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R/powerStressMin.R
In cops: Cluster Optimized Proximity Scaling
## # #' Torgerson scaling
## # #'
## # #' @param delta symmetric, numeric matrix of distances
## # #' @param p target space dimensions
## # #' @return a n x p matrix (the configuration)
## # #' @export
## # #' @examples
## # #' dis<-as.matrix(smacof::kinshipdelta)
## # #' res<-torgerson(dis)
## #torgerson <- function(delta, p = 2) {
## # z <- eigen(-doubleCenter((as.matrix (delta) ^ 2)/2))
## # v <- pmax(z$values,0)
## # return(z$vectors[,1:p]%*%diag(sqrt(v[1:p])))
## #}
## #' Power Stress SMACOF
## #'
## #' An implementation to minimize power stress by majorization with ratio or interval optimal scaling. Usually more accurate but slower than powerStressFast. Uses a repeat loop.
## #'
## #' @param delta dist object or a symmetric, numeric data.frame or matrix of distances
## #' @param kappa power of the transformation of the fitted distances; defaults to 1
## #' @param lambda the power of the transformation of the proximities; defaults to 1
## #' @param nu the power of the transformation for weightmat; defaults to 1
## #' @param type what type of MDS to fit. One of "ratio" or "interval". Default is "ratio".
## #' @param weightmat a matrix of finite weights
## #' @param init starting configuration
## #' @param ndim dimension of the configuration; defaults to 2
## #' @param acc numeric accuracy of the iteration. Default is 1e-6.
## #' @param itmax maximum number of iterations. Default is 10000.
## #' @param verbose should iteration output be printed; if > 1 then yes
## #' @param principal If ‘TRUE’, principal axis transformation is applied to the final configuration
## #'
## #' @return a smacofP object (inheriting form smacofB, see \code{\link{smacofSym}}). It is a list with the components
## #' \itemize{
## #' \item delta: Observed, untransformed dissimilarities
## #' \item tdelta: Observed explicitly transformed dissimilarities, normalized
## #' \item dhat: Explicitly transformed dissimilarities (dhats), optimally scaled and normalized
## #' \item confdist: Configuration dissimilarities
## #' \item conf: Matrix of fitted configuration
## #' \item stress: Default stress (stress 1; sqrt of explicitly normalized stress)
## #' \item spp: Stress per point
## #' \item ndim: Number of dimensions
## #' \item model: Name of smacof model
## #' \item niter: Number of iterations
## #' \item nobj: Number of objects
## #' \item type: Type of MDS model
## #' \item weightmat: weighting matrix as supplied
## #' \item stress.m: Default stress (stress-1^2)
## #' \item tweightmat: transformed weighthingmatrix (here weightmat^nu)
## #' }
## #'
## #' @section Note:
## #' The functionality related to power stress and the smacofP class is also available in the stops package (\code{\link[stops]{powerStressMin}}). Expect masking when both are loaded.
## #'
## #' @importFrom stats dist as.dist
## #' @importFrom smacofx spp
## #'
## #' @seealso \code{\link{smacofSym}}
## #'
## #' @examples
## #' dis<-smacof::kinshipdelta
## #' res<-powerStressMin(as.matrix(dis),kappa=2,lambda=1.5,itmax=1000)
## #' res
## #' summary(res)
## #' plot(res)
## #'
## #' @export
## powerStressMin <- function (delta, kappa=1, lambda=1, nu=1, type="ratio", weightmat=1-diag(nrow(delta)), init=NULL, ndim = 2, acc= 1e-6, itmax = 10000, verbose = FALSE, principal=FALSE) {
