Nothing
###################################################################################
##' Constructor of [\code{\linkS4class{STCmodel}}] class
##'
##'
##' \describe{
##' \item{G}{integer. It defines the number of mixture components.}
##' \item{K}{integer. It defines the number of polynoms for each component.}
##' \item{Q}{integer. It defines the degree of the polynoms.}
##' \item{spatial}{integer. It defines if the spatial dependencies are considered (1:yes, 0:no).}
##' \item{nbparam}{integer. It indicates the number of parameters involved by the model.}
##' }
##'
##' @examples
##' getSlots("STCmodel")
##'
##' @name STCmodel-class
##' @rdname STCmodel-class
##' @exportClass STCmodel
##'
setClass(
Class = "STCmodel",
representation = representation(
G="numeric",
K="numeric",
Q="numeric",
spatial="numeric",
nbparam="numeric"
),
prototype = prototype(
G=numeric(),
K=numeric(),
Q=numeric(),
spatial=numeric(),
nbparam=numeric()
)
)
###################################################################################
##' This function builts an instance of \linkS4class{STCmodel}.
##'
##'
##' @param G integer. It defines the number of mixture components.
##' @param K integer. It defines the number of polynoms for each component.
##' @param Q integer. It defines the degree of the polynoms.
##' @param nospatial binary. It defines if the spatial dependencies are considered (1:no, 0:yes).
##'
##'
##' @return Returns an instance of \linkS4class{STCmodel}.
##'
##'
##'
STCmodel <- function(G, K, Q, nospatial){
if (nospatial == 0){
nbparam <- G -1 + (Q+2)*K*G + (K-1)*G*4
}else{
nbparam <- G -1 + (Q+2)*K*G + (K-1)*G*2
}
return(new("STCmodel", G=G, K=K, Q=Q, spatial=1-nospatial, nbparam=nbparam))
}
###################################################################################
##' Constructor of [\code{\linkS4class{STCparam}}] class
##'
##'
##' \describe{
##' \item{proportions}{numeric. It defines the component proportions.}
##' \item{lambda}{list. It defines the logistic coefficients per component.}
##' \item{beta}{list. It defines the polynom coefficients per component.}
##' \item{sigma}{matrix. It defines the variance associated to each polynom per component.}
##' }
##'
##' @examples
##' getSlots("STCparam")
##'
##' @name STCparam-class
##' @rdname STCparam-class
##' @exportClass STCparam
##'
setClass(
Class = "STCparam",
representation = representation(
proportions="numeric",
lambda="list",
beta="list",
sigma="matrix"
),
prototype = prototype(
proportions=numeric(),
lambda=list(),
beta=list(),
sigma=matrix()
)
)
###################################################################################
##' Constructor of [\code{\linkS4class{STCcriteria}}] class
##'
##'
##' \describe{
##' \item{loglike}{numeric. It indicates the value of the log-likelihood.}
##' \item{AIC}{numeric. It indicates the value of the AIC criterion.}
##' \item{BIC}{numeric. It indicates the value of the BIC criterion.}
##' \item{ICL}{numeric. It indicates the value of the ICL criterion.}
##' }
##'
##' @examples
##' getSlots("STCcriteria")
##'
##' @name STCcriteria-class
##' @rdname STCcriteria-class
##' @exportClass STCcriteria
##'
setClass(
Class = "STCcriteria",
representation = representation(
loglike="numeric",
AIC="numeric",
BIC="numeric",
ICL="numeric",
degeneracy="numeric"
),
prototype = prototype(
loglike=numeric(),
AIC=numeric(),
BIC=numeric(),
ICL=numeric(),
degeneracy=numeric()
)
)
###################################################################################
##' Constructor of [\code{\linkS4class{STCpartitions}}] class
##'
##'
##' \describe{
##' \item{hardind}{numeric. It indicates the hard partition of the individuals (obtained by the MAP rule qpplied with the MLE).}
##' \item{fuzzyind}{matrix. It indicates the fuzzy partition (conditional probability of the component membership) of the individuals.}
##' \item{hardseg}{list. It indicates the segmentation (most probable polynom according to the spatial and temporal grid) per components}
##' }
##'
##' @examples
##' getSlots("STCpartitions")
##'
##' @name STCpartitions-class
##' @rdname STCpartitions-class
##' @exportClass STCpartitions
##'
setClass(
Class = "STCpartitions",
representation = representation(
hardind="numeric",
fuzzyind="matrix",
hardseg="list"
),
prototype = prototype(
hardind=numeric(),
fuzzyind=matrix(),
hardseg=list()
)
)
###################################################################################
##' Constructor of [\code{\linkS4class{STCdata}}] class
##'
##'
##' \describe{
##' \item{x}{matrix. It contains the observations. Each column corresponds to an individual. The row indicates the values of each site for each time.}
##' \item{TT}{numeric. It indicates the number of elements of the time grid.}
##' \item{JJ}{numeric. It indicates the number of sites.}
##' \item{n}{numeric. It indicates the number of observations.}
##' \item{map}{numeric. It indicates the spatial coordinates of each site.}
##' }
##'
##' @examples
##' getSlots("STCdata")
##'
##' @name STCdata-class
##' @rdname STCdata-class
##' @exportClass STCdata
##'
setClass(
Class = "STCdata",
representation = representation(
x="array",
TT="numeric",
JJ="numeric",
n="numeric",
m="numeric",
map="matrix"
),
prototype = prototype(
x=array(),
TT=numeric(),
JJ=numeric(),
n=numeric(),
m=numeric(),
map=matrix()
)
)
###################################################################################
##' Constructors of the class \linkS4class{STCdata}
##'
##'
##' @param x array It contains the observations to cluster where the dimesions are respectively: number of the observation, site of the observation, time of the observation.
