R code for the clustering and segmentation of time series (including with regime changes) by mixture of Hidden Logistic Processes (MixRHLP) and the EM algorithm; i.e functional data clustering and segmentation.
You can install the development version of mixRHLP from GitHub with:
# install.packages("devtools")
devtools::install_github("fchamroukhi/mixRHLP")To build vignettes for examples of usage, type the command below instead:
# install.packages("devtools")
devtools::install_github("fchamroukhi/mixRHLP",
build_opts = c("--no-resave-data", "--no-manual"),
build_vignettes = TRUE)Use the following command to display vignettes:
browseVignettes("mixRHLP")library(mixRHLP)# Application to a toy data set
data("toydataset")
x <- toydataset$x
Y <- t(toydataset[,2:ncol(toydataset)])
K <- 3 # Number of clusters
R <- 3 # Number of regimes (polynomial regression components)
p <- 1 # Degree of the polynomials
q <- 1 # Order of the logistic regression (by default 1 for contiguous segmentation)
variance_type <- "heteroskedastic" # "heteroskedastic" or "homoskedastic" model
n_tries <- 1
max_iter <- 1000
threshold <- 1e-5
verbose <- TRUE
verbose_IRLS <- FALSE
init_kmeans <- TRUE
mixrhlp <- emMixRHLP(X = x, Y = Y, K, R, p, q, variance_type, init_kmeans,
n_tries, max_iter, threshold, verbose, verbose_IRLS)
#> EM - mixRHLP: Iteration: 1 | log-likelihood: -18129.8169520025
#> EM - mixRHLP: Iteration: 2 | log-likelihood: -16642.732267463
#> EM - mixRHLP: Iteration: 3 | log-likelihood: -16496.947898833
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mixrhlp$summary()
#> ------------------------
#> Fitted mixRHLP model
#> ------------------------
#>
#> MixRHLP model with K = 3 clusters and R = 3 regimes:
#>
#> log-likelihood nu AIC BIC ICL
#> -14810.69 41 -14851.69 -14880.41 -14880.41
#>
#> Clustering table (Number of curves in each clusters):
#>
#> 1 2 3
#> 10 10 10
#>
#> Mixing probabilities (cluster weights):
#> 1 2 3
#> 0.3333333 0.3333333 0.3333333
#>
#>
#> --------------------
#> Cluster 1 (k = 1):
#>
#> Regression coefficients for each regime/segment r (r=1...R):
#>
#> Beta(r = 1) Beta(r = 2) Beta(r = 3)
#> 1 6.8902863 5.1134337 3.90153421
#> X^1 0.9265632 -0.3959402 0.08748466
#>
#> Variances:
#>
#> Sigma2(r = 1) Sigma2(r = 2) Sigma2(r = 3)
#> 0.981915 0.9787717 0.9702211
#>
#> --------------------
#> Cluster 2 (k = 2):
#>
#> Regression coefficients for each regime/segment r (r=1...R):
#>
#> Beta(r = 1) Beta(r = 2) Beta(r = 3)
#> 1 4.96556671 6.7326717 4.8807183
#> X^1 0.08880479 0.4984443 0.1350271
#>
#> Variances:
#>
#> Sigma2(r = 1) Sigma2(r = 2) Sigma2(r = 3)
#> 0.9559969 1.03849 0.9506928
#>
#> --------------------
#> Cluster 3 (k = 3):
#>
#> Regression coefficients for each regime/segment r (r=1...R):
#>
#> Beta(r = 1) Beta(r = 2) Beta(r = 3)
#> 1 6.3513369 4.214736 6.6536553
#> X^1 -0.2449377 0.839666 0.1024863
#>
#> Variances:
#>
#> Sigma2(r = 1) Sigma2(r = 2) Sigma2(r = 3)
#> 0.9498285 0.9270384 1.001413
mixrhlp$plot()
