A-quick-tour-of-mixHMM

In flamingos: Functional Latent Data Models for Clustering Heterogeneous Curves ('FLaMingos')

library(knitr)
knitr::opts_chunk$set(
    fig.align = "center",
    fig.height = 5.5,
    fig.width = 6,
    warning = FALSE,
    collapse = TRUE,
    dev.args = list(pointsize = 10),
    out.width = "90%",
    par = TRUE
)
knit_hooks$set(par = function(before, options, envir)
  { if (before && options$fig.show != "none") 
       par(family = "sans", mar = c(4.1,4.1,1.1,1.1), mgp = c(3,1,0), tcl = -0.5)
})

library(flamingos)

Introduction

mixHMM: Clustering and segmentation of heterogeneous curves/time series by mixture of gaussian Hidden Markov Models (MixHMMs) fitted by the EM/Baum-Welch algorithm.

It was written in R Markdown, using the knitr package for production.

See help(package="flamingos") for further details and references provided by citation("flamingos").

Load data

data("toydataset")
Y <- t(toydataset[,2:ncol(toydataset)])

Set up mixHMM model parameters

K <- 3 # Number of clusters
R <- 3 # Number of regimes (HMM states)
variance_type <- "heteroskedastic" # "heteroskedastic" or "homoskedastic" model

Set up EM parameters

ordered_states <- TRUE
n_tries <- 1
max_iter <- 1000
init_kmeans <- TRUE
threshold <- 1e-6
verbose <- TRUE

Estimation

mixhmm <- emMixHMM(Y = Y, K, R, variance_type, ordered_states, init_kmeans, 
                   n_tries, max_iter, threshold, verbose)

Summary

mixhmm$summary()

Plots

mixhmm$plot()

flamingos documentation built on Aug. 6, 2019, 5:10 p.m.