pcp: convenience version of the singular value decomposition

In hddplot: Use Known Groups in High-Dimensional Data to Derive Scores for Plots

convenience version of the singular value decomposition

Description

Packages results from an SVD on what can be either a cases by variables (features) or variables by cases layout, for use in principal component and related calculations

Usage

pcp(x = datasets::USArrests, varscores = TRUE, cases = "rows", center = "vars",
    standardize = FALSE, scale.cases = 1, log = FALSE, sc = 1, reflect = c(1, 1))

Arguments

x	matrix on which SVD is to be performed
varscores	logical; should scores be returned?
cases	specify either "rows" or "columns"
center	logical: if set to "vars", then values of variables will be centered
standardize	logical: should values of variables be standardized to zero mean and unit deviance. Takes precedence over the setting of center
scale.cases	set to a value in [0,1]. scale.cases=0 gives a pure rotation of the variables. scale.cases=1 weights a/c the singular values
log	logical: should logarithms be taken, prior to the calculation?
sc	the variable scores are divided by sqrt{sc-1}. By default, sc = number of cases
reflect	a vector of two elements, by default c(1,1). Use of -1 in one or both positions can be useful in reconciling results with output from other software

Value

g	case scores
h	variable scores
avv	variable means
sdev	singular values, divides by the square root of one less than the number of cases

Author(s)

John Maindonald

See Also

La.svd

Examples

USArrests.svd <- pcp(x = datasets::USArrests)

## The function is currently defined as
function(x=datasets::USArrests,
           varscores=TRUE,
           cases="rows",
           center="vars",
           standardize=FALSE,
           scale.cases=1,
           log=FALSE,
           sc=1,
           reflect=c(1,1))
{
  x <- as.matrix(x)
  avv <- 0
  sdv <- 1
  casedim <- 2-as.logical(cases=="rows")
  vardim <- 3-casedim
  ## casedim=1 if rows are cases; otherwise casedim=2
  ## scale.cases=0 gives a pure rotation of the variables
  ## scale.cases=1 weights a/c the singular values
  ncases <- dim(x)[casedim]
  nvar <- dim(x)[vardim]
  if(is.null(sc))sc <- dim(x)[casedim]-1
  if(log)x <- log(x, base=2)
  if(standardize){
    avv <- apply(x, vardim, mean)
    sdv <- apply(x, vardim, sd)
    x <- sweep(x, vardim, avv,"-")
    x <- sweep(x, vardim, sdv,"/")
  }
  else if(as.logical(match("vars", center, nomatch=0))){
    avv <- apply(x,vardim, mean)
    x <- sweep(x, vardim, avv,"-")}

  svdx <- La.svd(x, method = c("dgesdd"))
  h <- NULL
  if(cases=="rows"){
    g <- sweep(svdx$u, 2, svdx$d^scale.cases, "*")*sqrt(sc)
    if(varscores)
      h <- t((svdx$d^(1-scale.cases)* svdx$vt ))/sqrt(sc)
  }
  else if(cases=="columns"){
    g <- sweep(t(svdx$vt), 2, svdx$d^scale.cases, "*")*sqrt(sc)
    if(varscores)
      h <- sweep(svdx$u, 2, svdx$d^(1-scale.cases),"*")/sqrt(sc)
  }
  invisible(list(g=g, rotation=h, av=avv, sdev=svdx$d/sqrt(ncases-1)))
  }

hddplot documentation built on Sept. 14, 2023, 5:07 p.m.