selectHMMR: selectHMMR implements a model selection procedure to select...

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

Description

selectHMMR implements a model selection procedure to select an optimal HMMR model with unknown structure.

Usage

selectHMMR(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 (HMMR components).
Kmax	The maximum number of regimes (HMMR components).
pmin	The minimum order of the polynomial regression.
pmax	The maximum order of the polynomial regression.
criterion	The criterion used to select the HMMR model ("BIC", "AIC").
verbose	Optional. A logical value indicating whether or not a summary of the selected model should be displayed.

Details

selectHMMR selects the optimal HMMR 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 HMMR 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

selectHMMR returns an object of class ModelHMMR representing the selected HMMR model according to the chosen criterion.

See Also

ModelHMMR

Examples

data(univtoydataset)

selectedhmmr <- selectHMMR(X = univtoydataset$x, Y = univtoydataset$y,
                           Kmin = 2, Kmax = 6, pmin = 0, pmax = 2)

selectedhmmr$plot()

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