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rumenGP

rumenGP is an R package for importing, processing, visualizing, fitting, comparing, and interpreting in vitro rumen gas production data.

The package supports:

Installation

Development version:

# install.packages("devtools")

devtools::install_github(
  "araujorodrig-lab/rumenGP"
)

Or install from a local source:

devtools::install_local(
  "path/to/rumenGP"
)

Package Workflow

ANKOM RF
        ↓
process_ankom()
        ↓
     rumen_gp

Manual Gas Volume
        ↓
as_rumen_gp()
        ↓
     rumen_gp

Pressure Data
        ↓
as_rumen_gp()
        ↓
pressure_to_volume()
        ↓
     rumen_gp

             ↓

 Built-in Models
          +
  Custom Models

             ↓

 Comparison
 Ranking
 Diagnostics
 Visualization

Example Workflow: ANKOM Data

library(rumenGP)

Load and Import Example Data

The package includes a small example dataset that can be used to learn the workflow and test package functionality.

files <- example_data()

raw_data <- read_ankom(
  files$ankom
)

metadata <- read_metadata(
  files$metadata
)

Inspect ANKOM Data

The ANKOM RF file contains pressure measurements collected during the incubation period.

head(raw_data)
#> # A tibble: 6 × 52
#>   time_raw   `0`   `1`     `2`    `3`    `4`   `5`   `6`    `7`   `8`    `9`
#>   <chr>    <dbl> <dbl>   <dbl>  <dbl>  <dbl> <dbl> <dbl>  <dbl> <dbl>  <dbl>
#> 1 10:29:34  14.2 0      0      0      0      0     0      0     0     0     
#> 2 10:39:34  14.2 0.873 -0.0728 0.0364 0.0364 0.910 0.218 -0.146 0.873 0.0364
#> 3 10:49:34  14.2 1.42  -0.109  0      0.0364 1.53  0.218  0.764 1.53  0.0364
#> 4 10:59:34  14.2 1.82  -0.109  0      0.0364 2.04  0.218  0.764 2.07  0     
#> 5 11:09:34  14.2 2.11  -0.146  0      0.0364 2.47  0.218  0.764 2.58  0     
#> 6 11:19:34  14.2 2.40  -0.146  0      0.0364 2.84  0.218  0.764 2.98  0     
#> # ℹ 41 more variables: `10` <dbl>, `11` <dbl>, `12` <dbl>, `13` <dbl>,
#> #   `14` <dbl>, `15` <dbl>, `16` <dbl>, `17` <dbl>, `18` <dbl>, `19` <dbl>,
#> #   `20` <lgl>, `21` <dbl>, `22` <dbl>, `23` <dbl>, `24` <dbl>, `25` <lgl>,
#> #   `26` <dbl>, `27` <lgl>, `28` <lgl>, `29` <lgl>, `30` <lgl>, `31` <lgl>,
#> #   `32` <lgl>, `33` <lgl>, `34` <lgl>, `35` <lgl>, `36` <lgl>, `37` <lgl>,
#> #   `38` <lgl>, `39` <lgl>, `40` <lgl>, `41` <lgl>, `42` <lgl>, `43` <lgl>,
#> #   `44` <lgl>, `45` <lgl>, `46` <lgl>, `47` <lgl>, `48` <lgl>, `49` <lgl>, …

Inspect Metadata

The metadata table contains bottle identifiers, treatments, and replicate information.

head(metadata)
#> # A tibble: 6 × 5
#>   Bottle  Head   Rep Treatment    pH
#>    <dbl> <dbl> <dbl> <chr>     <dbl>
#> 1      1     1     1 Plant_A    6.24
#> 2      2     2     2 Plant_A    6.22
#> 3      3     3     3 Plant_A    6.24
#> 4      4     4     4 Plant_A    6.3 
#> 5      5     5     5 Plant_A    6.15
#> 6      6     6     6 Plant_A    6.16

Validate metadata

metadata <- validate_metadata(
  metadata
)
#> Metadata validation passed.
#> Heads: 24
#> Treatment: 5

Process ANKOM data

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

Validate processed data

gp <- validate_ankom(
  gp
)
#> rumenGP data validation passed.
#> Observations: 1752
#> Heads: 24
#> Treatments: 5

Example Workflow: Manual Gas Volume Data

manual_volume <- data.frame(

  Bottle = c(
    1,1,1,
    2,2,2
  ),

  Treatment = c(
    "Control","Control","Control",
    "Corn","Corn","Corn"
  ),

  Time = c(
    0,4,8,
    0,4,8
  ),

  Gas = c(
    0,20,40,
    0,35,60
  )

