Identify the Best Model for Each Treatment

Returns the top-ranked model within each treatment.

Usage

best_model_by_treatment(ranked_comparison)

Arguments

ranked_comparison

Output from rank_models_by_treatment().

Value

A data frame containing the highest-ranked model for each treatment.

Details

Rankings are obtained from rank_models_by_treatment() and are based on model performance 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 function provides a concise summary of the best-performing model for each treatment and is useful for identifying whether different treatments are best described by different kinetic models.

See also

compare_models_by_treatment, rank_models_by_treatment, model_win_frequency, compare_models

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_by_treatment(
  Groot = groot_fit,
  Gompertz = gompertz_fit
)

ranking <- rank_models_by_treatment(
  comparison
)

best_model_by_treatment(
  ranking
)
#> # A tibble: 5 × 12
#>   Treatment Model Mean_R2 Mean_RMSE Mean_AIC Mean_BIC Rank_R2 Rank_RMSE Rank_AIC
#>   <chr>     <chr>   <dbl>     <dbl>    <dbl>    <dbl>   <int>     <int>    <int>
#> 1 BLANK     Groot   0.969     0.943     201.     210.       1         1        1
#> 2 Plant_A   Groot   0.968     2.40      287.     297.       1         1        1
#> 3 Plant_B   Groot   0.990     2.27      315.     324.       1         1        1
#> 4 Plant_C   Groot   0.974     2.22      320.     329.       1         1        1
#> 5 TMR       Groot   0.988     3.14      377.     386.       1         1        1
#> # ℹ 3 more variables: Rank_BIC <int>, Total_Rank <int>, Overall_Rank <int>