This article presents a logit model for a vector of binary variables observed for each subject under multiple conditions. The model contains a vector of random effects to account for correlations among the repeated measurements. The fit yields estimates of within-subject effects comparing the conditions for each variable. The estimates are identical to conditional maximum likelihood estimates for a fixed-effects version of the logit model.
Extensions incorporate independent groups or allow variables to have multiple response categories.
Alan Agresti is Professor, Department of Statistics, University of Florida, Gainesville, FL 32611. This work was partially supported by a grant from the National Institutes of Health. The author thanks Martin Knott and Atalanta Ghosh for helpful discussions, and acknowledges the hospitality of the Statistics Department at London School of Economics and Political Science, where part of this work was conducted during a sabbatical. Copyright 1997 American Statistical Association. All rights reserved.