View source: R/milags_functions.R
calc_lagistic_fit_lag | R Documentation |
Calculates lag based on fitting logistic model to data
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
)
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 |
growth curve data with additional columns ('lag', and predicted biomass 'predicted'), and the fitting object if return.all.params was set to TRUE
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.