Plot Model Rankings

Visualizes model rankings across multiple performance metrics.

Usage

plot_model_rankings(ranking)

Arguments

ranking

Output from rank_models().

Value

A ggplot2 object.

Details

Rankings are typically based on metrics such as:

• R-squared (R²)

• Root Mean Squared Error (RMSE)

• Residual Sum of Squares (RSS)

• Akaike Information Criterion (AIC)

• Bayesian Information Criterion (BIC)

This visualization helps identify models that consistently perform well across several evaluation criteria.

See also

rank_models, compare_models, plot_model_performance

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
)

ranking <- rank_models(
  comparison
)

plot_model_rankings(
  ranking
)