calc_lagistic_fit_lag: 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
)

Value

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

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