componentCVPatterns: Cross-Validation of Simplivariate Component Patterns

Description

Performs pure cross-validation over specified patterns with mandatory fitters. This function evaluates different pattern fitting models using cross-validation to determine the best model for a given data subset.

Usage

componentCVPatterns(
  df,
  rows,
  cols,
  patternFunctions,
  patternFitters,
  preferenceOrder = names(patternFunctions),
  nRepeats = 40,
  testFraction = 0.2,
  minCellsForModels = 25,
  parsimonyMargin = 0.05,
  requireFitters = TRUE,
  verbose = FALSE
)

Value

A list containing:

decision: Character, the selected best pattern name
reason: Character, explanation of the selection reasoning
cv: Data frame with CV summary statistics for each model
repeats: Data frame with detailed results from each CV repeat
meta: List with metadata about the CV procedure

Arguments

df: A matrix or data frame containing the data
rows: Row indices to subset from df
cols: Column indices to subset from df
patternFunctions: A named list of pattern functions to evaluate
patternFitters: A named list of fitter functions corresponding to each pattern
preferenceOrder: Character vector specifying the preference order of patterns (default: names of patternFunctions)
nRepeats: Integer, number of cross-validation repeats (default: 40)
testFraction: Numeric, fraction of data to use for testing in each CV fold (default: 0.2)
minCellsForModels: Integer, minimum number of cells required for reliable CV (default: 25)
parsimonyMargin: Numeric, margin for parsimony selection as fraction (default: 0.05)
requireFitters: Logical, whether to require fitters for all patterns (default: TRUE)
verbose: Logical, whether to print progress messages (default: FALSE)