norm_anom: alpha-NOMINATE Result for Simulated Roll Call Matrix using Normal Utility

Description

An object of class anominate that contains the estimated alpha-NOMINATE result for data simulated using normal (Gaussian) utility. Although it can easily be obtained from calling the example in simulateData, it is included here to facilitate illustration of the examples for the plot and summary functions.

Usage

data(norm_anom)

Arguments

Value

An object of class anominate, which in this documentation is also referred to as an alpha-NOMINATE object.

alpha: An object of class mcmc with the sampled values of the alpha parameter.
beta: An object of class mcmc with the sampled values of the beta parameter.
legislators: A object of class mcmc with the sampled values of the legislator ideal points, with each dimension stored in a separate list (e.g., the first dimension coordinates are stored in legislators[[1]], the second dimension coordinates in legislators[[2]], etc.).
yea.locations: A object of class mcmc with the sampled values of the Yea locations (midpoint - spread in W-NOMINATE) for each vote, with each dimension stored in a separate list (e.g., the first dimension coordinates are stored in yea.locations[[1]], the second dimension coordinates in yea.locations[[2]], etc.).
nay.locations: A object of class mcmc with the sampled values of the Nay locations (midpoint + spread in W-NOMINATE) for each vote, with each dimension stored in a separate list (e.g., the first dimension coordinates are stored in nay.locations[[1]], the second dimension coordinates in nay.locations[[2]], etc.).
wnom.result: An object of class nomObject with the W-NOMINATE (wnominate) results.

Author

Royce Carroll rcarroll@rice.edu

Christopher Hare cdhare@ucdavis.edu

Jeffrey B. Lewis jblewis@ucla.edu

James Lo lo@uni-mannheim.de

Keith T. Poole ktpoole@uga.edu

Howard Rosenthal hl31@nyu.edu

Examples

Run this code

  # \donttest{
  normal.data <- simulateData(utility="normal") 
  norm_anom <- anominate(normal.data, dims=1, polarity=1, 
    nsamp=200, thin=1, burnin=100, random.starts=FALSE, 
    verbose=FALSE, constrain=FALSE)
  # }
  # 'norm_anom' can be retrieved quickly with: 
  data(norm_anom)
  
  summary(norm_anom)
  plot(norm_anom)