Hi Team,
I am creating a candidate set for the explicit partial design. My instrument has seven items, each with five levels. I want to overlap any two of the seven attributes between Choice A and Choice B for each task. Since there are 21 rotation overlap combinations (e.g., (item1, item2), (item1, item3), (item1, item4)...), I plan to create choice tasks for each of the 21 combinations and combine them into a candidate set. Below is my code for running the first combination using Ngene:
Design
;alts = alt1*, alt2*
;rows = 200
;eff = (mnl,d)
;alg = mfederov
;require:
alt1.item1 = alt2.item1,
alt1.item2 = alt2.item2
;model:
U(alt1) = b0[0] + b1.dummy[(u,-1,0)|(u,-0.5,0)|(u,-0.25,0)|(u,-0.1,1)]*item1[1,2,3,4,5] + b2.dummy[(u,-1,0)|(u,-0.5,0)|(u,-0.25,0)|(u,-0.1,1)]*item2[1,2,3,4,5] + b3.dummy[(u,-1,0)|(u,-0.5,0)|(u,-0.25,0)|(u,-0.1,1)]*item3[1,2,3,4,5] + b4.dummy[(u,-1,0)|(u,-0.5,0)|(u,-0.25,0)|(u,-0.1,1)]*item4[1,2,3,4,5] + b5.dummy[(u,-1,0)|(u,-0.5,0)|(u,-0.25,0)|(u,-0.1,1)]*item5[1,2,3,4,5] + b6.dummy[(u,-1,0)|(u,-0.5,0)|(u,-0.25,0)|(u,-0.1,1)]*item6[1,2,3,4,5] + b7.dummy[(u,-1,0)|(u,-0.5,0)|(u,-0.25,0)|(u,-0.1,1)]*item7[1,2,3,4,5] /
U(alt2) = b1.dummy*item1 + b2.dummy*item2 + b3.dummy*item3 + b4.dummy*item4 + b5.dummy*item5 + b6.dummy*item6 + b7.dummy*item7
$
I have the following questions:
1. The results generate an undefined MNL D-error. I have tried adjusting the number of rows (increase or decrease), but the generated design still shows an undefined MNL D-error. How can I fix this? The code runs smoothly with a defined D-error when I do not include the required condition and mfederov, so I believe it is not related to the model specification. Can I still use the choice tasks in the design with an undefined MNL D-error to construct my candidate set? I read in the manual that there does not need to be a lot of intelligence in the candidate set.
2. Am I correct in thinking that there is no explicit requirement for the size of the candidate set, as long as it covers the rotation combinations I need in the choice tasks, with randomly generated combinations of non-overlapping attribute levels for the remaining five attributes? Ngene will automatically select appropriate choice tasks from the candidate set when I input the dataset back into Ngene for running this partial design using the mfederov algorithm. If this is true, I assume I do not need to rely on Ngene to generate the candidate set, but could instead create it externally using R to generate a certain number of choice tasks, with my 21 combinations of overlapped attributes and a random sample of any size for the non-overlapped attributes?
Many thanks for your help and clarification.