selectMRHLP: selecMRHLP implements a model selection procedure to select...

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

Description

selecMRHLP implements a model selection procedure to select an optimal MRHLP model with unknown structure.

Usage

selectMRHLP(X, Y, Kmin = 1, Kmax = 10, pmin = 0, pmax = 4,
  criterion = c("BIC", "AIC"), verbose = TRUE)

Arguments

X	Numeric vector of length m representing the covariates/inputs x_{1},…,x_{m}.
Y	Matrix of size (m, d) representing a d dimension function of X observed at points 1,…,m. Y is the observed response/output.
Kmin	The minimum number of regimes (MRHLP components).
Kmax	The maximum number of regimes (MRHLP components).
pmin	The minimum order of the polynomial regression.
pmax	The maximum order of the polynomial regression.
criterion	The criterion used to select the MRHLP model ("BIC", "AIC").
verbose	Optional. A logical value indicating whether or not a summary of the selected model should be displayed.

Details

selectMRHLP selects the optimal MRHLP model among a set of model candidates by optimizing a model selection criteria, including the Bayesian Information Criterion (BIC). This function first fits the different MRHLP model candidates by varying the number of regimes K from Kmin to Kmax and the order of the polynomial regression p from pmin to pmax. The model having the highest value of the chosen selection criterion is then selected.

Value

selectMRHLP returns an object of class ModelMRHLP representing the selected MRHLP model according to the chosen criterion.

See Also

ModelMRHLP

Examples

data(multivtoydataset)

# Let's select a MRHLP model on a multivariate time series with 3 regimes:
data <- multivtoydataset[1:320, ]
x <- data$x
y <- data[, c("y1", "y2", "y3")]

selectedmrhlp <- selectMRHLP(X = x, Y = y, Kmin = 2, Kmax = 4,
                             pmin = 0, pmax = 1)

selectedmrhlp$summary()

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