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Analyzing Spatial Models of Choice and Judgment with R

Chapter 7: Advanced Topics

Code Files:



latent_RHS_model.bug: JAGS/BUGS code that prevents the measurement model from conditioning on the predictive model, "cutting" the latent variable (Ideology).



summary.chainreg.r: summary.chainreg function.



mimic_allvotes.r: MIMIC model for all votes in the 111th House.

JAGS/BUGS code: mimic_allvotes.bug



mimic_keyvotes.r: MIMIC model for only key votes in the 111th House.

JAGS/BUGS code: mimic_keyvotes.bug



mimic_keyvotes_plus_information.r: MIMIC model for key votes plus legislator information in the 111th House.

JAGS/BUGS code: mimic_keyvotes_plus_information.bug

Dataset: H111.dta



ordinalIRT_ANES2004.r: Runs ordinal IRT on issue scale data from the 2004 American National Election Study.

Dataset: ANES2004.Rda



dynamicIRT_ukcmp.r: Runs dynamic IRT on UK manifesto data.

Dataset: uk_cmp_2012.dta



Supplemental Links:



MCMCpack for Bayesian Inference in R

Book Chapters

Analyzing Spatial Models of Choice and Judgment with R
Chapter 1: Introduction
Chapter 2: The Basics of R
Chapter 3: Analyzing Issue Scales
Chapter 4: Analyzing Similarities and Dissimilarities Data
Chapter 5: Unfolding Analysis of Rank Order and Ratio Scale Data
Chapter 6: Analyzing Legislative Roll Call (Binary Choice) Data
Chapter 7: Advance Topics


Site Links

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NOMINATE Data, Roll Call Data, and Software
Course Web Pages: University of Georgia (2010 - )
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Course Web Pages: University of Houston (2000 - 2005)
Course Web Pages: Carnegie-Mellon University (1997 - 2000)
Analyzing Spatial Models of Choice and Judgment with R
Spatial Models of Parliamentary Voting
Recent Working Papers
Analyses of Recent Politics
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K7MOA Log Books: 1960 - 2017
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