ParamPWR-class: A Reference Class which contains the parameters of a PWR...

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

Description

ParamPWR contains all the parameters of a PWR model. The parameters are calculated by the initialization Method and then updated by the Method dynamic programming (here dynamic programming)

Fields

X

Numeric vector of length m representing the covariates/inputs x_{1},…,x_{m}.

Y

Numeric vector of length m representing the observed response/output y_{1},…,y_{m}.

m

Numeric. Length of the response/output vector Y.

K

The number of regimes (PWR components).

p

The order of the polynomial regression. p is fixed to 3 by default.

gamma

Set of transition points. gamma is a column matrix of size (K + 1, 1).

beta

Parameters of the polynomial regressions. beta is a matrix of dimension (p + 1, K), with p the order of the polynomial regression. p is fixed to 3 by default.

sigma2

The variances for the K regimes. sigma2 is a matrix of size (K, 1).

phi

A list giving the regression design matrices for the polynomial and the logistic regressions.

Methods

computeDynamicProgram(C1, K)

Method which implements the dynamic programming based on the cost matrix C1 and the number of regimes/segments K.

computeParam()

Method which estimates the parameters beta and sigma2 knowing the transition points gamma.

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