Using Orthogonal Main Effects Plan to select profiles for conjoint analysis

Question

I am trying to create a code in Python to select orthogonal profiles given some attributes and levels. For eg:

{'Pressure':[40,55,70,80,90],
'Temperature':[290, 320, 350],
'Flow rate':[0.2,0.4,0.5,0.6,0.7],
'Time':[5,11]}

If I need to a conjoint analysis using all possible profiles, it would become 535*2=150 profiles, which is too much. I need to reduce the number of profiles so that they are orthogonal. Is there any implementation in Python that I can use or any guidance on how I would go about building this would be highly appreciated.

Thanks!

Answer 1

Sorry, no experience with python, but fairly easy to do in R, even for absolute beginners:

# if you do not have it yet, install via install.packages("radiant")
library(radiant) 
doe(c("Pressure;40;55;70;80;90",
      "Temperature;290; 320; 350",
      "Flow rate;0.2;0.4;0.5;0.6;0.7",
      "Time;5;11"),
    trials = 50)

They also have Shiny-App: https://vnijs.shinyapps.io/radiant/?SSUID=3dcff236d7

Go to Design, then to doe, put in your Design Factors:

Pressure;40;55;70;80;90
Temperature;290; 320; 350
Flow rate;0.2;0.4;0.5;0.6;0.7
Time;5;11

Set trial to the number of profiles you can afford. That's it. Note that you will not get a perfectly orthogonal design, but it will usually be ok as long as the trial number is not too small.