distribution_to_logcount: Distribution of times to reach a certain microbial count

Description Usage Arguments Value Examples

View source: R/functions_counts.R

Description

Returns the probability distribution of the storage time required for the microbial count to reach log_count according to the predictions of a stochastic model. Calculations are done using linear interpolation of the individual model predictions.

Usage

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distribution_to_logcount(model, log_count)

Arguments

model

An instance of StochasticGrowth or MCMCgrowth.

log_count

The target microbial count.

Value

An instance of TimeDistribution.

Examples

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## We need an instance of StochasticGrowth

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

library(tibble)

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

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

## We can now call the function

time_distrib <- distribution_to_logcount(stoc_growth, 4)

## And plot the results

plot(time_distrib)

biogrowth documentation built on July 6, 2021, 5:07 p.m.