## if(inherits(delta,"dist") || is.data.frame(delta)) delta <- as.matrix(delta)
## if(!isSymmetric(delta)) stop("Delta is not symmetric.\n")
## if(inherits(weightmat,"dist") || is.data.frame(weightmat)) weightmat <- as.matrix(weightmat)
## if(!isSymmetric(weightmat)) stop("weightmat is not symmetric.\n")
## type <- match.arg(type, c("ratio", "interval",several.ok = FALSE))
## trans <- type
## typo <- type
## if (trans=="ratio"){
## trans <- "none"
## }
## if(verbose>0) cat("Minimizing",type,"power-stress with kappa=",kappa,"lambda=",lambda,"nu=",nu,"\n")
## n <- nrow (delta)
## r <- kappa/2
## p <- ndim
## if (p > (n - 1)) stop("Maximum number of dimensions is n-1!")
## if(is.null(rownames(delta))) rownames(delta) <- 1:n
## labos <- rownames(delta) #labels
## deltaorig <- delta
## delta <- delta^lambda
## weightmato <- weightmat
## weightmat <- weightmat^nu
## weightmat[!is.finite(weightmat)] <- 0
## delta <- delta / enorm (delta, weightmat)
## disobj <- smacof::transPrep(as.dist(delta), trans = trans, spline.intKnots = 2, spline.degree = 2)
## deltaold <- delta
## itel <- 1
## xold <- init
## if(is.null(init)) xold <- smacof::torgerson (delta, p = p)
## xstart <- xold
## xold <- xold / enorm (xold)
## n <- nrow (xold)
## nn <- diag (n)
## dold <- sqdist (xold)
## #Optimal scaling
## eold <- as.dist(sqrt(dold))
## dhat <- smacof::transform(eold, disobj, w = as.dist(weightmat), normq = 0.5)
## dhatt <- dhat$res
## dhatd <- structure(dhatt, Size = n, call = quote(as.dist.default(m=b)), class = "dist", Diag = FALSE, Upper = FALSE)
## delta <- as.matrix(dhatd)
## rold <- sum (weightmat * delta * mkPower (dold, r))
## nold <- sum (weightmat * mkPower (dold, 2 * r))
## aold <- rold / nold
## sold <- 1 - 2 * aold * rold + (aold ^ 2) * nold
## #Optimizing
## repeat {
## p1 <- mkPower (dold, r - 1)
## p2 <- mkPower (dold, (2 * r) - 1)
## by <- mkBmat (weightmat * delta * p1)
## cy <- mkBmat (weightmat * p2)
## ga <- 2 * sum (weightmat * p2)
## be <- (2 * r - 1) * (2 ^ r) * sum (weightmat * delta)
## de <- (4 * r - 1) * (4 ^ r) * sum (weightmat)
## if (r >= 0.5) {
## my <- by - aold * (cy - de * nn)
## }
## if (r < 0.5) {
## my <- (by - be * nn) - aold * (cy - ga * nn)
## }
## xnew <- my %*% xold
## xnew <- xnew / enorm (xnew)
## dnew <- sqdist (xnew)
## #Optimal scaling
## e <- as.dist(sqrt(dnew)) #I need the dist(x) here for interval
## dhat2 <- smacof::transform(e, disobj, w = as.dist(weightmat), normq = 0.5) ## dhat update
## dhatt <- dhat2$res
## dhatd <- structure(dhatt, Size = n, call = quote(as.dist.default(m=b)), class = "dist", Diag = FALSE, Upper = FALSE)
## delta <- as.matrix(dhatd)
## rnew <- sum (weightmat * delta * mkPower (dnew, r))
## nnew <- sum (weightmat * mkPower (dnew, 2 * r))
## anew <- rnew / nnew
## snew <- 1 - 2 * anew * rnew + (anew ^ 2) * nnew
## if(is.na(snew)) #if there are issues with the values
## {
## snew <- sold
## dnew <- dold
## anew <- aold
## xnew <- xold
## }
## if (verbose>2) {
## cat (
## formatC (itel, width = 4, format = "d"),
## formatC (
## sold,
## digits = 10,
## width = 13,
## format = "f"
## ),
## formatC (
## snew,
## digits = 10,
## width = 13,
## format = "f"
## ),
## "\n"
## )
## }
## if ((itel == itmax) || ((sold - snew) < acc))
## break ()
## itel <- itel + 1
## xold <- xnew
## dold <- dnew
## sold <- snew
## aold <- anew
## }
## xnew <- xnew/enorm(xnew)
## ## relabeling as they were removed in the optimal scaling
## rownames(delta) <- labos
## attr(xnew,"dimnames")[[1]] <- rownames(delta)
## attr(xnew,"dimnames")[[2]] <- paste("D",1:p,sep="")
## doutm <- mkPower(sqdist(xnew),r)
## deltam <- delta
## delta <- stats::as.dist(delta)
## #delta <- structure(delta, Size = n, call = quote(as.dist.default(m=b)),
## # class = "dist", Diag = FALSE, Upper = FALSE)
## deltaorig <- stats::as.dist(deltaorig)
## deltaold <- stats::as.dist(deltaold)
## #doute <- doutm/enorm(doutm) #this is an issue here!
