calc_lagistic_fit_lag: calc_lagistic_fit_lag

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

calc_lagistic_fit_lag

Description

Calculates lag based on fitting logistic model to data

Usage

calc_lagistic_fit_lag(
  data,
  n0,
  init_gr_rate = NULL,
  init_K = NULL,
  init_lag = NULL,
  algorithm,
  max_iter,
  return_all_params = FALSE,
  min_b = 0.2,
  min_a = 0.8
)

Arguments

data	a data frame with two required columns names: "time" and "biomass",and one optional column: "curve_id" This is data from may come from multiple growth curves
n0	a data frame describing initial biomass for each of the curves, i.e. it has two obligatory columns: "curve_id", "N0"
init_gr_rate	initial value for the growth rate, defaults to NULL in which case it will be approximated based on the data
init_K	initial value for the saturation parameter K, defaults to NULL in which case it will be approximated based on the data
init_lag	initial value for the lag parameter, defaults to NULL in which case it will be approximated based on the data
algorithm	eg. "auto", "Levenberg-Marquardt", "port"
max_iter	Maximum number of iterations
return_all_params	defaults to FALSE, TRUE if you also want to get K and growth.rate apart from lag
min_b	defaults to 0.2; mina and minb define where to look for exponential phase: it will be where the biomass is between min + (max-min)*(lower.bound.for.gr TO upper.bound.for.gr)
min_a	defaults to 0.8

Value

growth curve data with additional columns ('lag', and predicted biomass 'predicted'), and the fitting object if return.all.params was set to TRUE

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