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pscl (version 1.04.1)

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

1.04.1

License

GPL-2

Maintainer

Simon Jackman

Last Published

October 23rd, 2011

Functions in pscl (1.04.1)

Applications to a Political Science PhD Program

Predicted Probabilties for GLM Fits

politicalInformation

Interviewer ratings of respondent levels of political information

predicted probabilities from an ideal object

Zero-inflated Count Data Regression

California Congressional Districts in 2006

AustralianElectionPolling

Political opinion polls in Australia, 2004-07

Voter turnout experiment, using Rock The Vote ads

nicely formatted tables

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

Batting Averages for 18 major league baseball players, 1970

drop unanimous votes from rollcall objects and matrices

create an object of class rollcall

likelihood ratio test for over-dispersion in count data

plots an ideal object

predict.zeroinfl

Methods for zeroinfl Objects

A class for creating seats-votes curves

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

summary.rollcall

summarize a rollcall object

convert entries in a rollcall matrix to binary form

compute and optionally plot beta HDRs

drop user-specified elements from a rollcall object

Testing for the Presence of a Zero Hurdle

rollcall object, National Journal key votes of 2007

AustralianElections

elections to Australian House of Representatives, 1949-2007

constrain.items

constrain item parameters in analysis of roll call data

plot.predict.ideal

plot methods for predictions from ideal objects

Table of Actual Outcomes against Predicted Outcomes for discrete data models

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

convert an object of class ideal to a coda MCMC object

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

zeroinfl.control

Control Parameters for Zero-inflated Count Data Regression

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

predicted probabilities from fitting ideal to rollcall data

presidentialElections

elections for U.S. President, 1932-2008, by state

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

compute various pseudo-R2 measures

Hurdle Models for Count Data Regression

Methods for hurdle Objects

remap MCMC output via affine transformations

compute predicted probabilities from fitted models

UKHouseOfCommons

1992 United Kingdom electoral returns

Monte Carlo estimate of pi (3.14159265...)

Control Parameters for Hurdle Count Data Regression

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

political parties appearing in the U.S. Congress

cross national rates of trade union density

vectorRepresentation

convert roll call matrix to series of vectors

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

summary of an ideal object

Vuong's non-nested hypothesis test

add information about voting outcomes to a rollcall object

Absentee and Machine Ballots in Pennsylvania State Senate Races

constrain.legis

constrain legislators' ideal points in analysis of roll call data

extractRollCallObject

return the roll call object used in fitting an ideal model

inverse-Gamma distribution

Prussian army horse kick data

read roll call data in Poole-Rosenthal KH format

plot.seatsVotes

plot seats-votes curves