I’ve been helping a small team work on some willingness-to-pay analysis using double-bounded choice survey data. I’ll post more about that soon, but I am writing now to talk about the double-bounded choice models in R. The DCchoice library is really nice and makes the analysis about as simple as possible. Further, the text Stated Preference Methods Using R is fantastic.
But…the confidence interval bootstrap routine is really slow with double-bounded choice models. Single-bounded not so much. But it was taking hours to run, even for only 1000 samples. I was using SciServer for running the model and it gives me 16 cores, so I rewrote
threads, that allows you to set the total number of threads used.
This work used SciServer, a collaborative research environment for large-scale data-driven science. It is developed at, and administered by, the Institute for Data Intensive Engineering and Science at the Johns Hopkins University. SciServer is funded by the National Science Foundation Award ACI-1261715. For more information about SciServer, please visit http://www.sciserver.org.