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

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

Description

selectMHMMR implements a model selection procedure to select an optimal MHMMR model with unknown structure.

Usage

selectMHMMR(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 time series observed at points 1,…,m.
Kmin	The minimum number of regimes (c components).
Kmax	The maximum number of regimes (MHMMR components).
pmin	The minimum order of the polynomial regression.
pmax	The maximum order of the polynomial regression.
criterion	The criterion used to select the MHMMR model ("BIC", "AIC").
verbose	Optional. A logical value indicating whether or not a summary of the selected model should be displayed.

Details

selectMHMMR selects the optimal MHMMR 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 MHMMR 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

selectMHMMR returns an object of class ModelMHMMR representing the selected MHMMR model according to the chosen criterion.

See Also

ModelMHMMR

Examples

data(multivtoydataset)
x <- multivtoydataset$x
y <- multivtoydataset[, c("y1", "y2", "y3")]

selectedmhmmr <- selectMHMMR(X = x, Y = y, Kmin = 2, Kmax = 6,
                             pmin = 0, pmax = 2)

selectedmhmmr$summary()

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