biogrowth (version 1.0.6)

fit_coupled_growth: Growth fitting considering link between mu and lambda for the Baranyi-Ratkowsky model

Description

This function implements the methodology suggested by Garre et al. (2025; doi: 10.1016/j.ijfoodmicro.2025.111078) for the Baranyi-Ratkowsky model. Rather than fitting independent models for mu and lambda, this approach considers a link between both secondary models, reducing the number of unknown parameters from 3 to 4.

The function implements too modes of fitting: two-steps and one-step. Please see the respective sections for further information.

Usage

fit_coupled_growth(
  fit_data,
  start,
  known = c(),
  mode = "two_steps",
  weight = "sd",
  ...,
  logbase_mu = exp(1),
  logbase_logN = 10
)

Arguments

fit_data

a tibble (or data.frame) with the data for the fit. The content must be different depending on the fitting mode (see relevant sections within the help page).

start

a numeric vector of initial guesses for the parameter estimates

known

a numeric vector of known mode parameters. An empty vector by default (no knonw parameter)

mode

the type of model fitting approach. Either two_steps (fitted from the values of mu and lambda) or one_step (fitted from logN)

weight

weights to apply for the two_steps fit. Either NULL (no weights), sd (standard deviation; default) or mean (mean value).

...

ignored

logbase_mu

Base for the definition of mu. By default, exp(1) (natural logarithm).

logbase_logN

Base for the definition of logN. By default, 10 (decimal logarithm).

Two-steps fitting

In this mode, it is assumed that primary models have been already fitted to each experiment. Therefore, the data is available as a table of values of mu and lambda estimated at each temperature. Hence, fit_data must be a tibble (or data.frame) with three columns: temp (storage temperature), mu (specific growth rate) and lambda (lag phase duration). By default, mu must be defined in the scale of natural logarithm, although this can be modified using the logbase_mu argument. The package includes the dataset example_coupled_twosteps as an illustration of the type of data.

One-step fitting

In this mode, secondary models are directly fitted to the observed (log) microbial counts. Hence, fit_data must be a tibble (or data.frame) with three columns: temp (storage temperature), time (the elapsed time) and logN (the log-microbial concentration). By default, logN must be defined in the scale of decimal logarithm, although this can be modified using the logbase_logN argument. The package includes the dataset example_coupled_onestep as an illustration of the type of data.

Examples

## Example 1: Two-steps fitting-------------------------------------------------

## We can use the example dataset 

data(example_coupled_twosteps)

## We need to define initial guesses for every parameter

guess <- c(logC0 = -1, b = .1, Tmin = 5)

## We can now call the fitting function

my_fit <- fit_coupled_growth(example_coupled_twosteps, 
                             start = guess,
                             mode = "two_steps")

## Common S3 methods are included

print(my_fit)
coef(my_fit)
summary(my_fit)
plot(my_fit)

## Any model parameter can be fixed using the known argument

known <- c(b = .01)

## Please note that the guess must be updated, as now parameter can appear both as a guess and known

guess <- c(logC0 = -1, Tmin =  0)


fixed_fit <- fit_coupled_growth(example_coupled_twosteps, 
                                start = guess,
                                known = known, 
                                mode = "two_steps")

print(fixed_fit)
coef(fixed_fit)
summary(fixed_fit)
plot(fixed_fit)

## Example 2: One-step fitting--------------------------------------------------

## We can use an example dataset with the right format

data("example_coupled_onestep")

## The function requires initial guesses for every model parameter

guess <- c(logN0 = 2, logNmax = 8, b = 0.04, logC0 = -4, Tmin = 5)

## We can now call the fitting function

my_fit <- fit_coupled_growth(example_coupled_onestep,
                             start = guess,
                             mode = "one_step")

## The package includes common S3 methods

print(my_fit)
coef(my_fit)
summary(my_fit)
plot(my_fit)

## Any model parameter can be fixed before fitting

known <- c(logNmax = 7)

## Guesses must be updated, so no parameter appears twice

guess <- c(logN0 = 2, b = 0.04, logC0 = -4, Tmin = 5)

## We can now call the fitting function

my_fit <- fit_coupled_growth(example_coupled_onestep,
                             start = guess,
                             known = known,
                             mode = "one_step")

## The package includes common S3 methods

print(my_fit)
coef(my_fit)
summary(my_fit)
plot(my_fit)