Description Usage Arguments Value Examples
View source: R/fit_multi_dynamic.R
This functions enables to fit a growth model using a dataset comprised of several experiments with potentially different dynamic experimental conditions. Note that the definition of secondary models must comply with the 'secondary_model_data' function.
1 2 3 4 5 6 7 8 9 
starting_point 
a named vector of starting values for the model parameters. 
experiment_data 
a nested list with the experimental data. Each entry describes
one experiment as a list with two elements: data and conditions. 
known_pars 
named vector of known model parameters 
sec_model_names 
named character vector with names the environmental conditions and values the secondary model (see secondary_model_data). 
... 
additional arguments for 
check 
Whether to check the validity of the models. 
formula 
an object of class "formula" describing the x and y variables.

An instance of FitMultipleDynamicGrowth
.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33  ## We will use the multiple_experiments data set
data("multiple_experiments")
## For each environmental factor, we need to defined a model
sec_names < c(temperature = "CPM", pH = "CPM")
## Any model parameter can be fixed
known < list(Nmax = 1e8, N0 = 1e0, Q0 = 1e3,
temperature_n = 2, temperature_xmin = 20, temperature_xmax = 35,
pH_n = 2, pH_xmin = 5.5, pH_xmax = 7.5, pH_xopt = 6.5)
## The rest require starting values for model fitting
start < list(mu_opt = .8, temperature_xopt = 30)
## We can now call the fitting function
global_fit < fit_multiple_growth(start, multiple_experiments, known, sec_names)
## Parameter estimates can be retrieved with summary
summary(global_fit)
## We can compare fitted model against observations
plot(global_fit)
## Any single environmental factor can be added to the plot using add_factor
plot(global_fit, add_factor = "temperature")

Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.