est_multi_glob_genZ {MLCIRTwithin}

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_genZ(Y, X, model = c("m","l","g"), ind = 1:nrow(Y), de = NULL,
                    Z = NULL, z = NULL, Dis = NULL, dis = NULL, disp=FALSE,
                    only_sc = FALSE, Int = NULL, der_single = FALSE, maxit = 10)

Arguments

Y

matrix of response configurations

X

array of all distinct covariate configurations

model

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

ind

vector to link responses to covariates

de

initial vector of regression coefficients

Z

design matrix

z

intercept associated with the design matrix

Dis

matrix for inequality constraints on de

dis

vector for inequality constraints on de

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

score for the vector of regression coefficients

FI

Fisher information matrix

de

estimated vector of (free) regression coefficients

scde

score for the vector of (free) regression coefficients

FIde

Fisher information matrix for the vector of (free) regression coefficients

Sc

matrix of individual scores for the vector of regression coefficients (if der_single=TRUE)

Scde

matrix of individual scores for the vector of (free) regression coefficients (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.