anominate: alpha-NOMINATE: Ideal Point Estimator
In anominate: Alpha-NOMINATE Ideal Point Estimator

Description Usage Arguments Value Author(s) References See Also Examples

Fits the Bayesian (MCMC-based) version of Poole and Rosenthal's NOMINATE model described in Carroll, Lewis, Lo, Poole and Rosenthal, “The Structure of Utility in Spatial Models of Voting,” American Journal of Political Science 57(4): 1008–1028. The method estimates the structure of political actors' utility functions from roll call data with the inclusion of a separate parameter denoted as alpha. alpha values of 1 indicate normal (Gaussian) utility, while alpha values of 0 indicate quadratic utility.

1 2	anominate(rcObject, dims=1, nsamp=1000, thin=1, burnin=500, minvotes=20, lop=0.025, polarity=1, random.starts=TRUE, verbose=FALSE, constrain=FALSE)

`rcObject`	An roll call matrix of class `rollcall`, from Simon Jackman's `pscl` package
`dims`	Number of dimensions to estimate
`nsamp`	Total number of iterations for the sampler. `nsamp` divided by `thin` must be larger than `burnin`.
`thin`	Thinning interval
`burnin`	Number of iterations to be discarded
`minvotes`	Minimum number of votes required for a legislator to be included in the analysis
`lop`	A proportion between 0 and 1, the cut-off used for excluding lopsided votes, expressed as the proportion of non-missing votes on the minority side. The default, `lop=0.025`, eliminates votes where the minority is smaller than 2.5 percent
`polarity`	A vector specifying the row number of the legislator(s) constrained to have a positive (i.e., right-wing or conservative) score on each dimension
`random.starts`	If `TRUE`, initial values for the legislator and bill parameters are randomly drawn from a uniform distribution. If `FALSE`, the W-NOMINATE estimates are used as the initial values
`verbose`	If `TRUE`, the progress of the sampler at each 100th iteration is printed to the screen
`constrain`	If `TRUE`, this constrains alpha=1 and does not estimate it. This option should be used if more than one dimension is being estimated, which makes the method equivalent to a Bayesian implementation of Poole and Rosenthal's (1997) NOMINATE model.

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

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

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.

Poole, Keith T. and Howard Rosenthal. 1997. Congress: A Political-Economic History of Roll Call Voting. New York: Oxford University Press.

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

## Not run: 
data(sen111)
sen111_anom <- anominate(sen111, dims=1, polarity=2, nsamp=200, thin=1,
	burnin=100, random.starts=FALSE, verbose=TRUE)
summary(sen111_anom)
## Graphical summaries
plot(sen111_anom)
densplot.anominate(sen111_anom)
traceplot.anominate(sen111_anom)

## End(Not run)