est_multi_glob: Fit marginal regression models for categorical responses

In MultiLCIRT: Multidimensional Latent Class Item Response Theory Models

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.

MultiLCIRT documentation built on May 2, 2019, 2:38 p.m.