samurais (version 0.1.0)

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

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 $x_1,\dots ,x_m$.

Y

Numeric vector of length m representing the observed response/output $y_1,\dots ,y_m$.

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.

Value

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

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.

See Also

ModelRHLP

Examples

# NOT RUN {
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()
# }