X_votes

This data.frame includes logarithmized data on population casting votes, of the
population that is 25 years and older who completed 12th grade or higher 
education, on the number of owner-occupied housing units, and of aggregate 
income per county in Alabama, US. This data.frame can be specified as covariate
matrix in the network autocorrelation model to predict the voter turnout in 
Alabama.

datasets

The network autocorrelation model (NAM) can be used for studying the degree of social influence
regarding an outcome variable based on one or more known networks.
The degree of social influence is quantified via the network autocorrelation parameters. In case of a single
network, the Bayesian methods of Dittrich, Leenders, and Mulder
(2017) <DOI:10.1016/j.socnet.2016.09.002> and Dittrich, Leenders, and Mulder (2019)
<DOI:10.1177/0049124117729712> are implemented using a normal, flat, or independence
Jeffreys prior for the network autocorrelation. In the case of multiple
networks, the Bayesian methods of Dittrich, Leenders, and Mulder (2020)
<DOI:10.1177/0081175020913899> are implemented using a multivariate normal prior for
the network autocorrelation parameters. Flat priors are implemented
for estimating the coefficients. For Bayesian testing of equality and order-constrained
hypotheses, the default Bayes factor of Gu, Mulder, and Hoijtink, (2018)
<DOI:10.1111/bmsp.12110> is used with the posterior mean and posterior covariance
matrix of the NAM parameters based on flat priors as input.

Joris Mulder

BANAM

Bayesian Analysis of the Network Autocorrelation Model

Dino Dittrich

Roger Leenders

X_votes function

A data.frame with 67 rows and 4 columns
<table class="table">
<tr><td>pop_eligible</td><td><code>numeric</code></td><td>Logarithm of the population casting votes</td></tr>
<tr><td>pop_college</td><td><code>numeric</code></td><td>Logarithm of the population that is 25 years and older who completed 12th grade or higher education</td></tr>
<tr><td>homeownership</td><td><code>numeric</code></td><td>Logarithm of the number of owner-occupied housing units</td></tr>
<tr><td>income</td><td><code>numeric</code></td><td>Logarithmized aggregate income per county</td></tr>
</table>

Format

Covariate data frame for the Alabama voter turnout data — X_votes

A data.frame with 67 rows and 4 columns
<table class='table'>
<tr><td>pop_eligible</td><td><code>numeric</code></td><td>Logarithm of the population casting votes</td></tr>
<tr><td>pop_college</td><td><code>numeric</code></td><td>Logarithm of the population that is 25 years and older who completed 12th grade or higher education</td></tr>
<tr><td>homeownership</td><td><code>numeric</code></td><td>Logarithm of the number of owner-occupied housing units</td></tr>
<tr><td>income</td><td><code>numeric</code></td><td>Logarithmized aggregate income per county</td></tr>
</table>

pop_eligible	`numeric`	Logarithm of the population casting votes
pop_college	`numeric`	Logarithm of the population that is 25 years and older who completed 12th grade or higher education
homeownership	`numeric`	Logarithm of the number of owner-occupied housing units
income	`numeric`	Logarithmized aggregate income per county

X_votes: Covariate data frame for the Alabama voter turnout data

Description

Usage

Arguments

Format

References