# 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

 ```1 2``` ```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`.

 ```1 2 3 4 5 6 7 8 9``` ```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() ```