selectRHLP: selecRHLP implements a model selection procedure to select an...

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

Description

selecRHLP implements a model selection procedure to select an optimal RHLP model with unknown structure.

Usage

selectRHLP(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	Numeric vector of length m representing the observed response/output y_{1},…,y_{m}.
Kmin	The minimum number of regimes (RHLP components).
Kmax	The maximum number of regimes (RHLP components).
pmin	The minimum order of the polynomial regression.
pmax	The maximum order of the polynomial regression.
criterion	The criterion used to select the RHLP model ("BIC", "AIC").
verbose	Optional. A logical value indicating whether or not a summary of the selected model should be displayed.

Details

selectRHLP 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 RHLP 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

selectRHLP returns an object of class ModelRHLP representing the selected RHLP model according to the chosen criterion.

See Also

ModelRHLP

Examples

data(univtoydataset)

# Let's select a RHLP model on a time series with 3 regimes:
data <- univtoydataset[1:320,]

selectedrhlp <- selectRHLP(X = data$x, Y = data$y,
                           Kmin = 2, Kmax = 4, pmin = 0, pmax = 1)

selectedrhlp$summary()

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