R/Funclustering-package.R

In Funclustering: Functional Data Clustering

#' @importFrom graphics par plot
#' @importFrom grDevices dev.new
#' @import Rcpp fda methods
#' @useDynLib Funclustering
#' 
#' @title Funclustering
#' @docType package
#' @aliases Funclustering-package
#' @name Funclustering-package
#' @description  
#' This packages proposes a model-based clustering algorithm for multivariate functional data.
#' The parametric mixture model, based on the assumption of normality of the principal components resulting from a multivariate functional PCA, is estimated by an EM-like algorithm.
#' The main advantage of the proposed algorithm is its ability to take into account the dependence among curves. 
#' 
#' @details 
#' This package included a function named \link{funclust} which allow to perform functional data clustering. 
#' This package allows also to perform a functional PCA (function \link{mfpca}) for univariate or multivariate functional data, with possibility to define different weights for each observation.  
#' The output of funclust and mfpca are a list, so one can use summary() to display the results.
#' We can also plot the original curves with function \link{plotOC}. The function \link{plotfd} plots the curves after interpolation or smoothing.
#' See the help of theses functions for more details.  
#' 
#' @references   J.Jacques and C.Preda (2013), Funclust: a curves clustering method using functional random variable density approximation, Neurocomputing, 112, 164-171.
#'
#'J.Jacques and C.Preda (2013), Model-based clustering of multivariate functional data, Computational Statistics and Data Analysis, in press DOI 10.1016/j.csda.2012.12.004.
#'
#' @examples 
#' # Multivariate
#' # ---------  CanadianWeather (data from the fda package) --------
#' CWtime <- 1:365
#' CWrange <-c(1,365)
#' CWbasis <- create.fourier.basis(CWrange, nbasis = 65)
#' harmaccelLfd <- vec2Lfd(c(0,(2*pi/365)^2,0), rangeval = CWrange)
#' 
#' # -- Build the curves --
#' temperature <- CanadianWeather$dailyAv[,,"Temperature.C"]
#' CWfd1 <- smooth.basisPar(CWtime, temperature, CWbasis, Lfdobj = harmaccelLfd, lambda = 1e-2)$fd
#' precipitation <- CanadianWeather$dailyAv[,,"Precipitation.mm"]
#' CWfd2 <- smooth.basisPar(CWtime, precipitation, CWbasis, Lfdobj = harmaccelLfd, lambda = 1e-2)$fd
#' 
# -- the multivariate functional data object --  
#' CWfd <- list(CWfd1, CWfd2) 
#' 
#' # -- clustering in two class -- 
#' res <- funclust(CWfd, K = 2)
#' summary(res)
#' 
#' @seealso \link{funclust} 
#' 
#' @keywords package
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