## #doute <- stats::as.dist(doute)
## dout <- stats::as.dist(doutm)
## weightmatm <-weightmat
## weightmat <- stats::as.dist(weightmatm)
## weightmato <- stats::as.dist(weightmato)
## #resmat <- weightmatm*as.matrix((deltam - doutm)^2) #old spp now we sum up to 100
## #spp <- colMeans(resmat)
## spoint <- smacofx::spp(delta, dout, weightmat)
## resmat<-spoint$resmat
## rss <- sum(spoint$resmat[lower.tri(spoint$resmat)])
## spp <- spoint$spp
## #spp <- colMeans(resmat)
## if (principal) {
## xnew_svd <- svd(xnew)
## xnew <- xnew %*% xnew_svd$v
## }
## #stressen <- sum(weightmat*(doute-delta)^2)
## if(verbose>1) cat("*** Stress:",snew, "; Stress 1 (default reported):",sqrt(snew), "\n")
## out <- list(delta=deltaorig, dhat=delta, confdist=dout, iord=dhat2$iord.prim, conf = xnew, stress=sqrt(snew), spp=spp, ndim=p, weightmat=weightmato, resmat=resmat, rss=rss, init=xstart, model="Power-Stress SMACOF", niter = itel,nobj = dim(xnew)[1], type = type, call=match.call(), stress.m=snew, alpha = anew, sigma = snew, tdelta=deltaold, parameters=c(kappa=kappa,lambda=lambda,nu=nu), pars=c(kappa=kappa,lambda=lambda,nu=nu), theta=c(kappa=kappa,lambda=lambda,nu=nu),tweightmat=weightmat)
## class(out) <- c("smacofP","smacofB","smacof")
## out
## }
## #' @rdname powerStressMin
## #' @export
## powerstressMin <- powerStressMin
## #' @rdname powerStressMin
## #' @export
## postmds <- powerStressMin
## #' @rdname powerStressMin
## #' @export
## pstressMin <- powerStressMin
## #' @rdname powerStressMin
## #' @export
## pStressMin <- powerStressMin
## #' @rdname powerStressMin
## #' @export
## pstressmds <- powerStressMin
## ## #' Power Stress SMACOF
## ## #'
## ## #' An old implementation to minimize power stress by minimization-majorization. Usually more accurate but slower than powerStressFast. Uses a repeat loop.
## ## #'
## ## #' @param delta dist object or a symmetric, numeric data.frame or matrix of distances
## ## #' @param kappa power of the transformation of the fitted distances; defaults to 1
## ## #' @param lambda the power of the transformation of the proximities; defaults to 1
## ## #' @param nu the power of the transformation for weightmat; defaults to 1
## ## #' @param weightmat a matrix of finite weights
## ## #' @param init starting configuration
## ## #' @param ndim dimension of the configuration; defaults to 2
## ## #' @param acc numeric accuracy of the iteration. Default is 1e-6.
## ## #' @param itmax maximum number of iterations. Default is 10000.
## ## #' @param verbose should iteration output be printed; if > 1 then yes
## ## #'
## ## #' @return a smacofP object (inheriting form smacofB, see \code{\link{smacofSym}}). It is a list with the components
## ## #' \itemize{
## ## #' \item delta: Observed input dissimilarities, not normalized
## ## #' \item tdelta: Explicitly transformed input dissimilarities, not normalized
## ## #' \item dhats: Explicitly transformed dissimilarities, optimally scaled and normalized
## ## #' \item confdist: Configuration dissimilarities
## ## #' \item conf: Matrix of fitted configuration
## ## #' \item stress: Default stress (stress 1; sqrt of explicitly normalized stress)
## ## #' \item spp: Stress per point (based on stress.en)
## ## #' \item ndim: Number of dimensions
## ## #' \item model: Name of smacof model
## ## #' \item niter: Number of iterations
## ## #' \item nobj: Number of objects
## ## #' \item type: Type of MDS model
## ## #' }
## ## #' and some additional components
## ## #' \itemize{
## ## #' \item stress.m: default stress for the COPS and STOP defaults to the explicitly normalized stress on the normalized, transformed dissimilarities
## ## #' \item weightmat: weighting matrix
## ## #'}
## ## #'
## ## #' @section Note:
## ## #' The functionality related to power stress and the smacofP class is also available in the stops package (\code{\link[stops]{powerStressMin}}). Expect masking when both are loaded.
