Nothing

library(knitr) knitr::opts_chunk$set( fig.align = "center", fig.height = 5.5, fig.width = 6, warning = FALSE, collapse = TRUE, dev.args = list(pointsize = 10), out.width = "90%", par = TRUE ) knit_hooks$set(par = function(before, options, envir) { if (before && options$fig.show != "none") par(family = "sans", mar = c(4.1,4.1,1.1,1.1), mgp = c(3,1,0), tcl = -0.5) })

```
library(samurais)
```

In this package, it is possible to select models based on information criteria
such as **BIC**, **AIC** and **ICL**.

The selection is done on two parameters which are:

- $K$: The number of regimes;
- $p$: The order of the polyniomial regression.

Let's select a MHMMR model for the following multivariate time series $Y$:

data("multivtoydataset") x <- multivtoydataset$x y <- multivtoydataset[, c("y1", "y2", "y3")] matplot(x, y, type = "l", xlab = "x", ylab = "Y")

selectedmhmmr <- selectMHMMR(X = x, Y = y, Kmin = 2, Kmax = 6, pmin = 0, pmax = 3)

The selected model has $K = 5$ regimes and the order of the polynomial regression is $p = 0$. According to the way $Y$ has been generated, these parameters are what we expected.

Let's summarize the selected model:

selectedmhmmr$summary()

selectedmhmmr$plot(what = "smoothed")

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