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pscl (version 1.4.6)

Political Science Computational Laboratory, Stanford University

Description

Bayesian analysis of item-response theory (IRT) models, roll call analysis; computing highest density regions; maximum likelihood estimation of zero-inflated and hurdle models for count data; goodness-of-fit measures for GLMs; data sets used in writing and teaching at the Political Science Computational Laboratory; seats-votes curves.

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Version

Install

install.packages('pscl')

Monthly Downloads

27,194

Version

1.4.6

License

GPL-2

Maintainer

Simon Jackman

Last Published

August 27th, 2014

Functions in pscl (1.4.6)

extractRollCallObject

return the roll call object used in fitting an ideal model

Voter turnout experiment, using Rock The Vote ads

remap MCMC output via affine transformations

inverse-Gamma distribution

nicely formatted tables

Testing for the Presence of a Zero Hurdle

convert entries in a rollcall matrix to binary form

AustralianElectionPolling

Political opinion polls in Australia, 2004-07

article production by graduate students in biochemistry Ph.D. programs

Prussian army horse kick data

drop user-specified elements from a rollcall object

votes from the United States Supreme Court, from 1994-1997

plots an ideal object

Batting Averages for 18 major league baseball players, 1970

read roll call data in Poole-Rosenthal KH format

Hurdle Models for Count Data Regression

plot.seatsVotes

plot seats-votes curves

AustralianElections

elections to Australian House of Representatives, 1949-2007

information about the American states needed for U.S. Congress

predict.zeroinfl

Methods for zeroinfl Objects

summary.rollcall

summarize a rollcall object

compute predicted probabilities from fitted models

politicalInformation

Interviewer ratings of respondent levels of political information

summary of an ideal object

Methods for hurdle Objects

constrain.items

constrain item parameters in analysis of roll call data

Table of Actual Outcomes against Predicted Outcomes for discrete data models

Control Parameters for Hurdle Count Data Regression

UKHouseOfCommons

1992 United Kingdom electoral returns

plot.predict.ideal

plot methods for predictions from ideal objects

Zero-inflated Count Data Regression

cross national rates of trade union density

drop unanimous votes from rollcall objects and matrices

trace plot of MCMC iterates, posterior density of legislators' ideal points

presidentialElections

elections for U.S. President, 1932-2012, by state

Applications to a Political Science PhD Program

add information about voting outcomes to a rollcall object

Absentee and Machine Ballots in Pennsylvania State Senate Races

compute various pseudo-R2 measures

Predicted Probabilties for GLM Fits

Reports of voting in the 1992 U.S. Presidential election.

Vuong's non-nested hypothesis test

constrain.legis

constrain legislators' ideal points in analysis of roll call data

California Congressional Districts in 2006

analysis of educational testing data and roll call data with IRT models, via Markov chain Monte Carlo methods

U.S. Senate vote on the use of force against Iraq, 2002.

rollcall object, National Journal key votes of 2007

predicted probabilities from fitting ideal to rollcall data

predicted probabilities from an ideal object

rollcall object, 109th U.S. Senate (2005-06).

A class for creating seats-votes curves

likelihood ratio test for over-dispersion in count data

vectorRepresentation

convert roll call matrix to series of vectors

Monte Carlo estimate of pi (3.14159265...)

compute and optionally plot beta HDRs

convert an object of class ideal to a coda MCMC object

create an object of class rollcall

political parties appearing in the U.S. Congress

zeroinfl.control

Control Parameters for Zero-inflated Count Data Regression