Summary of Michaelis-Menten Fits

Summarizes parameter estimates and goodness-of-fit statistics for a fitted Michaelis-Menten model.

Usage

# S3 method for class 'mm_fit'
summary(object, ...)

Arguments

object

A mm_fit object.

...

Additional arguments passed to methods.

Value

A data frame containing parameter estimates and model diagnostics for each fitted bottle.

Details

The summary typically includes:

• Asymptotic gas production (A)

• Half-time parameter (K)

• Shape parameter (c)

• Residual Sum of Squares (RSS)

• Root Mean Squared Error (RMSE)

• R-squared (R²)

• Akaike Information Criterion (AIC)

• Bayesian Information Criterion (BIC)

The generalized Michaelis-Menten model is a flexible sigmoidal model commonly used to describe cumulative gas production.

The parameter K represents the time required to reach approximately half of the asymptotic gas production, while c controls curve shape and steepness.

Notes

The generalized Michaelis-Menten model is mathematically equivalent to the Groot model implemented in fit_groot().

Parameter correspondence:

• A = VF

• K = b

• c = k

Both formulations produce identical fitted values and model diagnostics when convergence is achieved.

See also

fit_mm, fit_groot, plot_fit, plot_residuals, 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
)

fit <- fit_mm(
  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

summary(
  fit
)
#> 
#> Michaelis-Menten model summary
#> ------------------------------
#> Total bottles: 24
#> Successful fits: 23
#> Failed fits: 1
#> Low R-squared (< 0.90): 2
#>