predict_dynamic_growth {biogrowth}  R Documentation 
Growth under dynamic conditions
Description
The function predict_dynamic_growth()
has been superseded by the toplevel
function predict_growth()
, which provides a unified approach for growth modelling.
Regardless on that, it can still predict population growth under dynamic conditions based on the Baranyi model (Baranyi and Roberts, 1994) and secondary models based on the gamma concept (Zwietering et al. 1992).
Model predictions are done by linear interpolation of the environmental
conditions defined in env_conditions
.
Usage
predict_dynamic_growth(
times,
env_conditions,
primary_pars,
secondary_models,
...,
check = TRUE,
logbase_logN = 10,
logbase_mu = logbase_logN,
formula = . ~ time
)
Arguments
times 
Numeric vector of storage times to make the predictions 
env_conditions 
Tibble (or data.frame) describing the variation of the environmental
conditions during storage. It must have with the elapsed time (named 
primary_pars 
A named list defining the parameters of the primary model
and the initial values of the model variables. That is, with names 
secondary_models 
A nested list describing the secondary models. 
... 
Additional arguments for 
check 
Whether to check the validity of the models. 
logbase_logN 
Base of the logarithm for the population size. By default, 10 (i.e. log10). See vignette about units for details. 
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. 
formula 
An object of class "formula" describing the x variable.

Value
An instance of DynamicGrowth()
.
Examples
## Definition of the environmental conditions
library(tibble)
my_conditions < tibble(time = c(0, 5, 40),
temperature = c(20, 30, 35),
pH = c(7, 6.5, 5)
)
## Definition of the model parameters
my_primary < list(mu_opt = 2,
Nmax = 1e8,N0 = 1e0,
Q0 = 1e3)
sec_temperature < list(model = "Zwietering",
xmin = 25, xopt = 35, n = 1)
sec_pH = list(model = "CPM",
xmin = 5.5, xopt = 6.5,
xmax = 7.5, n = 2)
my_secondary < list(
temperature = sec_temperature,
pH = sec_pH
)
my_times < seq(0, 50, length = 1000)
## Do the simulation
dynamic_prediction < predict_dynamic_growth(my_times,
my_conditions, my_primary,
my_secondary)
## Plot the results
plot(dynamic_prediction)
## We can plot some environmental factor with add_factor
plot(dynamic_prediction, add_factor = "temperature", ylims= c(0, 8),
label_y1 = "Microbial count (log CFU/ml)",
label_y2 = "Storage temperature (C)")