels for ATT_OST (because these levels were not spread very far apart, we are planning on widening the range for the final model). If we revise the levels of ATT_OST and remove ATT_Win, would we still be able to use these coefficients (committing ATT_Win) as priors to generate a Bayesian efficient design? Thank you very much for your insight! I am very new to choice modeling and this information is tremendously helpful.
Kind regards,
Dana
Output from MNL Model in Nlogit :
|-> NLOGIT
;LHS = CHOICE
;Choices = 1,2,3
;RPL
;Fcn = Att_Mos(n), Att_Fre(n), Att_OST(n), Att_Win(n), Att_WTP(ln), one(n) ;Model: U(1,2) =
+ ATT_Mos * ATT_Mos
+ ATT_Fre * ATT_Fre
+ ATT_OST * ATT_OST
+ ATT_Win * ATT_Win
+ ATT_WTP * ATT_WTP
/
U(3) = one $
Iterative procedure has converged
Normal exit: 6 iterations. Status=0, F= .2265967D+03
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Start values obtained using MNL model
Dependent variable Choice
Log likelihood function -226.59669
Estimation based on N = 252, K = 6
Inf.Cr.AIC = 465.2 AIC/N = 1.846
---------------------------------------
Log likelihood R-sqrd R2Adj
Constants only -231.9123 .0229-.0009
Note: R-sqrd = 1 - logL/Logl(constants)
Warning: Model does not contain a full
set of ASCs. R-sqrd is problematic. Use
model setup with ;RHS=one to get LogL0.
---------------------------------------
Response data are given as ind. choices
Number of obs.= 252, skipped 0 obs
--------+--------------------------------------------------------------------
| Standard Prob. 95% Confidence
CHOICE| Coefficient Error z |z|>Z* Interval
--------+--------------------------------------------------------------------
ATT_MOS| .00670 .00626 1.07 .2849 -.00558 .01897
ATT_FRE| .01525** .00630 2.42 .0155 .00290 .02761
ATT_OST| -.00211 .00943 -.22 .8230 -.02058 .01637
ATT_WIN| -.01282 .01184 -1.08 .2789 -.03604 .01039
ATT_WTP| -.00401** .00200 -2.00 .0450 -.00794 -.00009
Constant| -1.70277*** .43604 -3.91 .0001 -2.55739 -.84815
--------+--------------------------------------------------------------------
***, **, * ==> Significance at 1%, 5%, 10% level.
Model was estimated on May 12, 2020 at 11:55:10 AM
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