Compare Fitted Kinetic Models

Compares performance metrics across multiple fitted kinetic models.

Usage

compare_models(...)

Arguments

...

Named fitted model objects.

Value

A data frame summarizing model performance metrics for each fitted model.

Details

Model comparison metrics typically include:

• R-squared (R²)

• Root Mean Squared Error (RMSE)

• Residual Sum of Squares (RSS)

• Akaike Information Criterion (AIC)

• Bayesian Information Criterion (BIC)

This function helps researchers identify models that provide the best balance between goodness of fit and model complexity.

The resulting comparison table can be used with:

• rank_models()

• plot_model_performance()

• plot_model_rankings()

See also

rank_models, compare_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
)
#> Warning: Large negative pressure values detected. Minimum PSI = -1.274 . Please inspect the affected bottles.
#> rumenGP data validation passed.
#> Observations: 1752
#> Heads: 24
#> Treatments: 5

gompertz_fit <- fit_gompertz(
  gp
)
#> Warning: Large negative pressure values detected. Minimum PSI = -1.274 . Please inspect the affected bottles.
#> rumenGP data validation passed.
#> Observations: 1752
#> Heads: 24
#> Treatments: 5

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

comparison
#>      Model Bottles Successful_Fits Failed_Fits   Mean_R2 Mean_RMSE  Mean_RSS
#> 1    Groot      24              23           1 0.9768621  2.230506  518.1984
#> 2 Gompertz      24              24           0 0.9134729  4.305830 1838.9408
#>   Mean_AIC Mean_BIC Lambda_Boundary
#> 1 302.3719 311.4785               0
#> 2 400.7494 409.9112               8