## ## #'
## ## #' @importFrom stats dist as.dist
## ## #'
## ## #' @seealso \code{\link{smacofSym}}
## ## #'
## ## #' @examples
## ## #' dis<-smacof::kinshipdelta
## ## #' res<-powerStressMin(as.matrix(dis),kappa=2,lambda=1.5,itmax=1000)
## ## #' res
## ## #' summary(res)
## ## #' plot(res)
## ## #'
## ## #' @export
## ## powerStressMinOLD <- function (delta, kappa=1, lambda=1, nu=1, weightmat=1-diag(nrow(delta)), init=NULL, ndim = 2, acc= 1e-6, itmax = 10000, verbose = FALSE) {
## ## if(inherits(delta,"dist") || is.data.frame(delta)) delta <- as.matrix(delta)
## ## if(!isSymmetric(delta)) stop("Delta is not symmetric.\n")
## ## if(verbose>0) cat("Minimizing powerStress with kappa=",kappa,"lambda=",lambda,"nu=",nu,"\n")
## ## type <- "ratio"
## ## r <- kappa/2
## ## p <- ndim
## ## deltaorig <- delta #untransformed non-normalized input dissimilarities
## ## delta <- delta^lambda
## ## #weightmato <- weightmat
## ## weightmat <- weightmat^nu
## ## weightmat[!is.finite(weightmat)] <- 1
## ## deltaold <- delta #explicitly transformed non-normalized input dissimilarities
## ## delta <- delta / enorm (delta, weightmat)
## ## itel <- 1
## ## xold <- init
## ## if(is.null(init)) xold <- smacof::torgerson (delta, p = p)
## ## xold <- xold / enorm (xold)
## ## n <- nrow (xold)
## ## nn <- diag (n)
## ## dold <- sqdist (xold)
## ## rold <- sum (weightmat * delta * mkPower (dold, r))
## ## nold <- sum (weightmat * mkPower (dold, 2 * r))
## ## aold <- rold / nold
## ## sold <- 1 - 2 * aold * rold + (aold ^ 2) * nold
## ## repeat {
## ## p1 <- mkPower (dold, r - 1)
## ## p2 <- mkPower (dold, (2 * r) - 1)
## ## by <- mkBmat (weightmat * delta * p1)
## ## cy <- mkBmat (weightmat * p2)
## ## ga <- 2 * sum (weightmat * p2)
## ## be <- (2 * r - 1) * (2 ^ r) * sum (weightmat * delta)
## ## de <- (4 * r - 1) * (4 ^ r) * sum (weightmat)
## ## if (r >= 0.5) {
## ## my <- by - aold * (cy - de * nn)
## ## }
## ## if (r < 0.5) {
## ## my <- (by - be * nn) - aold * (cy - ga * nn)
## ## }
## ## xnew <- my %*% xold
## ## xnew <- xnew / enorm (xnew)
## ## dnew <- sqdist (xnew)
## ## rnew <- sum (weightmat * delta * mkPower (dnew, r))
## ## nnew <- sum (weightmat * mkPower (dnew, 2 * r))
## ## anew <- rnew / nnew
## ## snew <- 1 - 2 * anew * rnew + (anew ^ 2) * nnew
## ## if(is.na(snew)) #if there are issues with the values
## ## {
## ## snew <- sold
## ## dnew <- dold
## ## anew <- aold
## ## xnew <- xold
## ## }
## ## if (verbose>2) {
## ## cat (
## ## formatC (itel, width = 4, format = "d"),
## ## formatC (
## ## sold,
## ## digits = 10,
## ## width = 13,
## ## format = "f"
## ## ),
## ## formatC (
## ## snew,
## ## digits = 10,
## ## width = 13,
## ## format = "f"
## ## ),
## ## "\n"
## ## )
## ## }
## ## # if(is.na(snew)) #to avoid numerical issues if there are zeros somewhere
## ## # {
## ## # break ()
## ## # xnew <- xold
## ## # dnew <- dold
## ## # sold <- snew
## ## # aold <- anew
## ## # }
## ## if ((itel == itmax) || ((sold - snew) < acc))
## ## break ()
## ## itel <- itel + 1
## ## xold <- xnew
## ## dold <- dnew
## ## sold <- snew
## ## aold <- anew
## ## }
## ## attr(xnew,"dimnames")[[2]] <- paste("D",1:p,sep="")
## ## xnew <- xnew/enorm(xnew)
## ## doutm <- mkPower(sqdist(xnew),r)
## ## deltam <- delta
## ## delta <- stats::as.dist(delta)
## ## deltaorig <- stats::as.dist(deltaorig)
## ## deltaold <- stats::as.dist(deltaold)
## ## #doute <- doutm/enorm(doutm) #this is an issue here!
## ## #doute <- stats::as.dist(doute)
## ## dout <- stats::as.dist(doutm)
## ## weightmatm <-weightmat
## ## #resmat <- weightmatm*as.matrix((delta - doute)^2) #BUG
## ## resmat <- weightmatm*as.matrix((deltam - doutm)^2) #BUG
## ## spp <- colMeans(resmat) #BUG
## ## weightmat <- stats::as.dist(weightmatm)
## ## #stressen <- sum(weightmat*(doute-delta)^2)
## ## if(verbose>1) cat("*** Stress:",snew, "; Stress 1 (default reported):",sqrt(snew), "\n")
## ## out <- list(delta=deltaorig, tdelta=deltaold, dhats=delta, confdist=dout, conf = xnew, parameters=c(kappa=kappa,lambda=lambda,nu=nu), pars=c(kappa=kappa,lambda=lambda,nu=nu), theta=c(kappa=kappa,lambda=lambda,nu=nu), niter = itel, spp=spp, ndim=p, model="Power-Stress SMACOF", call=match.call(), nobj = dim(xnew)[1], type = type, stress=sqrt(snew), stress.m=snew, stress.en=stressen,resmat=resmat,weightmat=weightmat, alpha = anew, sigma = snew)
## ## class(out) <- c("smacofP","smacofB","smacof")
## ## out
## ## }
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## # #' Torgerson scaling
## # #'
## # #' @param delta symmetric, numeric matrix of distances
## # #' @param p target space dimensions
## # #' @return a n x p matrix (the configuration)
## # #' @export
## # #' @examples
## # #' dis<-as.matrix(smacof::kinshipdelta)
## # #' res<-torgerson(dis)
## #torgerson <- function(delta, p = 2) {
## # z <- eigen(-doubleCenter((as.matrix (delta) ^ 2)/2))
## # v <- pmax(z$values,0)
## # return(z$vectors[,1:p]%*%diag(sqrt(v[1:p])))
## #}
## #' Power Stress SMACOF
## #'
## #' An implementation to minimize power stress by majorization with ratio or interval optimal scaling. Usually more accurate but slower than powerStressFast. Uses a repeat loop.
