Fit dynamic growth models
Fits a growth model to a data obtained under dynamic conditions using the one-step approach (non-linear regression).
fit_dynamic_growth( fit_data, env_conditions, starting_point, known_pars, sec_model_names, ..., check = TRUE, formula = logN ~ time )
fit_data |
Tibble with the data to use for model fit. It must contain a column with the elapsed time (named "time" by default) and another one with the decimal logarithm of the observed population size (named "logN" by default). Different column names can be specified using the "formula" argument. |
env_conditions |
Tibble with the (dynamic) environmental conditions during the experiment. It must have one column with the elapsed time (named "time" by default) and as many columns as required with the environmental conditions. A different column name can be specified using the "formula" argument, although it must be the same one as in "fit_data". Note that only those defined in "sec_model_names" will be considered for the model fit. |
starting_point |
A named vector of starting values for the model parameters.
Parameters for the primary model must be named in the usual way. Parameters for the
secondary model are named as |
known_pars |
A named vectors of known model parameters (i.e. not fitted). They
must be named using the same convention as for |
sec_model_names |
A named character vector defining the secondary model for each
environmental factor. The names define the factor and the value the type of model.
Names must match columns in |
... |
Additional arguments passed to modFit. |
check |
Whether to check model parameters (TRUE by default). |
formula |
an object of class "formula" describing the x and y variables.
|
An instance of FitDynamicGrowth.
## We use the datasets included in the package
data("example_dynamic_growth")
data("example_env_conditions")
## Define the secondary models
sec_model_names <- c(temperature = "CPM", aw= "CPM")
## Any model parameter can be fixed
known_pars <- list(Nmax = 1e4, # Primary model
N0 = 1e0, Q0 = 1e-3, # Initial values of the primary model
mu_opt = 4, # mu_opt of the gamma model
temperature_n = 1, # Secondary model for temperature
aw_xmax = 1, aw_xmin = .9, aw_n = 1 # Secondary model for water activity
)
## The remaining parameters need initial values
my_start <- list(temperature_xmin = 25, temperature_xopt = 35,
temperature_xmax = 40, aw_xopt = .95)
## We can now call the fitting function
my_dyna_fit <- fit_dynamic_growth(example_dynamic_growth, example_env_conditions,
my_start, known_pars, sec_model_names)
summary(my_dyna_fit)
## We can compare the data and the fitted curve
plot(my_dyna_fit)
## We can plot any environmental condition using add_factor
plot(my_dyna_fit, add_factor = "aw",
label_y1 = "Log count (log CFU/ml)",
label_y2 = "Water activity")Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.