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)matrix of response configurations
array of all distinct covariate configurations
type of logit (g = global, l = local, m = multinomial)
vector to link responses to covariates
initial vector of regression coefficients
matrix for inequality constraints on be
vector for inequality constraints on be
to display partial output
to exit giving only the score
matrix of the fixed intercepts
to require single derivatives
estimated vector of regression coefficients
log-likelihood at convergence
matrix of the probabilities for each distinct covariate configuration
matrix of the probabilities for each covariate configuration
score
single derivative (if der_single=TRUE)
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.