parafac4microbiome (version 1.1.2)

assessModelQuality: Create randomly initialized models to determine the correct number of components by assessing model quality metrics.

Description

Create randomly initialized models to determine the correct number of components by assessing model quality metrics.

Usage

assessModelQuality(
  X,
  minNumComponents = 1,
  maxNumComponents = 5,
  numRepetitions = 100,
  method = "als",
  ctol = 1e-06,
  maxit = 2500,
  max_fn = 10000,
  rel_tol = 1e-08,
  abs_tol = 1e-08,
  grad_tol = 1e-08,
  numCores = 1
)

Value

A list object of the following:

• plots: Plots of all assessed metrics and an overview plot showing a summary of all of them.

• metrics: metrics of every created model (number of iterations, sum of squared errors, CORCONDIA score and variance explained).

• models: all created models.

Arguments

X

Input data

minNumComponents

Minimum number of components (default 1).

maxNumComponents

Maximum number of components (default 5).

numRepetitions

Number of randomly initialized models to create (default 100).

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.

ctol

Change in SSQ needed for model to be converged (default 1e-6).

maxit

Maximum number of iterations (default 2500).

max_fn

Maximum number of function evaluations allowed without convergence in the OPT case (default 10000).

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).

numCores

Number of cores to use. If set larger than 1, it will run the job in parallel (default 1)

Examples

X = Fujita2023$data

# Run assessModelQuality with less strict convergence parameters as example
assessment = assessModelQuality(X,
                                minNumComponents=1,
                                maxNumComponents=3,
                                numRepetitions=5,
                                ctol=1e-4,
                                maxit=250)
assessment$plots$overview