## #'
## #' @param delta dist object or a symmetric, numeric data.frame or matrix of distances
## #' @param kappa power of the transformation of the fitted distances; defaults to 1
## #' @param lambda the power of the transformation of the proximities; defaults to 1
## #' @param nu the power of the transformation for weightmat; defaults to 1
## #' @param type what type of MDS to fit. One of "ratio" or "interval". Default is "ratio".
## #' @param weightmat a matrix of finite weights
## #' @param init starting configuration
## #' @param ndim dimension of the configuration; defaults to 2
## #' @param acc numeric accuracy of the iteration. Default is 1e-6.
## #' @param itmax maximum number of iterations. Default is 10000.
## #' @param verbose should iteration output be printed; if > 1 then yes
## #' @param principal If ‘TRUE’, principal axis transformation is applied to the final configuration
## #'
## #' @return a smacofP object (inheriting form smacofB, see \code{\link{smacofSym}}). It is a list with the components
## #' \itemize{
## #' \item delta: Observed, untransformed dissimilarities
## #' \item tdelta: Observed explicitly transformed dissimilarities, normalized
## #' \item dhat: Explicitly transformed dissimilarities (dhats), optimally scaled and normalized
## #' \item confdist: Configuration dissimilarities
## #' \item conf: Matrix of fitted configuration
## #' \item stress: Default stress (stress 1; sqrt of explicitly normalized stress)
## #' \item spp: Stress per point
## #' \item ndim: Number of dimensions
## #' \item model: Name of smacof model
## #' \item niter: Number of iterations
## #' \item nobj: Number of objects
## #' \item type: Type of MDS model
## #' \item weightmat: weighting matrix as supplied
## #' \item stress.m: Default stress (stress-1^2)
## #' \item tweightmat: transformed weighthingmatrix (here weightmat^nu)
## #' }
## #'
## #' @section Note:
## #' The functionality related to power stress and the smacofP class is also available in the stops package (\code{\link[stops]{powerStressMin}}). Expect masking when both are loaded.
## #'
## #' @importFrom stats dist as.dist
## #' @importFrom smacofx spp
## #'
## #' @seealso \code{\link{smacofSym}}
## #'
## #' @examples
## #' dis<-smacof::kinshipdelta
## #' res<-powerStressMin(as.matrix(dis),kappa=2,lambda=1.5,itmax=1000)
## #' res
## #' summary(res)
## #' plot(res)
## #'
## #' @export
## powerStressMin <- function (delta, kappa=1, lambda=1, nu=1, type="ratio", weightmat=1-diag(nrow(delta)), init=NULL, ndim = 2, acc= 1e-6, itmax = 10000, verbose = FALSE, principal=FALSE) {
