selectHMMR {samurais} | R Documentation |
selectHMMR implements a model selection procedure to select an optimal HMMR model with unknown structure.
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
|
Y |
Numeric vector of length m representing the observed
response/output |
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
Examples
data(univtoydataset)
selectedhmmr <- selectHMMR(X = univtoydataset$x, Y = univtoydataset$y,
Kmin = 2, Kmax = 6, pmin = 0, pmax = 2)
selectedhmmr$plot()