ACMTFR_modelSelection: Model selection for ACMTFR

In CMTFtoolbox: Create (Advanced) Coupled Matrix and Tensor Factorization Models

Model selection for ACMTFR

Description

Model selection for ACMTFR

Usage

ACMTFR_modelSelection(
  datasets,
  modes,
  Y,
  sharedMode = 1,
  maxNumComponents = 5,
  alpha = 1,
  beta = rep(0.001, length(Z$object)),
  epsilon = 1e-08,
  pi = 0.5,
  normalize = TRUE,
  normY = 1,
  method = "CG",
  cg_update = "HS",
  line_search = "MT",
  max_iter = 10000,
  max_fn = 10000,
  abs_tol = 1e-10,
  rel_tol = 1e-10,
  grad_tol = 1e-10,
  nstart = 5,
  numCores = 1,
  cvFolds = 2
)

Arguments

datasets	List of arrays of datasets. Multi-way and two-way may be combined.
modes	Numbered modes per dataset in a list. Example element 1: 1 2 3 and element 2: 1 4 for the X tensor and Y matrix case with a shared subject mode.
Y	Dependent variable (regression part).
sharedMode	Mode that is shared between all blocks, used to remove fibers for numFolds randomly initialized models.
maxNumComponents	Maximum number of components to check (default 3).
alpha	Scalar penalizing the components to be norm 1 (default 1).
beta	Vector of penalty values for each dataset, penalizing the lambda terms (default 1e-3).
epsilon	Scalar value to make it possible to compute the partial derivatives of lambda (default 1e-8).
pi	Pi value of the loss function as specified by Van der Ploeg et al., 2025.
normalize	Normalize the X blocks to frobenius norm 1 (default TRUE).
normY	Normalize Y to a specific value, (default: 1).
method	Optimization method to use (default = "CG", the conjugate gradient). See mize::mize() for other options.
cg_update	Update method for the conjugate gradient algorithm, see mize::mize() for the options (default="HS", Hestenes-Steifel).
line_search	Line search algorithm to use, see mize::mize() for the options (default="MT", More-Thuente).
max_iter	Maximum number of iterations.
max_fn	Maximum number of function evaluations.
abs_tol	Function tolerance criterion for convergence.
rel_tol	Relative function tolerance criterion for convergence.
grad_tol	Absolute tolerence for the l2-norm of the gradient vector.
nstart	Number of models to produce (default 1). If set higher than one, the package will return the best fitted model.
numCores	Number of cores to use (default 1). If set higher than one, the package will attempt to run in parallel.
cvFolds	Number of CV folds to create (default 10).

Value

List object containing plots of all metrics and dataframes containing the data used to create them.

Examples

set.seed(123)

I = 10
J = 5
K = 3
df = array(rnorm(I*J*K), c(I,J,K))
df2 = array(rnorm(I*J*K), c(I,J,K))
datasets = list(df, df2)
modes = list(c(1,2,3), c(1,4,5))
Y = as.matrix(rnorm(I))

# A very small procedure is run to limit computational requirements
result = ACMTFR_modelSelection(datasets,
                              modes,
                              Y,
                              pi=1.0,
                              maxNumComponents=2,
                              nstart=2,
                              cvFolds=2,
                              rel_tol=0.5,
                              abs_tol=0.5)

result$plots$overview

CMTFtoolbox documentation built on Aug. 23, 2025, 1:11 a.m.