## if(inherits(delta,"dist") || is.data.frame(delta)) delta <- as.matrix(delta)
## if(!isSymmetric(delta)) stop("Delta is not symmetric.\n")
## if(inherits(weightmat,"dist") || is.data.frame(weightmat)) weightmat <- as.matrix(weightmat)
## if(!isSymmetric(weightmat)) stop("weightmat is not symmetric.\n")
## type <- match.arg(type, c("ratio", "interval",several.ok = FALSE))
## trans <- type
## typo <- type
## if (trans=="ratio"){
## trans <- "none"
## }
## if(verbose>0) cat("Minimizing",type,"power-stress with kappa=",kappa,"lambda=",lambda,"nu=",nu,"\n")
## n <- nrow (delta)
## r <- kappa/2
## p <- ndim
## if (p > (n - 1)) stop("Maximum number of dimensions is n-1!")
## if(is.null(rownames(delta))) rownames(delta) <- 1:n
## labos <- rownames(delta) #labels
## deltaorig <- delta
## delta <- delta^lambda
## weightmato <- weightmat
## weightmat <- weightmat^nu
## weightmat[!is.finite(weightmat)] <- 0
## delta <- delta / enorm (delta, weightmat)
## disobj <- smacof::transPrep(as.dist(delta), trans = trans, spline.intKnots = 2, spline.degree = 2)
## deltaold <- delta
## itel <- 1
## xold <- init
## if(is.null(init)) xold <- smacof::torgerson (delta, p = p)
## xstart <- xold
## xold <- xold / enorm (xold)
## n <- nrow (xold)
## nn <- diag (n)
## dold <- sqdist (xold)
## #Optimal scaling
## eold <- as.dist(sqrt(dold))
## dhat <- smacof::transform(eold, disobj, w = as.dist(weightmat), normq = 0.5)
## dhatt <- dhat$res
## dhatd <- structure(dhatt, Size = n, call = quote(as.dist.default(m=b)), class = "dist", Diag = FALSE, Upper = FALSE)
## delta <- as.matrix(dhatd)
## rold <- sum (weightmat * delta * mkPower (dold, r))
## nold <- sum (weightmat * mkPower (dold, 2 * r))
## aold <- rold / nold
## sold <- 1 - 2 * aold * rold + (aold ^ 2) * nold
## #Optimizing
## repeat {
## p1 <- mkPower (dold, r - 1)
## p2 <- mkPower (dold, (2 * r) - 1)
## by <- mkBmat (weightmat * delta * p1)
## cy <- mkBmat (weightmat * p2)
## ga <- 2 * sum (weightmat * p2)
## be <- (2 * r - 1) * (2 ^ r) * sum (weightmat * delta)
## de <- (4 * r - 1) * (4 ^ r) * sum (weightmat)
## if (r >= 0.5) {
## my <- by - aold * (cy - de * nn)
## }
## if (r < 0.5) {
## my <- (by - be * nn) - aold * (cy - ga * nn)
## }
## xnew <- my %*% xold
## xnew <- xnew / enorm (xnew)
## dnew <- sqdist (xnew)
## #Optimal scaling
## e <- as.dist(sqrt(dnew)) #I need the dist(x) here for interval
## dhat2 <- smacof::transform(e, disobj, w = as.dist(weightmat), normq = 0.5) ## dhat update
## dhatt <- dhat2$res
## dhatd <- structure(dhatt, Size = n, call = quote(as.dist.default(m=b)), class = "dist", Diag = FALSE, Upper = FALSE)
## delta <- as.matrix(dhatd)
## rnew <- sum (weightmat * delta * mkPower (dnew, r))
## nnew <- sum (weightmat * mkPower (dnew, 2 * r))
## anew <- rnew / nnew
## snew <- 1 - 2 * anew * rnew + (anew ^ 2) * nnew
## if(is.na(snew)) #if there are issues with the values
## {
## snew <- sold
## dnew <- dold
## anew <- aold
## xnew <- xold
## }
## if (verbose>2) {
## cat (
## formatC (itel, width = 4, format = "d"),
## formatC (
## sold,
## digits = 10,
## width = 13,
## format = "f"
## ),
## formatC (
## snew,
## digits = 10,
## width = 13,
## format = "f"
## ),
## "\n"
## )
## }
## if ((itel == itmax) || ((sold - snew) < acc))
## break ()
## itel <- itel + 1
## xold <- xnew
## dold <- dnew
## sold <- snew
## aold <- anew
## }
## xnew <- xnew/enorm(xnew)
## ## relabeling as they were removed in the optimal scaling
## rownames(delta) <- labos
## attr(xnew,"dimnames")[[1]] <- rownames(delta)
## attr(xnew,"dimnames")[[2]] <- paste("D",1:p,sep="")
## doutm <- mkPower(sqdist(xnew),r)
## deltam <- delta
## delta <- stats::as.dist(delta)
## #delta <- structure(delta, Size = n, call = quote(as.dist.default(m=b)),
## # class = "dist", Diag = FALSE, Upper = FALSE)
## deltaorig <- stats::as.dist(deltaorig)
## deltaold <- stats::as.dist(deltaold)
## #doute <- doutm/enorm(doutm) #this is an issue here!
