Two recent papers have suggested methods for generating correlated binary data with fixed marginal distributions and specified degrees of pairwise association. Emrich and Piedmonte suggested a method based on the existence of a multivariate normal distribution, while Lee suggested methods based on linear programming and Archimedian copulas. In this paper, a simpler method is described using the iterative proportional fitting algorithm for generating an n-dimensional distribution of correlated categorical data with specified margins of dimension 1, 2, …, k < n. An example of generating a distribution for a generalized estimating equations (GEE) model is discussed.
- Correlated outcomes
- Generalized estimating equations
- Loglinear models
- Random number generation
ASJC Scopus subject areas
- Statistics and Probability
- Statistics, Probability and Uncertainty