Mixture Hidden Markov Models for Sequence Data: The seqHMM Package in R

Abstract

Sequence analysis is being more and more widely used for the analysis of social sequences and other multivariate categorical time series data. However, it is often complex to describe, visualize, and compare large sequence data, especially when there are multiple parallel sequences per subject. Hidden (latent) Markov models (HMMs) are able to detect underlying latent structures and they can be used in various longitudinal settings: to account for measurement error, to detect unobservable states, or to compress information across several types of observations. Extending to mixture hidden Markov models (MHMMs) allows clustering data into homogeneous subsets, with or without external covariates.The seqHMM package in R is designed for the efficient modeling of sequences and other categorical time series data containing one or multiple subjects with one or multiple interdependent sequences using HMMs and MHMMs. Also other restricted variants of the MHMM can be fitted, e.g., latent class models, Markov models, mixture Markov models, or even ordinary multinomial regression models with suitable parameterization of the HMM.Good graphical presentations of data and models are useful during the whole analysis process from the first glimpse at the data to model fitting and presentation of results. The package provides easy options for plotting parallel sequence data, and proposes visualizing HMMs as directed graphs.

Within and Between-person Correlates of the Temporal Dynamics of Resting EEG Microstates

“…Transition probabilities and microstate sequence analysis. We also calculated first order Markov-chain transition probabilities from the time series of microstates using the R package seqHMM (Helske and Helske, 2019). The probability that a given microstate would transition to another microstate configuration was calculated for each pair of microstate configurations for each individual recording.…”

SpaMHMM: Sparse Mixture of Hidden Markov Models for Graph Connected Entities

“…When the expressiveness of an HMM is not enough, mixtures of HMMs have been adopted. Roughly speaking, mixtures of HMMs can be interpreted as the result of the combination of a set of independent standard HMMs which are observed through a memoryless transformation [3], [4], [5], [6].…”

Sequence Analysis: Where Are We, Where Are We Going?

“…The contributions of Piccarreta and Elzinga (2013) and Piccarreta (2017) are path-breaking in that respect. The graphical rendering of multichannel sequences also requires special attention and effective solutions are, for example, provided by Helske and Helske (2017) and their seqHMM package.…”

“…With regards to clustering, there are attempts to get rid of explicit dissimilarity measures by resorting to latent class approaches (Vermunt et al 2008;Barban and Billari 2012), or more or less similarly hidden Markov models (e.g., Helske and Helske 2017;Bolano et al 2016), to clustering the sequences. Markov-based approaches may also contribute to understanding the dynamics that drive the unfolding of the sequences.…”

“…However, the difficulty to synthesize the outcome of Markov-based transition models-especially when more realistic models with order greater than one are considered-negatively affected the extension of their usage. The graphical rendering of hidden Markov models (HMM) by Helske and Helske (2017) (see also Helske et al 2018, in this bundle) as well as the use and rendering of probabilistic suffix trees (Gabadinho and Ritschard 2016) for sequence analysis should facilitate the access to such probabilistic approaches and shed light on how the current situation is linked to the history of previous situations.…”