)

gp_manual <- as_rumen_gp(
  data = manual_volume,
  head_col = "Bottle",
  treatment_col = "Treatment",
  time_col = "Time",
  gas_col = "Gas"
)

head(gp_manual)
#>   Head Bottle Rep Treatment Time_h Gas_mL
#> 1    1      1   1   Control      0      0
#> 2    1      1   1   Control      4     20
#> 3    1      1   1   Control      8     40
#> 4    2      2   1      Corn      0      0
#> 5    2      2   1      Corn      4     35
#> 6    2      2   1      Corn      8     60

Example Workflow: Pressure Data

manual_pressure <- data.frame(

  Bottle = rep(
    1,
    10
  ),

  Time = c(
    0,2,4,6,8,
    12,16,24,36,48
  ),

  PSI = c(
    0,
    0.2,
    0.5,
    0.8,
    1.2,
    1.8,
    2.5,
    3.2,
    4.0,
    4.5
  )

)

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

head(gp_pressure)
#>   Head Bottle Rep Treatment Time_h    Gas_mL
#> 1    1      1   1   Unknown      0 0.0000000
#> 2    1      1   1   Unknown      2 0.7140855
#> 3    1      1   1   Unknown      4 1.7852138
#> 4    1      1   1   Unknown      6 2.8563421
#> 5    1      1   1   Unknown      8 4.2845132
#> 6    1      1   1   Unknown     12 6.4267698

Pressure measurements are automatically converted to gas volume using the same conversion framework used by process_ankom().

Model Fitting

groot_fit <- fit_groot(gp)
#> rumenGP data validation passed.
#> Observations: 1752
#> Heads: 24
#> Treatments: 5

brody_fit <- fit_brody(gp)
#> rumenGP data validation passed.
#> Observations: 1752
#> Heads: 24
#> Treatments: 5

gompertz_fit <- fit_gompertz(gp)
#> rumenGP data validation passed.
#> Observations: 1752
#> Heads: 24
#> Treatments: 5

Model summaries

summary(groot_fit)
#> 
#> Groot model summary
#> -------------------
#> Total bottles: 24
#> Successful fits: 24
#> Failed fits: 0
#> Low R-squared (< 0.90): 3

Compare Models

comparison <- compare_models(

  Groot = fit_groot(gp),

  Brody = fit_brody(gp),

  Gompertz = fit_gompertz(gp)

)
#> rumenGP data validation passed.
#> Observations: 1752
#> Heads: 24
#> Treatments: 5
#> rumenGP data validation passed.
#> Observations: 1752
#> Heads: 24
#> Treatments: 5
#> rumenGP data validation passed.
#> Observations: 1752
#> Heads: 24
#> Treatments: 5

comparison
#>      Model Bottles Successful_Fits Failed_Fits   Mean_R2 Mean_RMSE  Mean_RSS
#> 1    Groot      24              24           0 0.9134994  2.154301  491.4637
#> 2    Brody      24              23           1 0.9453304  3.604892 1076.8697
#> 3 Gompertz      24              23           1 0.9545965  4.216094 1837.5449
#>   Mean_AIC Mean_BIC Lambda_Boundary
#> 1 297.0687 306.1754               0
#> 2 393.6300 402.7918               0
#> 3 392.2245 401.3864               8

Rank Models

rank_models(
  comparison
)
#>      Model Bottles Successful_Fits Failed_Fits   Mean_R2 Mean_RMSE  Mean_RSS
#> 1    Groot      24              24           0 0.9134994  2.154301  491.4637
#> 2    Brody      24              23           1 0.9453304  3.604892 1076.8697
#> 3 Gompertz      24              23           1 0.9545965  4.216094 1837.5449
#>   Mean_AIC Mean_BIC Lambda_Boundary Rank_R2 Rank_RMSE Rank_AIC Rank_BIC
#> 1 297.0687 306.1754               0       3         1        1        1
#> 2 393.6300 402.7918               0       2         2        3        3
#> 3 392.2245 401.3864               8       1         3        2        2

Treatment-Level Comparison

treatment_comparison <-
  compare_models_by_treatment(

    Groot = fit_groot(gp),

    Brody = fit_brody(gp),

    Gompertz = fit_gompertz(gp)

  )
#> rumenGP data validation passed.
#> Observations: 1752
#> Heads: 24
#> Treatments: 5
#> rumenGP data validation passed.
#> Observations: 1752
#> Heads: 24
#> Treatments: 5
#> rumenGP data validation passed.
#> Observations: 1752
#> Heads: 24
#> Treatments: 5

ranked_treatments <-
  rank_models_by_treatment(
    treatment_comparison
  )

best_model_by_treatment(
  ranked_treatments
)
#> # 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   0.714      1.75     257.     266.       3         1        1
#> 2 Plant_A   Groot   0.968      2.33     281.     290.       1         1        1
#> 3 Plant_B   Groot   0.854      1.73     281.     290.       3         1        1
#> 4 Plant_C   Groot   0.982      2.11     309.     319.       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>

User-Defined Models

Researchers can fit their own kinetic equations using fit_custom().

