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 |

`rcObject` |
An roll call matrix of class |

`dims` |
Number of dimensions to estimate |

`nsamp` |
Total number of iterations for the sampler. |

`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, |

`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 |

`verbose` |
If |

`constrain` |
If |

A list with the following elements:

`alpha` |
An object of class |

`beta` |
An object of class |

`legislators` |
A object of class |

`yea.locations` |
A object of class |

`nay.locations` |
A object of class |

`wnom.result` |
An object of class |

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'.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 | ```
data(sen111)
### This command conducts estimates, which we instead load using data()
#sen111_anom <- anominate(sen111, dims=1, polarity=2, nsamp=200, thin=1,
# burnin=100, random.starts=FALSE, verbose=TRUE)
data(sen111_anom)
summary(sen111_anom)
## Graphical summaries
plot(sen111_anom)
densplot.anominate(sen111_anom)
traceplot.anominate(sen111_anom)
``` |

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