spatimeclus: This function performs the maximum likelihood estimation for...

In SpaTimeClus: Model-Based Clustering of Spatio-Temporal Data

Description

This function performs the maximum likelihood estimation for a known model in clustering

Usage

spatimeclus(obs, G, K, Q, map = NULL, m = 1:(dim(obs)[3]), crit = "BIC",
  tol = 0.001, param = NULL, nbcores = 1, nbinitSmall = 500,
  nbinitKept = 50, nbiterSmall = 20, nbiterKept = 500)

Arguments

obs	array It contains the observations to cluster where the dimensions are respectively: number of the observation, site of the observation, time of the observation.
G	numeric. It defines possible numbers of components.
K	numeric. It defines possible numbers of regressions per components
Q	numeric. It defines possible degrees of regressions.
map	matrix. It gives the spatial coordiantes of each site.
m	numeric. It indicates the moments of observations (optional, default is 1:T).
crit	character. It indicates the criterion used for the model selection ("AIC", "BIC" or "ICL", optional, default is "BIC").
tol	numeric. The algorithm is stopped when the loglikelihood increases less than tol during two successive iterations (optional, default is 0.001).
param	list of STCparam. It gives the initial values of the EM algorithm (optional, starting point are sampled at random).
nbcores	numeric. It defines the numerber of cores used by the alogrithm, only for Linux and Mac (optional, default is 1).
nbinitSmall	numeric. It defines the number of random initializations (optional, default is 500).
nbinitKept	numeric. It defines the number of chains estimated until convergence (optional, default is 50).
nbiterSmall	numeric. It defines the number of iterations before keeping the nbinitKept best chains (optional, default is 20).
nbiterKept	numeric. It defines the maximum number of iterations before to stop the algorith; (optional, default is 500).

Value

Returns an instance of STCresults.

Examples

## Not run: 
data(airparif)

# Clustering of the data by considering the spatial dependencies
res.spa <- spatimeclus(airparif$obs,  G=3, K=4, Q=4, map = airparif$map,
 nbinitSmall=50, nbinitKept=5, nbiterSmall=5)
summary(res.spa)

# Clustering of the data without considering the spatial dependencies
res.nospa <- spatimeclus(airparif$obs,  G=3, K=4, Q=4, nbinitSmall=50, nbinitKept=5, nbiterSmall=5)
summary(res.nospa)

## End(Not run)

SpaTimeClus documentation built on May 2, 2019, 5:22 p.m.