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Contents

• • Introduction
    • What Zelig and R Do
    • Getting Help
  
  
• User's Guide
  • Installation
    • Windows
    • MacOS X
    • UNIX and Linux
    • Version Compatability
  • Data Analysis Commands
    • Command Syntax
    • Data Sets
    • Variables
  • Statistical Commands
    • Zelig Commands
    • Supported Models
    • Replication Procedures
  • Graphing Commands
    • Drawing Plots
    • Adding Points, Lines, and Legends to Existing Plots
    • Saving Graphs to Files
    • Examples
  
  
• Advanced Zelig Uses
  • R Objects
    • Scalar Values
    • Data Structures
  • Programming Statements
    • Functions
    • If-Statements
    • For-Loops
  • Writing New Models
    • Managing Statistical Model Inputs
    • Easy Ways to Manage Matrices
  • Adding Models and Methods to Zelig
    • Making the Model Compatible with Zelig
    • Getting Ready for the GUI
    • Formatting Reference Manual Pages
  
  
• Reference Manual
  • Main Commands
    • zelig: Estimating a Statistical Model
    • setx: Setting Explanatory Variable Values
    • sim: Simulating Quantities of Interest
    • sim: Simulating Quantities of Interest
    • plot.zelig: Graphing Quantities of Interest
    • print: Printing Quantities of Interest
    • matchit: Create matched data
    • mi: Bundle multiply imputed data sets as a list
    • network: Format matricies into a data frame for social network analysis
    • plot.ci: Plotting Vertical confidence Intervals
    • rocplot: Receiver Operator Characteristic Plots
    • ternaryplot: Ternary diagram
    • ternarypoints: Adding Points to Ternary Diagrams
  • Models Zelig Can Run
    • ARIMA: ARIMA Models for Time Series Data
    • blogit: Bivariate Logistic Regression for Two Dichotomous Dependent Variables
    • bprobit: Bivariate Logistic Regression for Two Dichotomous Dependent Variables
    • ei.dynamic: Quinn's Dynamic Ecological Inference Model
    • ei.hier: Hierarchical Ecological Inference Model for Tables
    • ei.RxC: Hierarchical Multinomial-Dirichlet Ecological Inference Model for Tables
    • exp: Exponential Regression for Duration Dependent Variables
    • factor.bayes: Bayesian Factor Analysis
  
  
• Bibliography
  • factor.mix: Mixed Data Factor Analysis
  • factor.ord: Ordinal Data Factor Analysis
  • gamma: Gamma Regression for Continuous, Positive Dependent Variables
  • irt1d: One Dimensional Item Response Model
  • irtkd: -Dimensional Item Response Theory Model
  • logit: Logistic Regression for Dichotomous Dependent Variables
  • logit.bayes: Bayesian Logistic Regression
  • lognorm: Log-Normal Regression for Duration Dependent Variables
  • ls: Least Squares Regression for Continuous Dependent Variables
  • mlogit: Multinomial Logistic Regression for Dependent Variables with Unordered Categorical Values
  • mlogit.bayes: Bayesian Multinomial Logistic Regression
  • mloglm: Multinomial Log-Linear Regression for Contingency Table Models
  • negbin: Negative Binomial Regression for Event Count Dependent Variables
  • netls: Network Least Squares Regression for Continuous Proximity Matrix Dependent Variables
  • netlogit: Network Logistic Regression for Dichotomous Proximity Matrix Dependent Variables
  • normal: Normal Regression for Continuous Dependent Variables
  • normal.bayes: Bayesian Normal Linear Regression
  • ologit: Ordinal Logistic Regression for Ordered Categorical Dependent Variables
  • oprobit: Ordinal Probit Regression for Ordered Categorical Dependent Variables
  • oprobit.bayes: Bayesian Ordered Probit Regression
  • poisson: Poisson Regression for Event Count Dependent Variables
  • poisson.bayes: Bayesian Poisson Regression
  • probit: Probit Regression for Dichotomous Dependent Variables
  • probit.bayes: Bayesian Probit Regression
  • relogit: Rare Events Logistic Regression for Dichotomous Dependent Variables
  • tobit: Linear Regression for a Left-Censored Dependent Variable
  • tobit.bayes: Bayesian Linear Regression for a Censored Dependent Variable
  • weibull: Weibull Regression for Duration Dependent Variables
  • Commands for Programmers and Contributors
    • describe: Describe a model's systematic and stochastic parameters
    • model.end: Cleaning up after optimization
    • model.frame.multiple: Extracting the ``environment'' of a model formula
    • model.matrix.multiple: Design matrix for multivariate models
    • parse.formula: Parsing the inputs
    • parse.par: Select and reshape parameter vectors
    • put.start: Set specific starting values for certain parameters
    • set.start: Set starting values for all parameters
    • tag: Constrain parameter effects across equations
  
  
• Appendices
  • Frequently Asked Questions
    • For All Zelig Users
    • For Zelig Contributors
  • What's New? What's Next?
    • What's New: Zelig Release Notes
    • What's Next?
  
  
• Bibliography