This app automatically downloads data from the Huffpost poll aggregator. It then performs a Bayesian change-point analysis of that data to identify key periods when public opinion shifted
It is based in the R computing language, using the bcp package developed by Xiaofei Wang, Chandra Erdman, and John W. Emerson1. The program is based on the Barry-Hartigan method2.
It uses a very conservative prior (.001) to minimize noise in the data. Error is aggregated from all polls. There are 2,000 burn-ins and 10,000 markov-chain Monte Carlo resimulations of the data to calculate the posterior means & posterior probabilities.
You, the user, can choose which ones and which populations to focus on. Make your selections then click "Go"
First, you will need to download a copy of R appropriate for your computer (Mac, Windows, or Linux):
Next, you will need to install a package called 'shiny' to run it locally. You can do so by pasting this line into the R consul when you launch it:
install.packages("shiny")
It will ask you to choose a download mirror (you can choose anyone, the result is the same). Then, to run the software:
shiny::runGitHub("leedrake5/pollChangePoint")
The first time it runs, it may take some time to download the supporting software. After that, you should be good to go. If you'd like to download a copy and run it offline, you can instead download it from GitHub (https://github.com/leedrake5/CloudCal) and then run it locally:
shiny::runApp("your/computer/directory/pollChangePoint")
Beats me - this is the first time I've tried to do anything like this. Drop me an email at [email protected] if you have questions or need to address this step.
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Erdman, C, Emerson, J.W. 2007. bcp: An R package for performing a bayesian analysis of change point problems. Journal of Statistical Software 23(3): 1 - 13
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Barry, D, Hartigan, J.A. 1993. A Bayesian analysis for change point problems. Journal of the American Statistical Association 35(3), 309 - 319