callmix.labelling: Classification of positions as fishing or steaming, using...

In gilles-guillot/HMMVMS: Hidden Markov Models for the analysis of Vessel Monitoring data

Description

Classification of positions as fishing or steaming, using speed data.

Usage

callmix.labelling(x, nclust, algo = "depmix")

Arguments

x	a vector of the speed data
nclust	the number of clusters
algo	'depmix' or 'mclust'

Details

By using the methods provided in the algo argument, model-based clustering is performed, with the specified number of clusters. The clusters are then assigned to fishing of steaming using the following labelling scheme: Fishing corresponds to the combination of components that has a joint empirical variance smaller than that of the low-speed states estimated in a 2-component model. If there is more than one combination that reduces the variance, we choose the one with a joint empirical mean closest to that of the low-speed states estimates in a 2-component model.

Value

A list of 3 elements: 'states' A vector of the classified fishing or steaming states. 'loglik' Final log-likelihood estimate of the EM algorithm. 'parameters' A list of parameters dependent on the model.

Examples

data("DanishTrips")
res <- callmix.labelling(DanishTrips$speed,3)

gilles-guillot/HMMVMS documentation built on Dec. 23, 2019, 6:30 p.m.