Getting Started with rumenGP

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

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title: "Getting Started with rumenGP" output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{Getting Started with rumenGP} %\VignetteEngine{knitr::rmarkdown} \usepackage[utf8]{inputenc}

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knitr::opts_chunk$set(
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Introduction

rumenGP provides a complete workflow for analyzing in vitro rumen gas production experiments.

The package supports:

• ANKOM RF datasets
• Manual gas-volume datasets
• Pressure-based datasets
• Twelve built-in kinetic models
• User-defined kinetic models
• Model comparison and ranking
• Treatment-level model evaluation
• Diagnostic and visualization tools

This vignette demonstrates a complete workflow using the packaged ANKOM example dataset.

library(rumenGP)

Load Example Data

The package includes a small example dataset.

files <- example_data()

files

Import ANKOM Data

Import the ANKOM RF output file and metadata table.

raw_data <- read_ankom(
  files$ankom
)

metadata <- read_metadata(
  files$metadata
)

Validate Metadata

Before processing data, validate the metadata table.

metadata <- validate_metadata(
  metadata
)

Process ANKOM Data

Convert pressure measurements into cumulative gas production.

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

Validate Processed Data

The resulting dataset is a standardized rumen_gp object.

gp <- validate_ankom(
  gp
)

class(gp)

Inspect the data:

head(gp)

Visualize Raw Gas Production

Individual bottle profiles can be visualized.

plot_gp(
  gp,
  head = "1"
)

Fit Kinetic Models

Several built-in models are available.

groot_fit <- fit_groot(gp)

gompertz_fit <- fit_gompertz(gp)

brody_fit <- fit_brody(gp)

Summarize Model Fits

Each model provides parameter estimates and diagnostic statistics.

summary(groot_fit)

Identify Potentially Problematic Bottles

flags <- flag_model(
  groot_fit
)

head(flags)

Plot Model Fits

Observed and predicted values can be visualized.

plot_fit(
  groot_fit,
  head = "1"
)

Plot Residuals

Residual plots help identify systematic deviations.

plot_residuals(
  groot_fit,
  head = "1"
)

Compare Models

Compare model performance using multiple metrics.

comparison <- compare_models(

  Groot = groot_fit,

  Gompertz = gompertz_fit,

  Brody = brody_fit

)

comparison

The comparison table includes:

• Mean R-squared
• Mean RMSE
• Mean RSS
• Mean AIC
• Mean BIC
• Number of successful fits

Rank Models

rank_models(
  comparison
)

Compare Models by Treatment

Treatment-level comparisons are also available.

treatment_comparison <-
  compare_models_by_treatment(

    Groot = groot_fit,

    Gompertz = gompertz_fit,

    Brody = brody_fit

  )

treatment_comparison

Rank Models by Treatment

ranked_treatments <-
  rank_models_by_treatment(
    treatment_comparison
  )

ranked_treatments

Determine the Best Model per Treatment

best_models <-
  best_model_by_treatment(
    ranked_treatments
  )

best_models

Model Win Frequency

model_win_frequency(
  best_models
)

Quality Control Workflow

A typical workflow is:

Import data
    ↓
Validate metadata
    ↓
Process ANKOM data
    ↓
Validate processed data
    ↓
Fit models
    ↓
Flag problematic bottles
    ↓
Inspect residuals
    ↓
Exclude problematic bottles
    ↓
Refit models
    ↓
Compare models

Example bottle exclusion:

gp_clean <- exclude_heads(
  gp,
  heads = c("10"),
  reason = "Sensor malfunction"
)

Available Models

Current built-in models:

• Brody
• Dual Logistic
• EXP0
• EXPL
• Gompertz
• Groot
• LE0
• LEL
• Logistic
• Mitscherlich
• Michaelis-Menten
• Orskov and McDonald

Note on Groot and Michaelis-Menten

The Groot and generalized Michaelis-Menten models are mathematically equivalent.

Parameter correspondence:

• VF = A
• b = K
• k = c

Researchers may choose either formulation depending on the terminology commonly used in their field.

Next Steps

Additional package capabilities include:

Importing Manual Datasets

gp <- as_rumen_gp(
  data = my_data,
  head_col = "Bottle",
  time_col = "Time",
  gas_col = "Gas"
)

Importing Pressure Data

gp <- as_rumen_gp(
  data = my_data,
  head_col = "Bottle",
  time_col = "Time",
  pressure_col = "PSI",
  pressure_unit = "psi",
  headspace_volume = 60
)

User-Defined Models

custom_fit <- fit_custom(
  data = gp,
  formula =
    Gas_mL ~
      A *
      (
        Time_h /
        (
          Time_h + K
        )
      ),
  start = list(
    A = 150,
    K = 10
  ),
  lower = c(
    A = 0,
    K = 0
  ),
  model_name = "Hyperbolic"
)

See:

?as_rumen_gp
?fit_custom

for additional details.

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