Initialize simlr

Four initialization approaches for simlr. Returns either a single matrix derived from dimensionality reduction on all matrices (jointReduction=TRUE) or a list of reduced dimensionality matrices, one for each input. Either PCA or ICA can be used as the reduction method.

initializeSimlr(
  voxmats,
  k,
  jointReduction = TRUE,
  zeroUpper = FALSE,
  uAlgorithm = "ica",
  addNoise = 0
)

Arguments

voxmats	list that contains the named matrices.
k	rank of U matrix
jointReduction	boolean determining whether one or length of list bases are
zeroUpper	boolean determining whether upper triangular part of initialization is zeroed out
uAlgorithm	either "random", "randomProjection", eigenvalue, "pca" (default), "ica" or "cca"
addNoise	scalar value that adds zero mean unit variance noise, multiplied by the value of addNoise

Value

A single matrix or list of matrices

Author

BB Avants.

Examples

set.seed(1500)
nsub = 3
npix = c(10,6,13)
nk = 2
outcome = initializeSimlr(
  list( matrix(rnorm( nsub * npix[1] ),ncol=npix[1]),
        matrix(rnorm( nsub * npix[2] ),ncol=npix[2]),
        matrix(rnorm( nsub * npix[3] ),ncol=npix[3]) ),
  k = 2, uAlgorithm = 'pca' )