dgo is an R package for the dynamic estimation of group-level public opinion. You can use the package to estimate latent trait means in subpopulations from survey data. For example, dgo can estimate the average policy liberalism in each American state over time among Democrats, Independents, and Republicans, given their answers to survey questions about policy proposals.
dgo accomplishes this using a Bayesian group-level IRT approach developed by Caughey and Warshaw 2015. It models latent traits at the level of demographic and geographic groups rather than individuals. It uses a hierarchical model to borrow strength cross-sectionally and dynamic linear models to do so across time.
The package can also be used to estimate smoothed estimates of subpopulations’ average responses to single survey items, using a dynamic multi-level regression and poststratification (MRP) model (Park, Gelman, and Bafumi 2004). For instance, you can use dgo to estimate public opinion in each state on same-sex marriage or the Affordable Care Act.
This model opens up new areas of research on historical public opinion in the United States at the subnational level. It also allows scholars of comparative politics to estimate dynamic cross-national models of public opinion.
dgo can be installed from CRAN:
if (!require(devtools, quietly = TRUE)) install.packages("devtools") devtools::install_github("jamesdunham/dgo")
Load the package and set RStan’s recommended options for a local, multicore machine with excess RAM:
library(dgo) rstan_options(auto_write = TRUE) options(mc.cores = parallel::detectCores())
The minimal workflow from raw data to estimation is:
Please report issues that you encounter.
OS X only: RStan creates temporary files during estimation in a location given by
tempdir(), typically an arbitrary location in
/var/folders. If a model runs for days, these files can be cleaned up while still needed, which induces an error. A good solution is to set a safer path for temporary files, using an environment variable checked at session startup. For help setting environment variables, see the Stack Overflow question here. Confirm the new path before starting your model run by restarting R and checking the output from
Models fitted before October 2016 (specifically < #8e6a2cf) using dgirt are not fully compatible with dgo. Their contents can be extracted without using dgo, however, with the
$ indexing operator. For example: