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

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FLaMingos: Functional Latent datA Models for clusterING heterogeneOus curveS

flamingos is an open-source toolbox (available in R and in Matlab) for the simultaneous clustering and segmentation of heterogeneous functional data (i.e time-series ore more generally longitudinal data), with original and flexible functional latent variable models, fitted by unsupervised algorithms, including EM algorithms.

Our nice FLaMingos are mainly:

• mixRHLP;
• mixHMM;
• mixHMMR.

The models and algorithms are developped and written in Matlab by Faicel Chamroukhi, and translated and designed into R packages by Florian Lecocq, Marius Bartcus and Faicel Chamroukhi.

Installation

You can install the flamingos package from GitHub with:

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

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

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

Use the following command to display vignettes:

browseVignettes("flamingos")

Usage

library(flamingos)

mixRHLP

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

K <- 3 # Number of clusters
R <- 3 # Number of regimes (polynomial regression components)
p <- 1 # Degree of the polynomials
q <- 1 # Order of the logistic regression (by default 1 for contiguous segmentation)
variance_type <- "heteroskedastic" # "heteroskedastic" or "homoskedastic" model

n_tries <- 1
max_iter <- 1000
threshold <- 1e-5
verbose <- TRUE
verbose_IRLS <- FALSE
init_kmeans <- TRUE

mixrhlp <- emMixRHLP(X = x, Y = Y, K, R, p, q, variance_type, init_kmeans, 
                     n_tries, max_iter, threshold, verbose, verbose_IRLS)

mixrhlp$summary()

mixrhlp$plot()

mixHMM

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

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

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

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

mixhmm$summary()

mixhmm$plot()

mixHMMR

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

K <- 3 # Number of clusters
R <- 3 # Number of regimes/states
p <- 1 # Degree of the polynomial regression
variance_type <- "heteroskedastic" # "heteroskedastic" or "homoskedastic" model

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

mixhmmr <- emMixHMMR(X = x, Y = Y, K, R, p, variance_type, ordered_states, 
                     init_kmeans, n_tries, max_iter, threshold, verbose)

mixhmmr$summary()

mixhmmr$plot()

References

fchamroukhi/FLaMingos documentation built on Jan. 23, 2020, 9:20 a.m.