Convenience wrapper for 2-view eigenanatomy decomposition w/bootstrap initialization.

Decomposes two matrices into paired sparse eigenevectors to maximize canonical correlation.

sparseDecom2boot(
  inmatrix,
  inmask = c(NULL, NULL),
  sparseness = c(0.01, 0.01),
  nvecs = 50,
  its = 5,
  cthresh = c(0, 0),
  statdir = NA,
  perms = 0,
  uselong = 0,
  z = 0,
  smooth = 0,
  robust = 0,
  mycoption = 1,
  initializationList = list(),
  initializationList2 = list(),
  ell1 = 0.05,
  nboot = 10,
  nsamp = 1,
  doseg = FALSE,
  priorWeight = 0,
  verbose = FALSE,
  estimateSparseness = 0.2
)

Arguments

inmatrix

input as inmatrix=list(mat1,mat2). n by p input matrix and n by q input matrix , spatial variable lies along columns.

inmask

optional pair of antsImage masks

sparseness

a c(.,.) pair of values e.g c(0.01,0.1) enforces an unsigned 99 percent and 90 percent sparse solution for each respective view

nvecs

number of eigenvector pairs

its

number of iterations, 10 or 20 usually sufficient

cthresh

cluster threshold pair

statdir

temporary directory if you want to look at full output

perms

number of permutations

uselong

enforce solutions of both views to be the same - requires matrices to be the same size

z

subject space (low-dimensional space) sparseness value

smooth

smooth the data (only available when mask is used)

robust

rank transform input matrices

mycoption

enforce 1 - spatial orthogonality, 2 - low-dimensional orthogonality or 0 - both

initializationList

initialization for first view

initializationList2

initialization for 2nd view

ell1

gradient descent parameter, if negative then l0 otherwise use l1

nboot

n bootstrap runs

nsamp

number of samples e.g. 0.9 indicates 90 percent of data

doseg

boolean to control matrix orthogonality during bootstrap

priorWeight

Scalar value weight on prior between 0 (prior is weak) and 1 (prior is strong). Only engaged if initialization is used

verbose

activates verbose output to screen

estimateSparseness

effect size to estimate sparseness per vector

Value

outputs a decomposition of a pair of matrices

Author

Avants BB

Examples

if (FALSE) { # \dontrun{
mat <- replicate(100, rnorm(20))
mat2 <- replicate(100, rnorm(20))
mydecom <- sparseDecom2boot(
  inmatrix = list(mat, mat2),
  sparseness = c(0.1, 0.3), nvecs = 3, its = 3, perms = 0
)
wt <- 0.666
mat3 <- mat * wt + mat2 * (1 - wt)
mydecom <- sparseDecom2boot(
  inmatrix = list(mat, mat3),
  sparseness = c(0.2, 0.2), nvecs = 5, its = 10, perms = 200
)
} # }