predict_MCMC_growth {biogrowth}  R Documentation 
Stochastic growth of MCMC fit
Description
The function predict_MCMC_growth()
has been superseded by predictMCMC()
S3 methods of the relevant classes.
Nonetheless, it can still make a prediction of microbial growth including
parameter uncertainty based on a growth model
fitted using fit_MCMC_growth()
or fit_multiple_growth_MCMC()
.
This function predicts growth curves for niter
samples (with replacement)
of the samples of the MCMC algorithm. Then, credible intervals are calculated based on the
quantiles of the model predictions at each time point.
Usage
predict_MCMC_growth(
MCMCfit,
times,
env_conditions,
niter,
newpars = NULL,
formula = . ~ time
)
Arguments
MCMCfit 
An instance of 
times 
Numeric vector of storage times for the predictions. 
env_conditions 
Tibble with the (dynamic) environmental conditions during the experiment. It must have one column named 'time' with the storage time and as many columns as required with the environmental conditions. 
niter 
Number of iterations. 
newpars 
A named list defining new values for the some model parameters.
The name must be the identifier of a model already included in the model.
These parameters do not include variation, so defining a new value for a fitted
parameters "fixes" it. 
formula 
A formula stating the column named defining the elapsed time in

Value
An instance of MCMCgrowth()
.
Examples
## We need a FitDynamicGrowthMCMC object
data("example_dynamic_growth")
data("example_env_conditions")
sec_model_names < c(temperature = "CPM", aw= "CPM")
known_pars < list(Nmax = 1e4, # Primary model
N0 = 1e0, Q0 = 1e3, # 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
)
my_start < list(temperature_xmin = 25, temperature_xopt = 35,
temperature_xmax = 40,
aw_xopt = .95)
set.seed(12124) # Setting seed for repeatability
my_MCMC_fit < fit_MCMC_growth(example_dynamic_growth, example_env_conditions,
my_start, known_pars, sec_model_names, niter = 3000)
## Define the conditions for the simulation
my_times < seq(0, 15, length = 50)
niter < 2000
newpars < list(N0 = 1e1, # A parameter that was fixed
temperature_xmax = 120 # A parameter that was fitted
)
## Make the simulations
my_MCMC_prediction < predict_MCMC_growth(my_MCMC_fit,
my_times,
example_env_conditions, # It could be different from the one used for fitting
niter,
newpars)
## We can plot the prediction interval
plot(my_MCMC_prediction)
## We can also get the quantiles at each time point
print(my_MCMC_prediction$quantiles)