View source: R/milags_functions.R
choose_lag_fit_algorithm_baranyi | R Documentation |
Runs nlsLM/nls algorithms with three different parameter setups to fit the best Baranyi parameters to our data and chooses the best model
choose_lag_fit_algorithm_baranyi(
gr_curve,
LOG10N0,
init_lag,
init_mumax,
init_LOG10Nmax,
max_iter,
lower_bound
)
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; |
the best nls fitting object with parameters fitted to Baranyi model (lowest Res.Sum Sq provided that all coefficients are nonnegative)
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