est_multi_glob function

Fit marginal regression models for categorical responses

It estimates marginal regression models to datasets consisting of a categorical response and one or more covariates by a Fisher-scoring algorithm; this is an internal function.

est_multi_glob(Y, X, model, ind = 1:nrow(Y), be = NULL, Dis = NULL,
               dis = NULL, disp=FALSE, only_sc = FALSE, Int = NULL,
               der_single = FALSE)

Arguments

• Y: matrix of response configurations
• X: array of all distinct covariate configurations
• model: type of logit (g = global, l = local, m = multinomial)
• ind: vector to link responses to covariates
• be: initial vector of regression coefficients
• Dis: matrix for inequality constraints on be
• dis: vector for inequality constraints on be
• disp: to display partial output
• only_sc: to exit giving only the score
• Int: matrix of the fixed intercepts
• der_single: to require single derivatives

Returns

• be: estimated vector of regression coefficients

• lk: log-likelihood at convergence

• Pdis: matrix of the probabilities for each distinct covariate configuration

• P: matrix of the probabilities for each covariate configuration

• sc: score

• Sc: single derivative (if der_single=TRUE)

References

Colombi, R. and Forcina, A. (2001), Marginal regression models for the analysis of positive association of ordinal response variables, Biometrika, 88 , 1007-1019.

Glonek, G. F. V. and McCullagh, P. (1995), Multivariate logistic models, Journal of the Royal Statistical Society, Series B, 57 , 533-546.

Author(s)

Francesco Bartolucci - University of Perugia (IT)