## #doute <- stats::as.dist(doute)
## dout <- stats::as.dist(doutm)
## weightmatm <-weightmat
## weightmat <- stats::as.dist(weightmatm)
## weightmato <- stats::as.dist(weightmato)
## #resmat <- weightmatm*as.matrix((deltam - doutm)^2) #old spp now we sum up to 100
## #spp <- colMeans(resmat)
## spoint <- smacofx::spp(delta, dout, weightmat)
## resmat<-spoint$resmat
## rss <- sum(spoint$resmat[lower.tri(spoint$resmat)])
## spp <- spoint$spp
## #spp <- colMeans(resmat)
## if (principal) {
## xnew_svd <- svd(xnew)
## xnew <- xnew %*% xnew_svd$v
## }
## #stressen <- sum(weightmat*(doute-delta)^2)
## if(verbose>1) cat("*** Stress:",snew, "; Stress 1 (default reported):",sqrt(snew), "\n")
## out <- list(delta=deltaorig, dhat=delta, confdist=dout, iord=dhat2$iord.prim, conf = xnew, stress=sqrt(snew), spp=spp, ndim=p, weightmat=weightmato, resmat=resmat, rss=rss, init=xstart, model="Power-Stress SMACOF", niter = itel,nobj = dim(xnew)[1], type = type, call=match.call(), stress.m=snew, alpha = anew, sigma = snew, tdelta=deltaold, parameters=c(kappa=kappa,lambda=lambda,nu=nu), pars=c(kappa=kappa,lambda=lambda,nu=nu), theta=c(kappa=kappa,lambda=lambda,nu=nu),tweightmat=weightmat)
## class(out) <- c("smacofP","smacofB","smacof")
## out
## }
## #' @rdname powerStressMin
## #' @export
## powerstressMin <- powerStressMin
## #' @rdname powerStressMin
## #' @export
## postmds <- powerStressMin
## #' @rdname powerStressMin
## #' @export
## pstressMin <- powerStressMin
## #' @rdname powerStressMin
## #' @export
## pStressMin <- powerStressMin
## #' @rdname powerStressMin
## #' @export
## pstressmds <- powerStressMin
## ## #' Power Stress SMACOF
## ## #'
## ## #' An old implementation to minimize power stress by minimization-majorization. Usually more accurate but slower than powerStressFast. Uses a repeat loop.
## ## #'
## ## #' @param delta dist object or a symmetric, numeric data.frame or matrix of distances
## ## #' @param kappa power of the transformation of the fitted distances; defaults to 1
## ## #' @param lambda the power of the transformation of the proximities; defaults to 1
## ## #' @param nu the power of the transformation for weightmat; defaults to 1
## ## #' @param weightmat a matrix of finite weights
## ## #' @param init starting configuration
## ## #' @param ndim dimension of the configuration; defaults to 2
## ## #' @param acc numeric accuracy of the iteration. Default is 1e-6.
## ## #' @param itmax maximum number of iterations. Default is 10000.
## ## #' @param verbose should iteration output be printed; if > 1 then yes
## ## #'
## ## #' @return a smacofP object (inheriting form smacofB, see \code{\link{smacofSym}}). It is a list with the components
## ## #' \itemize{
## ## #' \item delta: Observed input dissimilarities, not normalized
## ## #' \item tdelta: Explicitly transformed input dissimilarities, not normalized
## ## #' \item dhats: Explicitly transformed dissimilarities, optimally scaled and normalized
## ## #' \item confdist: Configuration dissimilarities
## ## #' \item conf: Matrix of fitted configuration
## ## #' \item stress: Default stress (stress 1; sqrt of explicitly normalized stress)
## ## #' \item spp: Stress per point (based on stress.en)
## ## #' \item ndim: Number of dimensions
## ## #' \item model: Name of smacof model
## ## #' \item niter: Number of iterations
## ## #' \item nobj: Number of objects
## ## #' \item type: Type of MDS model
## ## #' }
## ## #' and some additional components
## ## #' \itemize{
## ## #' \item stress.m: default stress for the COPS and STOP defaults to the explicitly normalized stress on the normalized, transformed dissimilarities
## ## #' \item weightmat: weighting matrix
## ## #'}
## ## #'
## ## #' @section Note:
## ## #' The functionality related to power stress and the smacofP class is also available in the stops package (\code{\link[stops]{powerStressMin}}). Expect masking when both are loaded.
