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README.Rmd
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---
title: 'dgo: Dynamic Estimation of Group-Level Opinion'
output: github_document
---
[](https://travis-ci.org/jamesdunham/dgo)
[](https://ci.appveyor.com/project/jamesdunham/dgo)
[](https://codecov.io/gh/jamesdunham/dgo)
# Introduction
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](http://pan.oxfordjournals.org/content/early/2015/02/04/pan.mpu021.full.pdf+html).
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](http://stat.columbia.edu/~gelman/research/published/StateOpinionsNationalPolls.050712.dkp.pdf)).
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.
```{r, knitr-options, echo = FALSE}
# rmarkdown::render("README.Rmd")
knitr::opts_chunk$set(
echo = TRUE,
collapse = TRUE,
comment = "#>",
cache = TRUE,
cache.path = 'readme-cache/',
fig.path = "README/")
```
# Installation
dgo can be installed from [CRAN](https://CRAN.R-project.org/package=dgo):
```{r, eval = FALSE}
install.packages("dgo")
```
Or get the latest version from [GitHub](https://github.com/jamesdunham/dgo)
using [devtools](https://github.com/hadley/devtools/):
```{r, eval = FALSE}
if (!require(devtools, quietly = TRUE)) install.packages("devtools")
devtools::install_github("jamesdunham/dgo")
```
dgo requires a working installation of [RStan](http://mc-stan.org/interfaces/rstan.html).
If you don't have already have RStan, follow its
"[Getting Started](https://github.com/stan-dev/rstan/wiki/RStan-Getting-Started)" guide.
# Usage
Load the package and set RStan's recommended options for a local, multicore
machine with excess RAM:
```{r, result = 'hide', message = FALSE}
library(dgo)
rstan_options(auto_write = TRUE)
options(mc.cores = parallel::detectCores())
```
The minimal workflow from raw data to estimation is:
1. shape input data using the `shape()` function; and
2. pass the result to the `dgirt()` function to estimate a latent trait (e.g.,
conservatism) or `dgmrp()` function to estimate opinion on a single survey
question.
# Troubleshooting
Please [report issues](https://github.com/jamesdunham/dgo/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](https://stackoverflow.com/questions/17107206/change-temporary-directory).
Confirm the new path before starting your model run by restarting R and
checking the output from `tempdir()`.
* Models fitted before October 2016 (specifically <
[#8e6a2cf](https://github.com/jamesdunham/dgo/commit/8e6a2cfbe00b2cd4a908b3067241e06124d143cd))
using dgirt are not fully compatible with dgo. Their contents can be
extracted without using dgo, however, with the `$` indexing operator. For
example: `as.data.frame(dgirtfit_object$stan.cmb)`.
* Calling `dgirt()` or `dgmrp()` can generate
[warnings](http://mc-stan.org/misc/warnings#compiler-warnings) during model
compilation. These are safe to ignore, or can be suppressed by following the
linked instructions.
# Contributing and citing
dgo is under development and we welcome
[suggestions](https://github.com/jamesdunham/dgo/issues).
The package citation is:
Dunham, James, Devin Caughey, and Christopher Warshaw. 2018. dgo: Dynamic
Estimation of Group-level Opinion. R package. https://jdunham.io/dgo/.