Parallel Factor Analysis

Usage

parafac(
  Tensor,
  nfac,
  nstart = 1,
  maxit = 500,
  max_fn = 10000,
  ctol = 1e-04,
  rel_tol = 1e-08,
  abs_tol = 1e-08,
  grad_tol = 1e-08,
  initialization = "random",
  method = "als",
  verbose = FALSE,
  output = "best",
  sortComponents = FALSE
)

Arguments

Tensor: 3-way matrix of numeric data
nfac: Number of factors (components) to fit.
nstart: Number of models to randomly initialize (default 1).
maxit: Maximum number of iterations allowed without convergence in the ALS case (default 500).
max_fn: Maximum number of function evaluations allowed without convergence in the OPT case (default 10000).
ctol: Relative change in loss tolerated to call the algorithm converged in the ALS case (default 1e-4).
rel_tol: Relative change in loss tolerated to call the algorithm converged in the OPT case (default 1e-8).
abs_tol: Absolute loss tolerated to call the algorithm converged in the OPT case (default 1e-8).
grad_tol: Tolerance on the two-norm of the gradient divided over the number of elements in the gradient in the OPT case (default 1e-8).
initialization: "Random" for randomly initialized input vectors or "nvec" for svd-based best guess.
method: Use ALS algorithm ("als", default) or use all-at-once optimization ("opt"). The all-at-once optimization is based on a nonlinear conjugate gradient method with Hestenes-Stiefel updates and the More-Thuente line search algorithm.
verbose: verbose output
output: String ("best"/"all") Return only the best model of the nstart models ("best") or return all of them in a list object ("all").
sortComponents: Boolean to sort the components based on their variance explained (default FALSE)

Value

List object of the PARAFAC model or models.

Examples

X = array(rnorm(108*100*10), c(108,100,10))
model = parafac(X, 2)