fit_inactivation_MCMC {bioinactivation}  R Documentation 
Fitting of dynamic inactivation with MCMC
Description
Fits the parameters of an inactivation model to experimental using
the Markov Chain Monte Carlo fitting algorithm implemented in
the function modMCMC
of the package FME
.
Usage
fit_inactivation_MCMC(experiment_data, simulation_model, temp_profile,
starting_points, upper_bounds, lower_bounds, known_params, ...,
minimize_log = TRUE, tol0 = 1e05)
Arguments
experiment_data 
data frame with the experimental data to be adjusted. It must have a column named “time” and another one named “N”. 
simulation_model 
character identifying the model to be used. 
temp_profile 
data frame with discrete values of the temperature for
each time. It must have one column named 
starting_points 
starting values of the parameters for the adjustment. 
upper_bounds 
named numerical vector defining the upper bounds of the parameters for the adjustment. 
lower_bounds 
named numerical vector defining the lower bounds of the parameters for the adjustment. 
known_params 
named numerical vector with the fixed (i.e., not adjustable) model parameters. 
... 
other arguments for 
minimize_log 
logical. If 
tol0 
numeric. Observations at time 0 make Weibullbased models singular. The time for observatins taken at time 0 are changed for this value. 
Value
A list of class FitInactivationMCMC
with the following items:

modMCMC
: a list of classmodMCMC
with the information of the adjustment process. best_prediction: a list of class
SimulInactivation
with the prediction generated by the best predictor.data: a data frame with the data used for the fitting.
Examples
## EXAMPLE 1 
data(dynamic_inactivation) # The example data set is used.
get_model_data() # Retrieve the valid model keys.
simulation_model < "Peleg" # Peleg's model will be used
model_data < get_model_data(simulation_model)
model_data$parameters # Set the model parameters
dummy_temp < data.frame(time = c(0, 1.25, 2.25, 4.6),
temperature = c(70, 105, 105, 70)) # Dummy temp. profile
## Set known parameters and initial points/bounds for unknown ones
known_params = c(temp_crit = 100)
starting_points < c(n = 1, k_b = 0.25, N0 = 1e+05)
upper_bounds < c(n = 2, k_b = 1, N0 = 1e6)
lower_bounds < c(n = 0.5, k_b = 0.1, N0 = 1e4)
MCMC_fit < fit_inactivation_MCMC(dynamic_inactivation, simulation_model,
dummy_temp, starting_points,
upper_bounds, lower_bounds,
known_params,
niter = 100)
# It is recommended to increase niter
plot(MCMC_fit)
goodness_of_fit(MCMC_fit)
## END EXAMPLE 1 