choose_lag_fit_algorithm_baranyi: choose_lag_fit_algorithm_baranyi

In miLAG: Calculates Microbial Lag Duration (on the Population Level) from Provided Growth Curve Data

choose_lag_fit_algorithm_baranyi

Description

Runs nlsLM/nls algorithms with three different parameter setups to fit the best Baranyi parameters to our data and chooses the best model

Usage

choose_lag_fit_algorithm_baranyi(
  gr_curve,
  LOG10N0,
  init_lag,
  init_mumax,
  init_LOG10Nmax,
  max_iter,
  lower_bound
)

Arguments

gr_curve	data from one specific growth curve with the following columns: LOG10N, t
LOG10N0	init value for the LOG10N0 parameter
init_lag	initial value for the lag
init_mumax	initial value for the mumax parameter
init_LOG10Nmax	initial value for the LOG10Nmax parameter
max_iter	max. number of iterations
lower_bound	lower bound for the bounded nls optimization;

Value

the best nls fitting object with parameters fitted to Baranyi model (lowest Res.Sum Sq provided that all coefficients are nonnegative)

miLAG documentation built on April 3, 2025, 8:09 p.m.