Latent Markov (LM) models represent an important class of models for the analysis of longitudinal data (Bartolucci et. al., 2013), especially when response variables are categorical. These models have a great potential of application for the analysis of social, medical, and behavioral data as well as in other disciplines. We propose the R package LMest, which is tailored to deal with these types of model. In particular, we consider a general framework for extended LM models by including individual covariates and by formulating a mixed approach to take into account additional dependence structures in the data. Such extensions lead to a very flexible class of models, which allows us to fit different types of longitudinal data. Model parameters are estimated through the expectation-maximization algorithm, based on the forward-backward recursions, which is implemented in the main functions of the package. The package also allows us to perform local and global decoding and to obtain standard errors for the parameter estimates. We illustrate its use and the most important features on the basis of examples involving applications in health and criminology.

Bartolucci, F., Farcomeni, A., Pandolfi, S., Pennoni, F. (2015). LMest: an R package for latent Markov models for categorical longitudinal data [Rapporto tecnico].

LMest: an R package for latent Markov models for categorical longitudinal data

PENNONI, FULVIA
2015

Abstract

Latent Markov (LM) models represent an important class of models for the analysis of longitudinal data (Bartolucci et. al., 2013), especially when response variables are categorical. These models have a great potential of application for the analysis of social, medical, and behavioral data as well as in other disciplines. We propose the R package LMest, which is tailored to deal with these types of model. In particular, we consider a general framework for extended LM models by including individual covariates and by formulating a mixed approach to take into account additional dependence structures in the data. Such extensions lead to a very flexible class of models, which allows us to fit different types of longitudinal data. Model parameters are estimated through the expectation-maximization algorithm, based on the forward-backward recursions, which is implemented in the main functions of the package. The package also allows us to perform local and global decoding and to obtain standard errors for the parameter estimates. We illustrate its use and the most important features on the basis of examples involving applications in health and criminology.
Rapporto tecnico
expectation-maximization algorithm, forward-backward recursions, hidden Markov model
English
2015
1
41
http://arxiv.org/abs/1501.04448
Bartolucci, F., Farcomeni, A., Pandolfi, S., Pennoni, F. (2015). LMest: an R package for latent Markov models for categorical longitudinal data [Rapporto tecnico].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/69728
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