biogrowth (version 0.2.0)

predict_stochastic_growth: Isothermal growth with variability

Description

Stochastic simulation of microbial growth based on probability distributions of the parameters of the primary model. It is included by Monte Carlo simulation considering the parameters follow a multivariate normal distribution.

Usage

predict_stochastic_growth(
  model_name,
  times,
  n_sims,
  pars,
  corr_matrix = diag(nrow(pars)),
  check = TRUE
)

Arguments

model_name

Character describing the primary growth model.

times

Numeric vector of storage times for the simulations.

n_sims

Number of simulations.

pars

A tibble describing the parameter uncertainty (see details).

corr_matrix

Correlation matrix of the model parameters. Defined in the same order as in pars. An identity matrix by default (uncorrelated parameters).

check

Whether to do some tests. FALSE by default.

Value

An instance of StochasticGrowth.

Details

They are defined in the pars argument using a tibble with 4 columns:

• par: identifier of the model parameter (according to primary_model_data),

• mean: mean value of the model parameter.,

• sd: standard deviation of the model parameter.,

• scale: scale at which the model parameter is defined. Valid values are 'original' (no transformation), 'sqrt' square root or 'log' log-scale. The parameter sample is generated considering the parameter follows a marginal normal distribution at this scale, and is later converted to the original scale for calculations.

Examples

# NOT RUN {
## Definition of the simulation settings

my_model <- "Baranyi"
my_times <- seq(0, 30, length = 100)
n_sims <- 3000

pars <- tribble(
    ~par, ~mean, ~sd, ~scale,
    "logN0", 0, .2, "original",
    "mu", 2, .3, "sqrt",
    "lambda", 4, .4, "sqrt",
    "logNmax", 6, .5, "original"
)

## Calling the function

stoc_growth <- predict_stochastic_growth(my_model, my_times, n_sims, pars)

## We can plot the results

plot(stoc_growth)

## Adding parameter correlation

my_cor <- matrix(c(1,   0,   0, 0,
    0,   1, 0.7, 0,
    0, 0.7,   1, 0,
    0,   0,   0, 1),
    nrow = 4)

stoc_growth2 <- predict_stochastic_growth(my_model, my_times, n_sims, pars, my_cor)

plot(stoc_growth2)
# }
# NOT RUN {
# }