ighdez / edt

EDT: Efficient designs for Discrete Choice Experiments

EDT

EDT is a Python-based tool to construct D-efficient designs for Discrete Choice Experiments. EDT combines enough flexibility to construct from simple 2-alternative designs with few attributes, to more complex settings that may involve conditions between attributes.

While EDT designs are based on the widely-used D-efficiency criterion (see Kuhfeld, 2005), it differs from other free- or open-source efficient design tools (such as idefix for R) on the use of a Random Swapping Algorithm based on the work of Quan, Rose, Collins and Bliemer (2011), obtaining significant speed improvements to reach an optimal design, to a level that competes with well-known paid software such as NGene.

The main features of EDT are:

• Allows to customize each attribute in terms of:

  • Attribute Levels
  • Continuous or Dummy coding (Effects coding is work-in-progress)
  • Assignement of prior parameters
  • Attribute names

• Designs with constraints: EDT allows to define conditions over different attribute levels.

• Designs with blocks.

• Designs with alternative-specific constants (ASC).

• Multiple stopping criteria (Fixed number of iterations, iterations without improvement or fixed time).

• Allows to export the output design in an Excel file.

Any contributions to EDT are welcome via this Git, or to the email joseignaciohernandezh at gmail dot com.

References

• Kuhfeld, W. F. (2005). Experimental design, efficiency, coding, and choice designs. Marketing research methods in SAS: Experimental design, choice, conjoint, and graphical techniques, 47-97.
• Quan, W., Rose, J. M., Collins, A. T., & Bliemer, M. C. (2011). A comparison of algorithms for generating efficient choice experiments.

edt's Issues

Allow EDT to ignore attribute level balance

By default, EDT gives an error if we use a NSC that is not a multiple of the number of levels. Cannot ignore attribute level balance.

Goal: allow to ignore attribute level balance (with a warning)

A nice adding

Design cannot be generated when opt-out option is not present

For example, changing the option 'optout' from 1 to 0 in example.py throws an error cannot reshape array of size 118 into shape (120,1)