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Thanks so much, Michiel! I got it working after some finessing (was really hoping not to bother you by sending over the candidate set)!

Just in case it’s helpful for anyone else who runs into a similar issue: when I exported the candidate set from R to a CSV, I originally used the following line ...

Got it. Thank you, Michiel.

I had to change the status quo level for attribute Reg from 8 to 12 and the upper bound for this attribute from 10 to 12 . I did not change any restrictions I imposed. Below is my revised syntax (It's very similar to the original syntax). I also made sure: 1. The ...

Hi Michiel,

This is so helpful. Thank you. It turns out that the issue was due to a mismatched variable name. In the script, the cost variable in the status quo option is named as sq.cost_sq , but I had it as sq.cost in the candidate set. Once I fixed the mismatch, the script ran without any ...

Hi Michiel,

I'm running into an issue when using a custom candidate set. Below is my code for generating an efficient experimental design with no priors. It runs fine when I use mfedorov(candidates = 3000), but when I switch to referencing an external candidate set file using mfedorov(candidates ...

Thanks Michael. I did do this. However, D-error becomes pretty big, which seems to confirm my thought on the loss of efficiency, given that the quadratic term is a deterministic function of the linear term itself.

This is why I was curious if you've had any experience with it and/or if you'd ...

I'm designing a discrete choice experiment with four attributes: three topic-related attributes and one cost attribute. Each attribute has four levels. I want to estimate a quadratic utility function of the form:

V_i = beta_1 [Attribute1]_i + beta_2 [Attribute1]_i ^2 + beta_3 [Attribute2]_i + beta ...