A-quick-tour-of-PWR

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

PWR: Piecewise Regression (PWR) for time series (or structured longitudinal data) modeling and optimal segmentation by using dynamic programming.

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").

Load data

data("univtoydataset")

Set up PWR model parameters

K <- 5 # Number of segments
p <- 3 # Polynomial degree

Estimation

pwr <- fitPWRFisher(univtoydataset$x, univtoydataset$y, K, p)

Summary

pwr$summary()

Plots

Regressors

pwr$plot(what = "regressors")

Segmentation

pwr$plot(what = "segmentation")

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