---
title: "A-quick-tour-of-RHLP"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{A-quick-tour-of-RHLP}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r setup, include=FALSE}
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)
})
```

```{r, message = FALSE, echo = FALSE}
library(samurais)
```

# Introduction

**RHLP**: Flexible and user-friendly probabilistic segmentation of time series 
(or structured longitudinal data) with smooth and/or abrupt regime changes by 
a mixture model-based regression approach with a hidden logistic process, 
fitted by the EM algorithm.

It was written in R Markdown, using the [knitr](https://cran.r-project.org/package=knitr)
package for production.

See `help(package="samurais")` for further details and references provided by `citation("samurais")`.

# Load data

```{r}
data("univtoydataset")
x <- univtoydataset$x
y <- univtoydataset$y
```

# Set up RHLP model parameters

```{r}
K <- 5 # Number of regimes (mixture components)
p <- 3 # Dimension of beta (order of the polynomial regressors)
q <- 1 # Dimension of w (order of the logistic regression: to be set to 1 for segmentation)
variance_type <- "heteroskedastic" # "heteroskedastic" or "homoskedastic" model
```

# Set up EM parameters

```{r}
n_tries <- 1
max_iter = 1500
threshold <- 1e-6
verbose <- TRUE
verbose_IRLS <- FALSE
```

# Estimation

```{r}
rhlp <- emRHLP(X = x, Y = y, K, p, q, variance_type, n_tries, 
               max_iter, threshold, verbose, verbose_IRLS)
```

# Summary

```{r}
rhlp$summary()
```

# Plots

## Fitted regressors

```{r}
rhlp$plot(what = "regressors")
```

## Estimated signal

```{r}
rhlp$plot(what = "estimatedsignal")
```

## Log-likelihood

```{r}
rhlp$plot(what = "loglikelihood")
```
