Fit dynamic microbial inactivation models — fit_dynamic

fit_dynamic_inactivation() estimates selected parameters of a dynamic Weibull-Peleg inactivation model by minimizing the residual sum of squares against observed data.

Usage

fit_dynamic_inactivation(
  data,
  profile,
  time,
  response,
  start,
  estimate = NULL,
  fixed = NULL,
  lower = NULL,
  upper = NULL,
  model = "weibull_peleg",
  secondary = "constant",
  dt = 0.01,
  method = "rk4",
  temperature = "temperature",
  optimizer = "L-BFGS-B",
  control = list(),
  ...
)

Arguments

data: Data frame with observed microbial counts.
profile: A dynamic_profile() object or compatible data frame.
time, response: Column names in data containing observation time and microbial response.
start: Named list of starting parameter values passed to predict_dynamic_growth(). Numeric scalar entries are candidates for estimation.
estimate: Character vector of parameter names to estimate. If NULL, all numeric scalar entries in start except those listed in fixed are estimated.
fixed: Optional named list of parameters to keep fixed during fitting.
lower, upper: Optional named numeric vectors or lists with lower and upper bounds for estimated parameters.
model, secondary, dt, method, temperature: Arguments passed to predict_dynamic_inactivation().
optimizer: Optimization method passed to stats::optim().
control: Optional control list passed to stats::optim().
...: Additional arguments passed to predict_dynamic_growth().

Value

A predmicror_dynamic_fit object.

Examples

profile <- dynamic_profile(time = c(0, 10), temperature = c(60, 60))
obs <- data.frame(time = c(0, 5, 10), logN = c(7, 6, 5))
fit <- fit_dynamic_inactivation(
  obs,
  profile = profile,
  time = "time",
  response = "logN",
  start = list(logN0 = 7, b = 0.15, n = 1),
  estimate = "b",
  dt = 0.25
)
coef(fit)
#> logN0     b     n 
#>   7.0   0.2   1.0