A-quick-tour-of-HMMR In samurais: Statistical Models for the Unsupervised Segmentation of Time-Series ('SaMUraiS')

```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)
```

Introduction

HMMR: Flexible and user-friendly probabilistic segmentation of time series (or structured longitudinal data) with regime changes by a 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("univtoydataset")
```

Set up HMMR model parameters

```K <- 5 # Number of regimes (states)
p <- 3 # Dimension of beta (order of the polynomial regressors)
variance_type <- "heteroskedastic" # "heteroskedastic" or "homoskedastic" model
```

Set up EM parameters

```n_tries <- 1
max_iter <- 1500
threshold <- 1e-6
verbose <- TRUE
```

Estimation

```hmmr <- emHMMR(univtoydataset\$x, univtoydataset\$y, K, p, variance_type, n_tries,
max_iter, threshold, verbose)
```

Summary

```hmmr\$summary()
```

Plots

Predicted time series and predicted regime probabilities

```hmmr\$plot(what = "predicted")
```

Filtered time series and filtering regime probabilities

```hmmr\$plot(what = "filtered")
```

Fitted regressors

```hmmr\$plot(what = "regressors")
```

Smoothed time series and segmentation

```hmmr\$plot(what = "smoothed")
```

Log-likelihood

```hmmr\$plot(what = "loglikelihood")
```

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samurais documentation built on July 28, 2019, 5:02 p.m.