R/cop_sammon2.R

In cops: Cluster Optimized Proximity Scaling

#' Another COPS versions of Sammon mapping models (via smacofSym)
#'
#' Uses smacofSym, so it can deal with a weightmatrix and different types. The free parameter is lambda for power transformations of the observed proximities.
#'
#' @param dis numeric matrix or dist object of a matrix of proximities
#' @param theta theta the theta vector of powers; this must be a scalar of the lambda transformation for the observed proximities. Defaults to 1.
#' @param type MDS type. Default is ratio.
#' @param ndim number of dimensions of the target space
#' @param itmaxi number of iterations. default is 1000.
#' @param weightmat (optional) a matrix of nonnegative weights (NOT the sammon weights)
#' @param init (optional) initial configuration
#' @param stressweight weight to be used for the fit measure; defaults to 1
#' @param cordweight weight to be used for the cordillera; defaults to 0.5
#' @param q the norm of the corrdillera; defaults to 1
#' @param minpts the minimum points to make up a cluster in OPTICS; defaults to ndim+1
#' @param epsilon the epsilon parameter of OPTICS, the neighbourhood that is checked; defaults to 10
#' @param rang range of the distances (min distance minus max distance). If NULL (default) the cordillera will be normed to each configuration's maximum distance, so an absolute value of goodness-of-clusteredness.
#' @param verbose numeric value hat prints information on the fitting process; >2 is extremely verbose
#' @param normed should the cordillera be normed; defaults to TRUE
#' @param scale should the configuration be scale adjusted
#' @param ... additional arguments to be passed to the fitting procedure
#' 
#' @return A list with the components
#'    \itemize{
#'         \item{stress:} the stress-1 value
#'         \item{stress.m:} default normalized stress
#'         \item{copstress:} the weighted loss value
#'         \item{OC:} the Optics cordillera value
#'         \item{parameters:} the parameters used for fitting (lambda)
#'         \item{fit:} the returned object of the fitting procedure
#'         \item{cordillera:} the cordillera object
#' }
#'
#'
#' @importFrom stats dist as.dist
#' @importFrom smacof smacofSym
#' @import cordillera
#'@keywords multivariate
cop_sammon2 <- function(dis,theta=1,type="ratio",ndim=2,weightmat=1-diag(nrow(dis)),init=NULL,itmaxi=1000,...,stressweight=1,cordweight=0.5,q=1,minpts=ndim+1,epsilon=10,rang=NULL,verbose=0,normed=TRUE,scale="sd"){
  if(is.null(init)) init <- "torgerson" 
  if(inherits(dis,"dist")) dis <- as.matrix(dis)
  #if(is.null(weightmat)) weightmat <- 1-diag(dim(dis)[1]) 
  #kappa first argument, lambda=second
  if(length(theta)>3) stop("There are too many parameters in the theta argument.")
  lambda <- theta[1]
  nu <- -1
  elscalw <- dis^(nu*lambda) #the weighting in elastic scaling
  diag(elscalw) <- 1
  combwght <- stats::as.dist(weightmat*elscalw) #combine the user weights and the elastic scaling weights
  fit <- smacof::smacofSym(dis^lambda,type=type,ndim=ndim,weightmat=combwght,init=init,verbose=isTRUE(verbose==2),itmax=itmaxi,...) #optimize with smacof
  fit$lambda <- lambda
  #fit$stress.1 <- fit$stress
  #fitdis <- fit$confdist
  #delts <- fit$dhat 
  #fit$stress.r <- sum(combwght*((delts-fitdis)^2))
  fit$stress.m <- fit$stress^2
  fit$parameters <- fit$theta <- fit$pars <-  c(lambda=lambda)
  fit$deltaorig <- stats::as.dist(dis)
  copobj <- copstress(fit,stressweight=stressweight,cordweight=cordweight,q=q,minpts=minpts,epsilon=epsilon,rang=rang,verbose=isTRUE(verbose>1),scale=scale,normed=normed,init=init)
  out <- list(stress=fit$stress, stress.m=fit$stress.m, copstress=copobj$copstress, OC=copobj$OC, parameters=copobj$parameters, fit=fit,copsobj=copobj) #target functions
  out
}