In fchamroukhi/MRHLP: Multiple Regression Model with a Hidden Logistic Process ('MRHLP') for Joint Segmentation of Multivariate Time Series

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Overview

MRHLP is an R package for flexible and user-friendly probabilistic joint segmentation of multivariate time series (or multivariate structured longitudinal data) with smooth and/or abrupt regime changes by a mixture model-based multiple regression approach with a hidden logistic process (Multiple Regression model with a Hidden Logistic Process (MRHLP)). The model is fitted by the EM algorithm.

Installation

You can install the MRHLP package from GitHub with:

# install.packages("devtools")
devtools::install_github("fchamroukhi/MRHLP")

To build vignettes for examples of usage, type the command below instead:

# install.packages("devtools")
devtools::install_github("fchamroukhi/MRHLP", 
                         build_opts = c("--no-resave-data", "--no-manual"), 
                         build_vignettes = TRUE)

Use the following command to display vignettes:

browseVignettes("MRHLP")

Usage

library(MRHLP)

# Application to a toy data set
data("toydataset")
x <- toydataset$x
y <- toydataset[,c("y1", "y2", "y3")]

K <- 5 # Number of regimes (mixture components)
p <- 1 # Dimension of beta (order of the polynomial regressors)
q <- 1 # Dimension of w (order of the logistic regression: to be set to 1 for segmentation)
variance_type <- "heteroskedastic" # "heteroskedastic" or "homoskedastic" model

n_tries <- 1
max_iter <- 1500
threshold <- 1e-6
verbose <- TRUE
verbose_IRLS <- FALSE

mrhlp <- emMRHLP(X = x, Y = y, K, p, q, variance_type, n_tries, 
                 max_iter, threshold, verbose, verbose_IRLS)

mrhlp$summary()

mrhlp$plot()

# Application to a real data set (human activity recogntion data)
data("realdataset")
x <- realdataset$x
y <- realdataset[,c("y1", "y2", "y3")]

K <- 5 # Number of regimes (mixture components)
p <- 3 # Dimension of beta (order of the polynomial regressors)
q <- 1 # Dimension of w (order of the logistic regression: to be set to 1 for segmentation)
variance_type <- "heteroskedastic" # "heteroskedastic" or "homoskedastic" model

n_tries <- 1
max_iter <- 1500
threshold <- 1e-6
verbose <- TRUE
verbose_IRLS <- FALSE

mrhlp <- emMRHLP(X = x, Y = y, K, p, q, variance_type, n_tries, 
                 max_iter, threshold, verbose, verbose_IRLS)

mrhlp$summary()

mrhlp$plot()

Model selection

In this package, it is possible to select models based on information criteria such as BIC, AIC and ICL.

The selection can be done for the two following parameters:

• K: The number of regimes;
• p: The order of the polynomial regression.

Let's select a MRHLP model for the following multivariate time series Y:

data("toydataset")
x <- toydataset$x
y <- toydataset[, c("y1", "y2", "y3")]
matplot(x, y, type = "l", xlab = "x", ylab = "Y", lty = 1)

selectedmrhlp <- selectMRHLP(X = x, Y = y, Kmin = 2, Kmax = 6, pmin = 0, pmax = 3)

selectedmrhlp$plot(what = "estimatedsignal")

fchamroukhi/MRHLP documentation built on Sept. 23, 2019, 4:17 a.m.