est_multi_glob {MultiLCIRT}

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.

Usage

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

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

score

Sc

single derivative (if der_single=TRUE)

Author(s)

Francesco Bartolucci - University of Perugia (IT)

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.