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(mixRHLP)
```

**mixRHLP** is a R package for segmentation. It provides functions for
parameter estimation via the EM algorithm. This document gives a quick tour of
**mixRHLP** (version `r packageVersion("mixRHLP")`

) functionalities. It was
written in R Markdown, using the knitr
package for production.
See `help(package="mixRHLP")`

for further details and references provided by `citation("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(what = "estimatedsignal")

mixrhlp$plot(what = "regressors")

mixrhlp$plot(what = "loglikelihood")

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