est_multi_glob_gen: Fit marginal regression models for categorical responses

Description

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 that also works with response variables having a different number of response categories.

Usage

est_multi_glob_gen(Y, X, model, ind = 1:nrow(Y), be = NULL, Dis = NULL, dis = NULL, disp=FALSE, only_sc = FALSE, Int = NULL, der_single = FALSE, maxit = 10)

Arguments

matrix of response configurations

array of all distinct covariate configurations

model

type of logit (g = global, l = local, m = multinomial)

ind

vector to link responses to covariates

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

maxit

maximum number of iterations

Value

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: single derivative (if der_single=TRUE)
sc: score

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.