alpha-NOMINATE Estimate, Simulated Roll Call Matrix using Normal Utility

Description

alpha-NOMINATE estimates from simulated roll call matrix using normal utility. Estimates in one dimension.

Usage

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Value

An object of class anominate, a list with the following elements:

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 results

Author(s)

Christopher Hare, Royce Carroll, Jeffrey B. Lewis, James Lo, Keith T. Poole and Howard Rosenthal

References

Carroll, Royce, Jeffrey B. Lewis, James Lo, Keith T. Poole and Howard Rosenthal. 2013. “The Structure of Utility in Spatial Models of Voting.” American Journal of Political Science 57(4): 1008–1028.

See Also

'anominate.sim','anominate','summary.anominate','plot.anominate','densplot.anominate','traceplot.anominate'.

Examples

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normal.data <- anominate.sim(utility="normal") 
### This command conducts estimates, which we instead load using data()
#norm_anom <- anominate(normal.data, dims=1, polarity=2, nsamp=200, thin=1, 
#	burnin=100, random.starts=FALSE, verbose=TRUE)
data(norm_anom)
summary(norm_anom)