## ## #'
## ## #' @importFrom stats dist as.dist
## ## #'
## ## #' @seealso \code{\link{smacofSym}}
## ## #'
## ## #' @examples
## ## #' dis<-smacof::kinshipdelta
## ## #' res<-powerStressMin(as.matrix(dis),kappa=2,lambda=1.5,itmax=1000)
## ## #' res
## ## #' summary(res)
## ## #' plot(res)
## ## #'
## ## #' @export
## ## powerStressMinOLD <- function (delta, kappa=1, lambda=1, nu=1, weightmat=1-diag(nrow(delta)), init=NULL, ndim = 2, acc= 1e-6, itmax = 10000, verbose = FALSE) {
## ## if(inherits(delta,"dist") || is.data.frame(delta)) delta <- as.matrix(delta)
## ## if(!isSymmetric(delta)) stop("Delta is not symmetric.\n")
## ## if(verbose>0) cat("Minimizing powerStress with kappa=",kappa,"lambda=",lambda,"nu=",nu,"\n")
## ## type <- "ratio"
## ## r <- kappa/2
## ## p <- ndim
## ## deltaorig <- delta #untransformed non-normalized input dissimilarities
## ## delta <- delta^lambda
## ## #weightmato <- weightmat
## ## weightmat <- weightmat^nu
## ## weightmat[!is.finite(weightmat)] <- 1
## ## deltaold <- delta #explicitly transformed non-normalized input dissimilarities
## ## delta <- delta / enorm (delta, weightmat)
## ## itel <- 1
## ## xold <- init
## ## if(is.null(init)) xold <- smacof::torgerson (delta, p = p)
## ## xold <- xold / enorm (xold)
## ## n <- nrow (xold)
## ## nn <- diag (n)
## ## dold <- sqdist (xold)
## ## rold <- sum (weightmat * delta * mkPower (dold, r))
## ## nold <- sum (weightmat * mkPower (dold, 2 * r))
## ## aold <- rold / nold
## ## sold <- 1 - 2 * aold * rold + (aold ^ 2) * nold
## ## repeat {
## ## p1 <- mkPower (dold, r - 1)
## ## p2 <- mkPower (dold, (2 * r) - 1)
## ## by <- mkBmat (weightmat * delta * p1)
## ## cy <- mkBmat (weightmat * p2)
## ## ga <- 2 * sum (weightmat * p2)
## ## be <- (2 * r - 1) * (2 ^ r) * sum (weightmat * delta)
## ## de <- (4 * r - 1) * (4 ^ r) * sum (weightmat)
## ## if (r >= 0.5) {
## ## my <- by - aold * (cy - de * nn)
## ## }
## ## if (r < 0.5) {
## ## my <- (by - be * nn) - aold * (cy - ga * nn)
## ## }
## ## xnew <- my %*% xold
## ## xnew <- xnew / enorm (xnew)
## ## dnew <- sqdist (xnew)
## ## rnew <- sum (weightmat * delta * mkPower (dnew, r))
## ## nnew <- sum (weightmat * mkPower (dnew, 2 * r))
## ## anew <- rnew / nnew
## ## snew <- 1 - 2 * anew * rnew + (anew ^ 2) * nnew
## ## if(is.na(snew)) #if there are issues with the values
## ## {
## ## snew <- sold
## ## dnew <- dold
## ## anew <- aold
## ## xnew <- xold
## ## }
## ## if (verbose>2) {
## ## cat (
## ## formatC (itel, width = 4, format = "d"),
## ## formatC (
## ## sold,
## ## digits = 10,
## ## width = 13,
## ## format = "f"
## ## ),
## ## formatC (
## ## snew,
## ## digits = 10,
## ## width = 13,
## ## format = "f"
## ## ),
## ## "\n"
## ## )
## ## }
## ## # if(is.na(snew)) #to avoid numerical issues if there are zeros somewhere
## ## # {
## ## # break ()
## ## # xnew <- xold
## ## # dnew <- dold
## ## # sold <- snew
## ## # aold <- anew
## ## # }
## ## if ((itel == itmax) || ((sold - snew) < acc))
## ## break ()
## ## itel <- itel + 1
## ## xold <- xnew
## ## dold <- dnew
## ## sold <- snew
## ## aold <- anew
## ## }
## ## attr(xnew,"dimnames")[[2]] <- paste("D",1:p,sep="")
## ## xnew <- xnew/enorm(xnew)
## ## doutm <- mkPower(sqdist(xnew),r)
## ## deltam <- delta
## ## delta <- stats::as.dist(delta)
## ## deltaorig <- stats::as.dist(deltaorig)
## ## deltaold <- stats::as.dist(deltaold)
## ## #doute <- doutm/enorm(doutm) #this is an issue here!
## ## #doute <- stats::as.dist(doute)
## ## dout <- stats::as.dist(doutm)
## ## weightmatm <-weightmat
## ## #resmat <- weightmatm*as.matrix((delta - doute)^2) #BUG
## ## resmat <- weightmatm*as.matrix((deltam - doutm)^2) #BUG
## ## spp <- colMeans(resmat) #BUG
## ## weightmat <- stats::as.dist(weightmatm)
## ## #stressen <- sum(weightmat*(doute-delta)^2)
## ## if(verbose>1) cat("*** Stress:",snew, "; Stress 1 (default reported):",sqrt(snew), "\n")
## ## out <- list(delta=deltaorig, tdelta=deltaold, dhats=delta, confdist=dout, conf = xnew, parameters=c(kappa=kappa,lambda=lambda,nu=nu), pars=c(kappa=kappa,lambda=lambda,nu=nu), theta=c(kappa=kappa,lambda=lambda,nu=nu), niter = itel, spp=spp, ndim=p, model="Power-Stress SMACOF", call=match.call(), nobj = dim(xnew)[1], type = type, stress=sqrt(snew), stress.m=snew, stress.en=stressen,resmat=resmat,weightmat=weightmat, alpha = anew, sigma = snew)
## ## class(out) <- c("smacofP","smacofB","smacof")
## ## out
## ## }
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