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"

)
#> rumenGP data validation passed.
#> Observations: 1752
#> Heads: 24
#> Treatments: 5

summary(custom_fit)
#> 
#> Custom model summary
#> --------------------
#> Model name: Hyperbolic
#> 
#> Formula:
#> Gas_mL ~ A * (Time_h/(Time_h + K))
#> 
#> Total bottles: 24
#> Successful fits: 23
#> Failed fits: 1
#> Mean R-squared: 0.8965
#> Mean RMSE: 3.5146
#> Mean AIC: 384.8351
#> Mean BIC: 391.7065

Compare custom and built-in models:

compare_models(

  Groot = fit_groot(gp),

  Brody = fit_brody(gp),

  Hyperbolic = custom_fit

)
#> rumenGP data validation passed.
#> Observations: 1752
#> Heads: 24
#> Treatments: 5
#> rumenGP data validation passed.
#> Observations: 1752
#> Heads: 24
#> Treatments: 5
#>        Model Bottles Successful_Fits Failed_Fits   Mean_R2 Mean_RMSE  Mean_RSS
#> 1      Groot      24              24           0 0.9134994  2.154301  491.4637
#> 2      Brody      24              23           1 0.9453304  3.604892 1076.8697
#> 3 Hyperbolic      24              23           1 0.8964862  3.514586 1053.1526
#>   Mean_AIC Mean_BIC Lambda_Boundary
#> 1 297.0687 306.1754               0
#> 2 393.6300 402.7918               0
#> 3 384.8351 391.7065               0

Visualization

Raw gas production

plot_gp(
  gp,
  head = "1"
)

Plot model fit

plot_fit(
  groot_fit,
  head = "1"
)

Plot residuals

plot_residuals(
  groot_fit,
  head = "1"
)

Treatment means

plot_treatment_mean(
  Groot = fit_groot(gp),
  Brody = fit_brody(gp),
  treatment = "Control"
)

Quality Control Workflow

Import data
    ↓
Validate metadata
    ↓
Create rumen_gp object
    ↓
Fit models
    ↓
Flag problematic bottles
    ↓
Inspect residuals
    ↓
Document exclusions
    ↓
Refit models
    ↓
Compare models

Example:

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

Implemented Models

Built-in Models

Model Equivalence

Several model formulations included in rumenGP are mathematically equivalent and are provided because different scientific disciplines and publications prefer different parameterizations.

Groot ↔︎ Generalized Michaelis-Menten

The Groot model

V(t) = VF / [1 + (b/t)^k]

is mathematically equivalent to the generalized Michaelis-Menten model

V(t) = A t^c / (t^c + K^c)

Parameter correspondence:

Both formulations produce identical fitted values, residuals, model diagnostics, AIC, BIC, RMSE, and R-squared when convergence is achieved.

Researchers may choose either formulation according to the terminology commonly used in their field.

Groot ↔︎ Michaelis-Menten ↔︎ Log-logistic

The Log-logistic model

V(t) = VF(rt)^a / [1 + (rt)^a]

can be rewritten as

V(t) = VF t^a / [t^a + (1/r)^a]

which is mathematically identical to the Groot and generalized Michaelis-Menten formulations.

Parameter correspondence:

Groot Michaelis-Menten Log-logistic
VF A VF
b K 1/r
k c a

These formulations describe the same underlying curve and differ only in parameterization.

Consequently, rumenGP already represents the Log-logistic model through the existing Groot and generalized Michaelis-Menten implementations.

Recent Model Additions

Burr XII

Equation:

V(t) = VF [1 - (1 + (rt)a)(-p)]

Parameters:

Advantages:

Limitations:

Inverse Paralogistic

Equation:

V(t) = VF [1 + (rt)(-a)](-a)

Parameters:

Advantages:

Limitations:

Neither Burr XII nor Inverse Paralogistic should be regarded as universally superior.

Model performance depends on feed type, experimental design, data quality, and the model-selection criteria used.

No single gas production model is expected to perform best for every dataset.

A recommended workflow is:

  1. Fit several biologically plausible models.
  2. Verify model convergence.
  3. Inspect fitted curves visually.
  4. Examine residual patterns.
  5. Compare RMSE, AIC, and BIC.
  6. Evaluate parameter plausibility.
  7. Select the model most appropriate for the research objective.

Published comparative work has reported strong performance for Burr XII, Inverse Paralogistic, and Log-logistic formulations across diverse feed datasets.

Because the Log-logistic formulation is mathematically equivalent to both Groot and generalized Michaelis-Menten models, rumenGP already provides this curve family through those existing parameterizations.

Major Features

Data Import

Modeling

Diagnostics

Visualization

Model Comparison

Completed

Development Status

rumenGP is currently maintained and developed as an open-source framework for rumen gas production analysis.

Current releases support:

Future releases may include additional reporting, model-evaluation, and statistical workflows.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.