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

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,
  ctol = 1e-04,
  maxit = 500,
  numCores = 1
)

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).

ctol

Relative change in loss tolerated to call the algorithm converged in the ALS case (default 1e-4).

maxit

Maximum number of iterations allowed without convergence (default 500).

numCores

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

Examples

X = Fujita2023$data

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