parafac {parafac4microbiome} | R Documentation |
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
)
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 |
|
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)