| mult.latent.reg {mult.latent.reg} | R Documentation |
Regression and Clustering in Multivariate Response Scenarios
Description
This package implements methodology for the estimation of multivariate response models with random effects on one or two levels;
whereby the (one-dimensional) random effect represents a latent variable approximating the multivariate space of outcomes,
after possible adjustment for covariates. The estimation methodology makes use of a nonparametric maximum likelihood-type approach,
where the random effect distribution is approximated by a discrete mixture, hence allowing the use of the EM algorithm for the estimation of all model parameters.
The method is particularly useful for multivariate,
highly correlated outcome variables with unobserved heterogeneities. Applications include regression with multivariate responses,
as well as multivariate clustering or ranking problems.
The details of the models can be found in Zhang and Einbeck (2024) and Zhang et al. (2023).
The main functions are mult.em_1level and mult.em_2level for the fitting of the raw models, as well as envelope functions
mult.reg_1level and mult.reg_2level which facilitate iterative runs of the algorithm with a view to
finding optimal starting points, with help by function start_em.
Details
Package: mult.latent.reg
Type: Package
License: GPL-3
Author(s)
Yingjuan Zhang <yingjuan.zhang@durham.ac.uk>
Jochen Einbeck
References
Zhang, Y., Einbeck, J., and Drikvandi, R. (2023). A multilevel multivariate response model for data with latent structures. In: Proceedings of the 37th International Workshop on Statistical Modelling, Dortmund; pages 343-348. Link on RG: https://www.researchgate.net/publication/375641972_A_multilevel_multivariate_response_model_for_data_with_latent_structures.
Zhang, Y. and Einbeck, J. (2024). A Versatile Model for Clustered and Highly Correlated Multivariate Data. J Stat Theory Pract 18(5).doi:10.1007/s42519-023-00357-0