Description Details Author(s) References See Also
This package contains functions for likelihood and posterior analysis of conditionally specified logistic regression models.
Assume that for each of n experimental units the values of m binary variables
Yi1, …, Yim
are recorded. The 'MleCslogistic' and 'BayesCslogistic' functions fit a conditional specified logistic regression model, such that for i = 1, …, n and j = 1, …, m,
logit P(Yij=1 | Yik=yk, k neq j) = Xij β j + ∑_{k=1, k \neq j} αjk yk
where, the parameters αjk have interpretation as conditional log-odds ratios and the parameters β j correspond to the regression coefficients associated to the vector of covariates Xij. For compatibility of conditional distributions it is assumed that αjk = αkj, j \neq k .
Alejandro Jara Vallejos Alejandro.JaraVallejos@med.kuleuven.be
Maria Jose Garcia-Zattera MariaJose.GarciaZattera@med.kuleuven.be
Garcia-Zattera, M. J., Jara, A., Lesaffre, E. and Declerck, D. (2005). On conditional independence for multivariate binary data in caries research. In preparation.
Joe, H. and Liu, Y. (1996). A model for multivariate response with covariates based on compatible conditionally specified logistic regressions. Satistics & Probability Letters 31: 113-120.
MleCslogistic
, BayesCslogistic
.
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