fit_multiple_growth: Fitting growth models to multiple dynamic experiments
In biogrowth: Modelling of Population Growth

Description Usage Arguments Value Examples

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. `data` is a tibble with a column giving the elapsed time (named "time" by default) and another one with the decimal logarithm of the population size (named "logN" by default). `conditions` is a tibble with one column giving the elapsed time (using the same name as `data`) and as many additional columns as environmental factors. The default column names can be changed with the formula argument.
`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 `modFit`.
`check`	Whether to check the validity of the models. `TRUE` by default.
`formula`	an object of class "formula" describing the x and y variables. `logN ~ time` as a default.

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")