fit_dynamic_inactivation {bioinactivation} | R Documentation |

Fits the parameters of an inactivation model to experimental data.
The function `modFit`

of the package `FME`

is
used for the adjustment.

```
fit_dynamic_inactivation(experiment_data, simulation_model, temp_profile,
starting_points, upper_bounds, lower_bounds, known_params, ...,
minimize_log = TRUE, tol0 = 1e-05)
```

`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. |

`...` |
further arguments passed to |

`minimize_log` |
logical. If |

`tol0` |
numeric. Observations at time 0 make Weibull-based models singular. The time for observatins taken at time 0 are changed for this value. |

A list of class `FitInactivation`

with the following items:

fit_results: a list of class

`modFit`

with the info of the adjustment.best_prediction: a list of class

`SimulInactivation`

with prediction made by the adjusted model.data: a data frame with the data used for the fitting.

```
## 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 = Inf)
lower_bounds <- c(n = 0, k_b = 0, N0 = 1e4)
dynamic_fit <- fit_dynamic_inactivation(dynamic_inactivation, simulation_model,
dummy_temp, starting_points,
upper_bounds, lower_bounds,
known_params)
plot(dynamic_fit)
goodness_of_fit(dynamic_fit)
## END EXAMPLE 1 -----
```

[Package *bioinactivation* version 1.2.3 Index]