View source: R/fit_multi_dynamic.R
fit_multiple_growth | R Documentation |
The function fit_multiple_growth()
has been superseded by the top-level
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 = 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")
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