parafac4microbiome (version 1.1.2)

parafac: Parallel Factor Analysis

Description

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
)

Value

List object of the PARAFAC model or models.

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)

Examples

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