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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$fig.show != "none") par(family = "sans", mar = c(4.1,4.1,1.1,1.1), mgp = c(3,1,0), tcl = -0.5) })
library(samurais)
MHMMR: Flexible and user-friendly probabilistic joint segmentation of multivariate time series (or multivariate structured longitudinal data) with regime changes by a multiple regression model governed by a hidden Markov process, fitted by the EM (Baum-Welch) algorithm.
It was written in R Markdown, using the knitr package for production.
See help(package="samurais")
for further details and references provided by citation("samurais")
.
data("multivtoydataset")
K <- 5 # Number of regimes (states) p <- 3 # Dimension of beta (order of the polynomial regressors) variance_type <- "heteroskedastic" # "heteroskedastic" or "homoskedastic" model
n_tries <- 1 max_iter <- 1500 threshold <- 1e-6 verbose <- TRUE
mhmmr <- emMHMMR(multivtoydataset$x, multivtoydataset[,c("y1", "y2", "y3")], K, p, variance_type, n_tries, max_iter, threshold, verbose)
mhmmr$summary()
mhmmr$plot(what = "predicted")
mhmmr$plot(what = "filtered")
mhmmr$plot(what = "regressors")
mhmmr$plot(what = "smoothed")
mhmmr$plot(what = "loglikelihood")
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