sparseDecomboot: Convenience wrapper for eigenanatomy decomposition.

In stnava/ANTsR: ANTs in R: Quantification Tools for Biomedical Images

Convenience wrapper for eigenanatomy decomposition.

Description

Decomposes a matrix into sparse eigenevectors to maximize explained variance.

Usage

sparseDecomboot(
  inmatrix,
  inmask = NULL,
  sparseness = 0.01,
  nvecs = 50,
  its = 5,
  cthresh = 250,
  z = 0,
  smooth = 0,
  initializationList = list(),
  mycoption = 0,
  nboot = 10,
  nsamp = 0.9,
  robust = 0,
  doseg = TRUE
)

Arguments

inmatrix	n by p input images , subjects or time points by row , spatial variable lies along columns
inmask	optional antsImage mask
sparseness	lower values equal more sparse
nvecs	number of vectors
its	number of iterations
cthresh	cluster threshold
z	u penalty, experimental
smooth	smoothness eg 0.5
initializationList	see initializeEigenanatomy
mycoption	0, 1 or 2 all produce different output 0 is combination of 1 (spatial orthogonality) and 2 (subject space orthogonality)
nboot	boostrap integer e.g. 10 equals 10 boostraps
nsamp	value less than or equal to 1, e.g. 0.9 means 90 percent of data will be used in each boostrap resampling
robust	boolean
doseg	orthogonalize bootstrap results

Author(s)

Avants BB

Examples

mat <- replicate(100, rnorm(20))
mydecom <- sparseDecomboot(mat, nboot = 5, nsamp = 0.9, nvecs = 2)

## Not run: 
# for prediction
if (usePkg("randomForest") & usePkg("spls")) {
  data(lymphoma)
  training <- sample(rep(c(TRUE, FALSE), 31))
  sp <- 0.001
  myz <- 0
  nv <- 5
  ldd <- sparseDecomboot(lymphoma$x[training, ],
    nvecs = nv,
    sparseness = (sp), mycoption = 1, z = myz, nsamp = 0.9, nboot = 50
  ) # NMF style
  outmat <- as.matrix(ldd$eigenanatomyimages)
  # outmat<-t(ldd$cca1outAuto)
  traindf <- data.frame(
    lclass = as.factor(lymphoma$y[training]),
    eig = lymphoma$x[training, ] %*% t(outmat)
  )
  testdf <- data.frame(
    lclass = as.factor(lymphoma$y[!training]),
    eig = lymphoma$x[!training, ] %*% t(outmat)
  )
  myrf <- randomForest(lclass ~ ., data = traindf)
  predlymp <- predict(myrf, newdata = testdf)
  print(paste(
    "N-errors:", sum(abs(testdf$lclass != predlymp)),
    "non-zero ", sum(abs(outmat) > 0)
  ))
  for (i in 1:nv) {
    print(paste(" non-zero ", i, " is: ", sum(abs(outmat[i, ]) > 0)))
  }
}
# end dontrun

## End(Not run)

stnava/ANTsR documentation built on April 16, 2024, 12:17 a.m.