Convenience wrapper for eigenanatomy decomposition.

Decomposes a matrix into sparse eigenevectors to maximize explained variance.

networkEiganat(
  Xin,
  sparseness = c(0.1, 0.1),
  nvecs = 5,
  its = 5,
  gradparam = 1,
  mask = NA,
  v,
  prior,
  pgradparam = 0.1,
  clustval = 0,
  downsample = 0,
  doscale = T,
  domin = T,
  verbose = F,
  dowhite = 0,
  timeme = T,
  addb = T,
  useregression = T
)

Arguments

Xin: n by p input images , subjects or time points by row , spatial variable lies along columns
sparseness: sparseness pair c( 0.1 , 0.1 )
nvecs: number of vectors
its: number of iterations
gradparam: gradient descent parameter for data
mask: optional antsImage mask
v: the spatial solultion
prior: the prior
pgradparam: gradient descent parameter for prior term
clustval: integer greater than or equal to zero
downsample: bool
doscale: bool
domin: bool
verbose: bool
dowhite: bool
timeme: bool
addb: bool
useregression: bool

Value

outputs a decomposition of a population or time series matrix

Author

Avants BB

Examples

if (FALSE) { # \dontrun{
mat <- replicate(100, rnorm(20))
mydecom <- networkEiganat(mat, nvecs = 5)
ch1 <- usePkg("randomForest")
ch2 <- usePkg("BGLR")
if (ch1 & ch2) {
  data(mice)
  snps <- quantifySNPs(mice.X)
  numericalpheno <- as.matrix(mice.pheno[, c(4, 5, 13, 15)])
  numericalpheno <- residuals(lm(numericalpheno ~
    as.factor(mice.pheno$Litter)))
  phind <- 3
  nfolds <- 6
  train <- sample(rep(c(1:nfolds), 1800 / nfolds))
  train <- (train < 4)
  lowr <- lowrankRowMatrix(as.matrix(snps[train, ]), 900)
  snpdS <- sparseDecom(lowr, nvecs = 2, sparseness = (-0.001), its = 3)
  snpdF <- sparseDecom(lowrankRowMatrix(as.matrix(snps[train, ]), 100),
    nvecs = 2, sparseness = (-0.001), its = 3
  )
  projmat <- as.matrix(snpdS$eig)
  projmat <- as.matrix(snpdF$eig)
  snpdFast <- networkEiganat(as.matrix(snps[train, ]),
    nvecs = 2,
    sparseness = c(1, -0.001), downsample = 45, verbose = T, its = 3,
    gradparam = 10
  )
  snpdSlow <- networkEiganat(as.matrix(snps[train, ]),
    nvecs = 2,
    sparseness = c(1, -0.001), downsample = 0, verbose = T,
    its = 3, gradparam = 10
  )
  snpd <- snpdSlow
  snpd <- snpdFast
  projmat <- as.matrix(snpd$v)
  snpdF <- sparseDecom(lowrankRowMatrix(as.matrix(snps[train, ]), 10),
    nvecs = 2, sparseness = (-0.001), its = 3
  )
  projmat <- as.matrix(snpdS$eig)
  snpse <- as.matrix(snps[train, ]) %*% projmat
  traindf <- data.frame(bmi = numericalpheno[train, phind], snpse = snpse)
  snpse <- as.matrix(snps[!train, ]) %*% projmat
  testdf <- data.frame(bmi = numericalpheno[!train, phind], snpse = snpse)
  myrf <- glm(bmi ~ ., data = traindf)
  preddf <- predict(myrf, newdata = testdf)
  cor.test(preddf, testdf$bmi)
} # ch1 and ch2
###########
} # }