Nothing
########################################################################################################################
## BIC extractor
########################################################################################################################
#'
#' BIC criterion.
#'
#' This function gives the BIC criterion of an instance of \code{\linkS4class{VSLCMresults}}.
#' BIC is computed according to the formula \deqn{BIC=log-likelihood - 0.5*\nu*log(n)}
#' where \eqn{\nu} denotes the number of parameters in the fitted model and \eqn{n} represents the sample size.
#'
#' @param object instance of \code{\linkS4class{VSLCMresults}}.
#'
#' @name BIC
#' @rdname BIC-methods
#' @docType methods
#' @exportMethod BIC
#' @aliases BIC BIC,VSLCMresults-method
#' @references Schwarz, G. (1978). Estimating the dimension of a model. Annals of Statistics, 6(2), 461-464.
#' @examples
#' # Data loading:
#' data(heart)
#'
#' # Cluster analysis without variable selection (number of clusters between 1 and 3)
#' res<- VarSelCluster(heart[,-13], 2, vbleSelec = FALSE)
#'
#' # Get the BIC value
#' BIC(res)
setMethod(f="BIC",
signature = c("VSLCMresults"),
definition = function(object) object@criteria@BIC)
########################################################################################################################
## AIC extractor
########################################################################################################################
#'
#' AIC criterion.
#'
#' This function gives the AIC criterion of an instance of \code{\linkS4class{VSLCMresults}}.
#' AIC is computed according to the formula\deqn{AIC=log-likelihood - \nu} where \eqn{\nu} denotes the number of parameters in the fitted model.
#'
#' @param object instance of \code{\linkS4class{VSLCMresults}}.
#'
#' @name AIC
#' @rdname AIC-methods
#' @docType methods
#' @exportMethod AIC
#' @aliases AIC AIC,VSLCMresults-method
#' @references Akaike, H. (1974), "A new look at the statistical model identification", IEEE Transactions on Automatic Control, 19 (6): 716-723.
#' @examples
#' # Data loading:
#' data(heart)
#'
#' # Cluster analysis without variable selection
#' res <- VarSelCluster(heart[,-13], 2, vbleSelec = FALSE)
#'
#' # Get the AIC value
#' AIC(res)
setMethod(f="AIC",
signature = c("VSLCMresults"),
definition = function(object) object@criteria@AIC)
########################################################################################################################
## MICL extractor
########################################################################################################################
##' MICL criterion
##'
##' @description
##' This function gives the MICL criterion for an instance of \code{\linkS4class{VSLCMresults}}.
##'
##' @param object \code{\linkS4class{VSLCMresults}}
##'
##' @references Marbac, M. and Sedki, M. (2017). Variable selection for model-based clustering using the integrated completed-data likelihood. Statistics and Computing, 27 (4), 1049-1063.
##'
##'
##' @examples
##' \dontrun{
##' # Data loading:
##' data("heart")
##'
##' # Cluster analysis with variable selection
##' object <- VarSelCluster(heart[,-13], 2, vbleSelec = TRUE, crit.varsel = "MICL")
##'
##' # Get the MICL value
##' MICL(object)
##' }
##' @export
##'
##'
MICL <- function(object){
check.results(object)
if (length(object@criteria@MICL)==0) stop("This criterion wasn't computed during model selection")
object@criteria@MICL
}
########################################################################################################################
## ICL extractor
########################################################################################################################
##' ICL criterion
##'
##' @description
##' This function gives the ICL criterion for an instance of \code{\linkS4class{VSLCMresults}}.
##'
##' @param object \code{\linkS4class{VSLCMresults}}
##'
##' @references Biernacki, C., Celeux, G., and Govaert, G. (2000). Assessing a mixture model for clustering with the integrated completed likelihood. IEEE transactions on pattern analysis and machine intelligence, 22(7), 719-725.
##'
##'
##' @examples
##' # Data loading:
##' data(heart)
##'
##' # Cluster analysis without variable selection
##' res <- VarSelCluster(heart[,-13], 2, vbleSelec = FALSE)
##'
##' # Get the ICL value
##' ICL(res)
##'
##' @export
##'
##'
ICL <- function(object){
check.results(object)
object@criteria@ICL
}
########################################################################################################################
## fitted
########################################################################################################################
#'
#' Extract the partition or the probabilities of classification
#'
#' @description
#' This function returns the probabilities of classification or the partition among the observations of an instance of \code{\linkS4class{VSLCMresults}}.
