Nothing
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## Differentes classes S4 accessibles a l'utilisateur et etant des slots des classes S4
## VSLCMresultsContinuous et/ou VSLCMresultsCategorical
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## Classe S4 VSLCMcriteria contenant la logvraisemblance (loglikelihood), la valeur des criteres BIC, ICL et MICL
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##' Constructor of \code{\linkS4class{VSLCMcriteria}} class
##'
##'
##' \describe{
##' \item{loglikelihood}{numeric. Log-likelihood}
##' \item{AIC}{numeric. Value of the AIC criterion.}
##' \item{BIC}{numeric. Value of the BIC criterion.}
##' \item{ICL}{numeric. Value of the ICL criterion.}
##' \item{MICL}{numeric. Value of the MICL criterion.}
##' \item{nbparam}{integer. Number of parameters.}
##' \item{cvrate}{numeric. Rate of convergence of the alternated algorithm for optimizing the MICL criterion.}
##' \item{degeneracyrate}{numeric. Rate of degeneracy for the selected model.}
##' \item{discrim}{numeric. Discriminative power of each variable.}
##' }
##'
##' @examples
##' getSlots("VSLCMcriteria")
##'
##' @name VSLCMcriteria-class
##' @rdname VSLCMcriteria-class
##' @exportClass VSLCMcriteria
setClass(Class = "VSLCMcriteria",
representation = representation(loglikelihood="numeric", AIC="numeric", BIC="numeric", ICL="numeric", MICL="numeric", nbparam="numeric", cvrate="numeric", degeneracyrate="numeric", discrim="numeric"),
prototype = prototype(loglikelihood=numeric(), AIC=numeric(), BIC=numeric(), ICL=numeric(), MICL=numeric(), nbparam=numeric(), cvrate=numeric(), degeneracyrate=numeric(), discrim=numeric())
)
InitCriteria <- function()
new("VSLCMcriteria", loglikelihood=-Inf, AIC=-Inf, BIC=-Inf, ICL=-Inf, MICL=-Inf)
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## Classe S4 VSLCMpartitions contenant la partition MAP (zMAP), la partition zstar (zOPT) et la partition floue (tik)
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##' Constructor of \code{\linkS4class{VSLCMpartitions}} class
##'
##'
##' \describe{
##' \item{zMAP}{numeric. A vector indicating the class membership of each individual by using the MAP rule computed for the best model with its maximum likelihood estimates.}
##' \item{zOPT}{numeric. Partition maximizing the integrated complete-data likelihood of the selected model.}
##' \item{tik}{numeric. Fuzzy partition computed for the best model with its maximum likelihood estimates.}
##' }
##'
##' @examples
##' getSlots("VSLCMpartitions")
##'
##' @name VSLCMpartitions-class
##' @rdname VSLCMpartitions-class
##' @exportClass VSLCMpartitions
setClass(
Class = "VSLCMpartitions",
representation = representation(zMAP="numeric", zOPT="numeric", tik="matrix"),
prototype = prototype(zMAP=numeric(), zOPT=numeric(), tik=matrix(0,0,0))
)
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## Classe S4 VSLCMstrategy contenant les parametres de reglages detailles dans VarSELLCMmixte.R
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##' Constructor of \code{\linkS4class{VSLCMstrategy}} class
##'
##'
##' \describe{
##' \item{initModel}{numeric. Number of initialisations for the model selection algorithm.}
##' \item{vbleSelec}{logical. It indicates if the selection of the variables is performed.}
##' \item{paramEstim}{logical. It indicates if the parameter estimation is performed.}
##' \item{parallel}{logical. It indicates if a parallelisation is done.}
##' \item{nbSmall}{numeric. It indicates the number of small EM.}
##' \item{iterSmall}{numeric. It indicates the number of iteration for the small EM}
##' \item{nbKeep}{numeric. It indicates the number of chains kept for the EM.}
##' \item{iterKeep}{numeric. It indicates the maximum number of iteration for the EM.}
##' \item{tolKeep}{numeric. It indicates the value of the difference between successive iterations of EM stopping the EM.}
##' }
##'
##' @examples
##' getSlots("VSLCMstrategy")
##'
##' @name VSLCMstrategy-class
##' @rdname VSLCMstrategy-class
##' @exportClass VSLCMstrategy
##'
setClass(
Class = "VSLCMstrategy",
representation = representation(initModel="numeric", vbleSelec="logical", crit.varsel="character", paramEstim="logical", parallel="logical",
nbSmall="numeric", iterSmall="numeric", nbKeep="numeric", iterKeep="numeric", tolKeep="numeric"),
prototype = prototype(initModel=numeric(), vbleSelec=logical(), crit.varsel=character(), paramEstim=logical(), parallel=logical(),
nbSmall=numeric(), iterSmall=numeric(), nbKeep=numeric(), iterKeep=numeric(), tolKeep=numeric())
)
## Constructeur de la classe S4 VSLCMstrategy
VSLCMstrategy <- function(initModel, nbcores, vbleSelec, crit.varsel, paramEstim, nbSmall, iterSmall, nbKeep, iterKeep, tolKeep){
new("VSLCMstrategy",
initModel=initModel,
parallel=(nbcores>1),
vbleSelec=vbleSelec,
crit.varsel=crit.varsel,
paramEstim=paramEstim,
nbSmall=nbSmall,
iterSmall=iterSmall,
nbKeep=min(nbKeep, nbSmall),
iterKeep=iterKeep,
tolKeep=tolKeep)
}
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## Classe S4 VSLCMmodel contenant le nombre de classes (g) et le role des variables (omega)
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##' Constructor of \code{\linkS4class{VSLCMmodel}} class
##'
##'
##' \describe{
##' \item{g}{numeric. Number of components.}
##' \item{omega}{logical. Vector indicating if each variable is irrelevant (1) or not (0) to the clustering.}
##' \item{names.relevant}{character. Names of the relevant variables.}
##' \item{names.irrelevant}{character. Names of the irrelevant variables.}
##' }
##'
##' @examples
##' getSlots("VSLCMmodel")
##'
##' @name VSLCMmodel-class
##' @rdname VSLCMmodel-class
##' @exportClass VSLCMmodel
##'
setClass(
Class = "VSLCMmodel",
representation = representation(g="numeric", omega="numeric", names.relevant="character", names.irrelevant="character"),
prototype = prototype(g=numeric(), omega=numeric(), names.relevant=character(), names.irrelevant=character())
)
check.results <- function(obj){
if (class(obj)!="VSLCMresults") stop("Results must be an instance of VSLCMresults returned by the function VarSelCluster of R package VarSelLCM")
}
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