est_multi_glob_genZ: Fit marginal regression models for categorical responses

In MLCIRTwithin: Latent Class Item Response Theory (LC-IRT) Models under Within-Item Multidimensionality

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.

MLCIRTwithin documentation built on Sept. 30, 2019, 5:04 p.m.