#'
#' @param object instance of \code{\linkS4class{VSLCMresults}}.
#' @param type the type of prediction: probability of classification (probability) or the partition (partition)
#'
#'
#' @name fitted
#' @rdname fitted-methods
#' @docType methods
#' @exportMethod fitted
#' @aliases fitted fitted,VSLCMresults-method
#' @examples
#' # Data loading:
#' data(heart)
#'
#' # Cluster analysis without variable selection (number of clusters between 1 and 3)
#' res <- VarSelCluster(heart[,-13], 2, vbleSelec = FALSE)
#'
#' # Get the ICL value
#' fitted(res)
setMethod(f="fitted",
signature = c("VSLCMresults"),
definition = function(object, type="partition"){
if (!(type %in% c("probability", "partition")))
stop("type must be probability or partition")
out <- object@partitions@zMAP
if (type=="probability") out <- object@partitions@tik
out
}
)
########################################################################################################################
## fitted.values
########################################################################################################################
#'
#' Extract the partition or the probabilities of classification
#'
#' @description
#' This function returns the probabilities of classification or the partition among the observations of an instance of \code{\linkS4class{VSLCMresults}}.
#'
#' @param object instance of \code{\linkS4class{VSLCMresults}}.
#' @param type the type of prediction: probability of classification (probability) or the partition (partition)
#'
#'
#' @name fitted.values
#' @rdname fitted.values-methods
#' @docType methods
#' @exportMethod fitted.values
#' @aliases fitted.values fitted.values,VSLCMresults-method
#' @examples
#' # Data loading:
#' data(heart)
#'
#' # Cluster analysis without variable selection (number of clusters between 1 and 3)
#' res <- VarSelCluster(heart[,-13], 2, vbleSelec = FALSE)
#'
#' # Get the ICL value
#' fitted.values(res)
setMethod(f="fitted.values",
signature = c("VSLCMresults"),
definition = function(object, type="partition"){
if (!(type %in% c("probability", "partition")))
stop("type must be probability or partition")
out <- object@partitions@zMAP
if (type=="probability") out <- object@partitions@tik
out
}
)
########################################################################################################################
## coef
########################################################################################################################
#'
#' Extract the parameters
#'
#' @description
#' This function returns an instance of class \code{\linkS4class{VSLCMparam}} which contains the model parameters.
#'
#' @param object instance of \code{\linkS4class{VSLCMresults}}.
#'
#'
#' @name coef
#' @rdname coef-methods
#' @docType methods
#' @exportMethod coef
#' @aliases coef coef,VSLCMresults-method
#'
#' @examples
#' # Data loading:
#' data(heart)
#'
#' # Cluster analysis without variable selection (number of clusters between 1 and 3)
#' res <- VarSelCluster(heart[,-13], 1:3, vbleSelec = FALSE)
#'
#' # Get the ICL value
#' coef(res)
setMethod(f="coef",
signature = c("VSLCMresults"),
definition = function(object) object@param)
########################################################################################################################
## coefficients
########################################################################################################################
#'
#' Extract the parameters
#'
#' @description
#' This function returns an instance of class \code{\linkS4class{VSLCMparam}} which contains the model parameters.
#'
#' @param object instance of \code{\linkS4class{VSLCMresults}}.
#'
#' @name coefficients
#' @rdname coefficients-methods
#' @docType methods
#' @exportMethod coefficients
#' @aliases coefficients coefficients,VSLCMresults-method
#' @examples
#' # Data loading:
#' data(heart)
#'
#' # Cluster analysis without variable selection (number of clusters between 1 and 3)
#' res <- VarSelCluster(heart[,-13], 1:3, vbleSelec = FALSE)
#'
#' # Get the ICL value
#' coefficients(res)
setMethod(f="coefficients",
signature = c("VSLCMresults"),
definition = function(object) object@param)
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