##' @param map matrix. It gives the spatial coordiantes of each site.
##' @param m numeric. It indicates the moments of observations.
##'
##' @return Returns an instance of \linkS4class{STCdata}.
##'
##'
##'
##' @export
##'
##'
BuildSTCdata <- function(x, map, m=1:(dim(x)[3])){
di <- dim(x)
if (is.null(map)){
output <- new("STCdata", x=x, n=di[1], JJ=di[2], TT=di[3], m=m)
}else{
output <- new("STCdata", x=x, n=di[1], JJ=di[2], TT=di[3], m=m, map=map)
}
return(output)
}
###################################################################################
##' Constructor of [\code{\linkS4class{STCtune}}] class
##'
##'
##' \describe{
##' \item{tol}{numeric. The algorithm is stopped when two successive iterations increase the log-likelihood less than tol.}
##' \item{nbinitSmall}{numeric. Number of random initializations for the short run EM algorithm.}
##' \item{nbinitKept}{numeric. Number of initializations kept for the long run EM algorithm.}
##' \item{nbiterSmall}{numeric. Maximum number of iteration before stopping the short run EM algorithm.}
##' \item{nbiterKept}{numeric. Maximum number of iteration before stopping the long run EM algorithm.}
##' }
##'
##' @examples
##' getSlots("STCtune")
##'
##' @name STCtune-class
##' @rdname STCtune-class
##' @exportClass STCtune
##'
setClass(
Class = "STCtune",
representation = representation(
tol="numeric",
nbinitSmall="numeric",
nbinitKept="numeric",
nbiterSmall="numeric",
nbiterKept="numeric"
),
prototype = prototype(
tol=numeric(),
nbinitSmall=numeric(),
nbinitKept=numeric(),
nbiterSmall=numeric(),
nbiterKept=numeric()
)
)
###################################################################################
##' Constructor of [\code{\linkS4class{STCresults}}] class
##'
##'
##' \describe{
##' \item{model}{\linkS4class{STCmodel}. It contains the elements relied to the model.}
##' \item{data}{\linkS4class{STCdata}. It contains the elements relied to the data.}
##' \item{param}{\linkS4class{STCparam}. It contains the elements relied to the parameters.}
##' \item{criteria}{\linkS4class{STCcriteria}. It contains the elements relied to the information criteria.}
##' \item{partitions}{\linkS4class{STCpartitions}. It contains the elements relied to the partitions.}
##' \item{tune}{\linkS4class{STCtune}. It contains the tunning parameters of the algorithm.}
##' \item{allmodels}{matrix. list of the estimnated models and their information criterion.}
##' }
##'
##' @examples
##' getSlots("STCresults")
##'
##' @name STCresults-class
##' @rdname STCresults-class
##' @exportClass STCresults
##'
setClass(
Class = "STCresults",
representation = representation(
model="STCmodel",
data="STCdata",
param="STCparam",
criteria="STCcriteria",
partitions="STCpartitions",
tune="STCtune",
allmodels="matrix"
),
prototype = prototype(
model=new("STCmodel"),
param=new("STCdata"),
param=new("STCparam"),
criteria=new("STCcriteria"),
partitions=new("STCpartitions"),
tune=new("STCtune"),
allmodels=matrix()
)
)
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