biogrowth: predict_stochastic_growth – R documentation

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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.

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.

Value

An instance of StochasticGrowth.

Examples

## 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)

biogrowth

Modelling of Population Growth

v0.2.0

GPL-3

Authors

Alberto Garre [aut, cre] (<https://orcid.org/0000-0002-4404-3550>), Jeroen Koomen [aut], Heidy den Besten [aut], Marcel Zwietering [aut]

Initial release