Fitting growth models to multiple dynamic experiments
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.
fit_multiple_growth( starting_point, experiment_data, known_pars, sec_model_names, ..., check = TRUE, formula = logN ~ time )
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.
## 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 = 1e-3,
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")Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.