rank_models: Rank Models

In rumenGP: Rumen Gas Production Modeling, Comparison, and Visualization

Rank Models

Description

Ranks fitted models using multiple model performance criteria.

Usage

rank_models(comparison)

Arguments

comparison

Output of compare_models().

Details

Rankings are based on metrics produced by compare_models() and may include:

• R-squared (R²)

• Root Mean Squared Error (RMSE)

• Residual Sum of Squares (RSS)

• Akaike Information Criterion (AIC)

• Bayesian Information Criterion (BIC)

Models that perform consistently well across multiple metrics typically receive better overall rankings.

This function is useful when comparing several competing kinetic models and identifying those that provide the best balance between fit quality and model complexity.

Value

A data frame containing model rankings across performance metrics.

See Also

compare_models, rank_models_by_treatment, plot_model_performance, plot_model_rankings

Examples

files <- example_data()

raw_data <- read_ankom(
  files$ankom
)

metadata <- read_metadata(
  files$metadata
)

gp <- process_ankom(
  raw_data,
  metadata,
  headspace_ml = 210,
  temperature_c = 39
)

groot_fit <- fit_groot(
  gp
)

gompertz_fit <- fit_gompertz(
  gp
)

comparison <- compare_models(
  Groot = groot_fit,
  Gompertz = gompertz_fit
)

rank_models(
  comparison
)

rumenGP documentation built on Oct. 2, 2026, 5:09 p.m.