Dear all,
This is my basic code. Because priors are not known, I started with zero priors. For the same design, when * and mfederov algorithms are used at a time, there was no attribute-level balance but different possible combinations were found to be present. When mfederov algorithm is not used with * present or not for dominance check, attribute-level balance was present but different attribute combinations were found to be missing (same combinations were repeated in different blocks). The d-error when the algorithm was used was slightly higher than when not used.
1. What exactly is the use of this algorithm?
2.What should I do to maximize the design efficiency without having dominant alternatives? Is there any check to find out dominance or identical alternatives apart from checking manually after experiment design?
3. Is the design without different pair-wise combinations of attributes (as observed when mfederov is not used) efficient?
4. Is there any problem if a design without attribute-level balance used for surveying or is it a necessary criterion that should be fulfilled?
design
;alts = Alternative1*, Alternative2*
;rows = 24
;eff = (mnl,d)
;block = 6
;model:
U(Alternative1) = a.dummy[-0.002|-0.0012|-0.001] * A[0,1,2,3] +
b.dummy[-0.002|-0.0012|-0.001] * B[0,1,2,3] +
c.dummy[-0.002|-0.0015|-0.001] * C[0,1,2,3] +
d.dummy[-0.002|-0.001|-0.001] * D[0,1,2,3] +
e[0.003] * E[12,14,16] +
f[-0.003] * F[20,15,10,5] +
g[-0.003] * G[6,7,8,9]
/
U(Alternative2) = a * A + b * B + c * C + d * D + e * E + f * F + g * G
$
The dummy variables are related to transportation facilities and the continuous are service hours, waiting time and cost of travel. To show the order of levels, I have used dummy coefficients in decreasing order with respect to the last level (highest improvement level).
5. Is the syntax correct?
Regards
P. Vaishnavi