ind_clust: Individual-level clustering of movement metrics

In BastilleRousseau/moveNT: An R package for the analysis of movement data using network theory

Individual-level clustering of movement metrics

Description

Perform individual-level clustering (first step) of movement metrics. This function uses the output of table_cluster and perform a mixture-model. Users can select which variables will be used and the maximum number of clusters. See also mclust

Usage

ind_clust(table, max.n.clust = 8, modelname = "EEV",
  vars = c("Weight", "Degree", "Betweenness", "Speed", "DotP"))

Arguments

table	An output from the table_cluster function
max.n.clust	The maximum number of clusters to test, see the documentation for mclust for more information. Default = 8.
modelname	The model structure of the clustering, see the documentation for mclust for more information. Default is equal mean and variance for each clusters (EEV).
vars	The variable to be included. Default = c("Weight", "Degree", "Betweenness", "Speed", "DotP")

Value

A list object with each element representing an individual.

Examples

data(albatross)
grid<-loop(albatross, 35000)
table_grid<-table_cluster(albatross, grid)
ls_ind<-ind_clust(table_grid, max.n.clust=8)
table(unlist(lapply(ls_ind, function(x) x$G)))

BastilleRousseau/moveNT documentation built on Aug. 26, 2023, 5:54 a.m.