spdesign: Designing Stated Preference Experiments

spdesign spdesign website

Contemporary software commonly used to design stated preference experiments are expensive and the code is closed source. spdesign is a free software package with an easy to use interface to make flexible stated preference experimental designs using state-of-the-art methods.

Installation

The package can be installed from CRAN.

install.packages("spdesign")

A development version of the package can be installed from Github. Remember to select the most recent development version (check the available branches).

devtools::install_github("edsandorf/spdesign")

Example

This is a basic example which shows you how to solve a common problem:

library(spdesign)

#' Specifying a utility function with 3 attributes and a constant for the
#' SQ alternative. The design has 20 rows.
utility <- list(
  alt1 = "b_x1[0.1]  * x1[1:5] + b_x2[0.4] * x2[c(0, 1)] + b_x3[-0.2] * x3[seq(0, 1, 0.25)]",
  alt2 = "b_x1       * x1      + b_x2      * x2          + b_x3       * x3",
  alt3 = "b_sq[0.15] * sq[1]"
)

# Generate designs ----
design <- generate_design(utility,
                          rows = 20,
                          model = "mnl", 
                          efficiency_criteria = "d-error",
                          algorithm = "rsc")

# Add a blocking variable to the design with 4 blocks.
design <- block(design, 2)


summary(design)

Bugs and error reporting

All software contains bugs and we would very much like to find these and root them out. If you find a bug or get an error message, please reach out so that we can try and improve the software.

Acknowledgements

We are grateful to Petr Mariel, Jürgen Meyerhoff and Ainhoa Vega for providing feedback and extensive testing of the package. We also thank participants in the 2022 Summer School “Valuing options of adaption to climate change using choice experiments” at the University of Cape Town for valuable feedback on a beta version of the package. The package comes with no warranty and the authors cannot be held liable for errors or mistakes resulting from use. The authors acknowledge funding from the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant INSPiRE (Grant agreement ID: 793163).