View source: R/fit_multi_dynamic.R
fit_multiple_growth  R Documentation 
The function fit_multiple_growth()
has been superseded by the toplevel
function fit_growth()
, which provides a unified approach for growth modelling.
But, if you so wish, this function still enables fitting 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,
logbase_mu = logbase_logN,
logbase_logN = 10
)
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 of the environmental conditions and values of 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.

logbase_mu 
Base of the logarithm the growth rate is referred to. By default, the same as logbase_logN. See vignette about units for details. 
logbase_logN 
Base of the logarithm for the population size. By default, 10 (i.e. log10). See vignette about units for details. 
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 = 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")
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