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README.md
In rumenGP: Rumen Gas Production Modeling, Comparison, and Visualization
rumenGP
rumenGP is an R package for importing, processing, visualizing,
fitting, comparing, and interpreting in vitro rumen gas production
data.
The package supports:
- ANKOM RF datasets
- Manually collected gas-volume datasets
- Pressure-based datasets (PSI or kPa)
- Twelve built-in kinetic models
- User-defined kinetic models
- Model comparison and ranking
- Treatment-level model evaluation
- Diagnostic and visualization tools
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
- Brody
- Dual Logistic
- EXP0
- EXPL
- Gompertz
- Groot
- LE0
- LEL
- Logistic
- Mitscherlich
- Michaelis-Menten
- Orskov and McDonald
Model Notes
The Groot and generalized Michaelis-Menten models are mathematically
equivalent.
Parameter correspondence:
- VF = A
- b = K
- k = c
Major Features
Data Import
process_ankom()as_rumen_gp()
Modeling
- 12 built-in models
fit_custom()
Diagnostics
summary()flag_model()exclude_heads()
Visualization
plot_gp()plot_fit()plot_residuals()plot_treatment_mean()
Model Comparison
compare_models()rank_models()compare_models_by_treatment()rank_models_by_treatment()best_model_by_treatment()model_win_frequency()
Completed
- ✅ ANKOM RF workflow
- ✅ Generic data import
- ✅ Pressure-based workflows
- ✅ Twelve kinetic models
- ✅ Custom model framework
- ✅ Model comparison framework
- ✅ Treatment-level model ranking
- ✅ Diagnostic workflows
- ✅ Visualization tools
- ✅ Five package vignettes
- ✅ pkgdown website
- ✅ Clean R CMD check
Development Status
rumenGP is currently maintained and developed as an open-source
framework for rumen gas production analysis. Future releases may include
additional modeling, reporting, and statistical workflows.
Citation
If you use rumenGP in research, please cite:
Rodrigues, A. A. and Mantovani, H. C.
rumenGP:
An R package for rumen gas production
kinetic modeling, model comparison,
and visualization.
(Citation information will be updated as the package develops.)
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rumenGP documentation built on Oct. 2, 2026, 5:09 p.m.
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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:
- ANKOM RF datasets
- Manually collected gas-volume datasets
- Pressure-based datasets (PSI or kPa)
- Twelve built-in kinetic models
- User-defined kinetic models
- Model comparison and ranking
- Treatment-level model evaluation
- Diagnostic and visualization tools
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
- Brody
- Dual Logistic
- EXP0
- EXPL
- Gompertz
- Groot
- LE0
- LEL
- Logistic
- Mitscherlich
- Michaelis-Menten
- Orskov and McDonald
Model Notes
The Groot and generalized Michaelis-Menten models are mathematically equivalent.
Parameter correspondence:
- VF = A
- b = K
- k = c
Major Features
Data Import
process_ankom()as_rumen_gp()
Modeling
- 12 built-in models
fit_custom()
Diagnostics
summary()flag_model()exclude_heads()
Visualization
plot_gp()plot_fit()plot_residuals()plot_treatment_mean()
Model Comparison
compare_models()rank_models()compare_models_by_treatment()rank_models_by_treatment()best_model_by_treatment()model_win_frequency()
Completed
- ✅ ANKOM RF workflow
- ✅ Generic data import
- ✅ Pressure-based workflows
- ✅ Twelve kinetic models
- ✅ Custom model framework
- ✅ Model comparison framework
- ✅ Treatment-level model ranking
- ✅ Diagnostic workflows
- ✅ Visualization tools
- ✅ Five package vignettes
- ✅ pkgdown website
- ✅ Clean R CMD check
Development Status
rumenGP is currently maintained and developed as an open-source framework for rumen gas production analysis. Future releases may include additional modeling, reporting, and statistical workflows.
Citation
If you use rumenGP in research, please cite:
Rodrigues, A. A. and Mantovani, H. C.
rumenGP:
An R package for rumen gas production
kinetic modeling, model comparison,
and visualization.
(Citation information will be updated as the package develops.)
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