Mixtures of Exponential-Distance Models with Covariates.

MEDseq R Package

Mixtures of Exponential-Distance Models

for Clustering Longitudinal Life-Course Sequences

with Gating Covariates and Sampling Weights

Written by Keefe Murphy

Description

Fits MEDseq models introduced by Murphy et al. (2021) <doi:10.1111/rssa.12712>, i.e. fits mixtures of exponential-distance models for clustering longitudinal/categorical life-course sequence data via the EM/CEM algorithm. A family of parsimonious precision parameter constraints are accommodated. So too are sampling weights. Gating covariates can be supplied via formula interfaces. Visualisation of the results of such models is also facilitated.

The most important function in the MEDseq package is: MEDseq_fit, for fitting the models via EM/CEM. This function requires the data to be in "stslist" format; the function seqdef is conveniently reexported from the TraMineR package for this purpose.

MEDseq_control allows supplying additional arguments which govern, among other things, controls on the initialisation of the allocations for the EM/CEM algorithm and the various model selection options. MEDseq_compare is provided for conducting model selection between different results from using different covariate combinations &/or initialisation strategies, etc. MEDseq_stderr is provided for computing the standard errors of the coefficients for the covariates in the gating network.

A dedicated plotting function exists for visualising various aspects of the results, using new methods as well as some existing methods adapted from the TraMineR package. Finally, the package also contains two data sets: biofam and mvad.

Installation

You can install the latest stable official release of the MEDseq package from CRAN:

install.packages("MEDseq")

or the development version from GitHub:

# If required install devtools:  
# install.packages('devtools')  
devtools::install_github('Keefe-Murphy/MEDseq')

In either case, you can then explore the package with:

library(MEDseq)  
help(MEDseq_fit) # Help on the main modelling function

For a more thorough intro, the vignette document is available as follows:

vignette("MEDseq", package="MEDseq")

However, if the package is installed from GitHub the vignette is not automatically created. It can be accessed when installing from GitHub with the code:

devtools::install_github('Keefe-Murphy/MEDseq', build_vignettes = TRUE)

Alternatively, the vignette is available on the package's CRAN page.

References

Murphy, K., Murphy, T. B., Piccarreta, R., and Gormley, I. C. (2021). Clustering longitudinal life-course sequences using mixtures of exponential-distance models. Journal of the Royal Statistical Society: Series A (Statistics in Society), 184(4): 1414--1451. <doi:10.1111/rssa.12712>.