Two Random Effects Models for Multivariate Binary Data

Biometrics ◽  
1994 ◽  
Vol 50 (1) ◽  
pp. 164 ◽  
Author(s):  
Barry W. McDonald
Keyword(s):  
Random Effects ◽  
Binary Data ◽  
Random Effects Models ◽  
Multivariate Binary Data

scholarly journals Intra-class Correlation in Random-effects Models for Binary Data

2003 ◽  
Vol 3 (1) ◽  
pp. 32-46 ◽  
Author(s):  
Germán Rodríguez ◽  
Irma Elo
Keyword(s):  
Random Effects ◽  
Latent Variable ◽  
Binary Data ◽  
Binary Outcomes ◽  
Measures Of Association ◽  
Intra Class Correlation ◽  
Random Effects Models ◽  
Alternative Measures

We review the concept of intra-class correlation in random-effects models for binary outcomes as estimated by Stata's xtprobit, xtlogit, and xtclog. We consider the usual measures of correlation based on a latent variable formulation of these models and note corrections to the last two procedures. We also discuss alternative measures of association based on manifest variables or actual outcomes and introduce a new command xtrho for computing these measures for all three types of models.

scholarly journals Modeling clustered binary data with excess zero clusters

2016 ◽  
Vol 27 (9) ◽  
pp. 2641-2656
Author(s):  
John Kwagyan ◽  
Victor Apprey
Keyword(s):  
Random Effects ◽  
Binary Data ◽  
Latent Class ◽  
Marginal Likelihood ◽  
Gaussian Quadrature ◽  
Gaussian Model ◽  
Likelihood Estimation ◽  
Clustered Binary Data ◽  
Random Effects Models ◽  
Cancer Data

We establish a zero-inflated (random-effects) logistic-Gaussian model for clustered binary data in which members of clusters in one latent class have a zero response with probability one, and members of clusters in a second latent class yield correlated outcomes. Response probabilities in terms of random-effects models are formulated, and maximum marginal likelihood estimation procedures based on Gaussian quadrature are developed. Application to esophageal cancer data in Chinese families is presented.

Marginalized random effects models for multivariate longitudinal binary data

2009 ◽  
Vol 28 (8) ◽  
pp. 1284-1300 ◽  
Author(s):  
Keunbaik Lee ◽  
Yongsung Joo ◽  
Jae Keun Yoo ◽  
JungBok Lee
Keyword(s):  
Random Effects ◽  
Binary Data ◽  
Random Effects Models ◽  
Longitudinal Binary Data

Positional Estimation Within a Latent Space Model for Networks

Methodology ◽  
2006 ◽  
Vol 2 (1) ◽  
pp. 24-33 ◽  
Author(s):  
Susan Shortreed ◽  
Mark S. Handcock ◽  
Peter Hoff
Keyword(s):  
Random Effects ◽  
Network Data ◽  
Random Effects Models ◽  
Space Model ◽  
Network Analyses ◽  
Recent Advances ◽  
Latent Space ◽  
Latent Space Model ◽  
Modeling Data

Recent advances in latent space and related random effects models hold much promise for representing network data. The inherent dependency between ties in a network makes modeling data of this type difficult. In this article we consider a recently developed latent space model that is particularly appropriate for the visualization of networks. We suggest a new estimator of the latent positions and perform two network analyses, comparing four alternative estimators. We demonstrate a method of checking the validity of the positional estimates. These estimators are implemented via a package in the freeware statistical language R. The package allows researchers to efficiently fit the latent space model to data and to visualize the results.

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