parafac: Parallel Factor Analysis

In parafac4microbiome: Parallel Factor Analysis Modelling of Longitudinal Microbiome Data

Parallel Factor Analysis

Description

Parallel Factor Analysis

Usage

parafac(
  Tensor,
  nfac,
  nstart = 1,
  maxit = 500,
  ctol = 1e-04,
  initialization = "random",
  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 (default 500).
ctol	Relative change in loss tolerated to call the algorithm converged in the ALS case (default 1e-4).
initialization	"Random" for randomly initialized input vectors or "nvec" for svd-based best guess.
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)

parafac4microbiome documentation built on Aug. 21, 2026, 9:14 a.m.