time_to_size {biogrowth}

Time for the population to reach a given size

Description

Calculates the elapsed time required for the population to reach a given size (in log scale)

Usage

time_to_size(model, size, type = "discrete", logbase_logN = NULL)

Arguments

model

An instance of GrowthPrediction, GrowthFit, GlobalGrowthFit, GrowthUncertainty or MCMCgrowth.

size

Target population size (in log scale)

type

Tye of calculation, either "discrete" (default) or "distribution"

logbase_logN

Base of the logarithm for the population size. By default, 10 (i.e. log10). See vignette about units for details.

Details

The calculation method differs depending on the value of type. If type="discrete" (default), the function calculates by linear interpolation a discrete time to reach the target population size. If type="distribution", this calculation is repeated several times, generating a distribution of the time. Note that this is only possible for instances of GrowthUncertainty or MCMCgrowth.

Value

If type="discrete", a number. If type="distribution", an instance of TimeDistribution.

Examples

## Example 1 - Growth predictions -------------------------------------------

## The model is defined as usual with predict_growth

my_model <- list(model = "modGompertz", logN0 = 0, C = 6, mu = .2, lambda = 20)

my_time <- seq(0, 100, length = 1000)  # Vector of time points for the calculations

my_prediction <- predict_growth(my_time, my_model, environment = "constant")

plot(my_prediction)

## We just have to pass the model and the size (in log10)

time_to_size(my_prediction, 3)

## If the size is not reached, it returns NA

time_to_size(my_prediction, 8)

## By default, it considers the population size is defined in the same log-base
## as the prediction. But that can be changed using logbase_logN

time_to_size(my_prediction, 3)
time_to_size(my_prediction, 3, logbase_logN = 10)
time_to_size(my_prediction, log(100), logbase_logN = exp(1))

## Example 2 - Model fit ----------------------------------------------------

my_data <- data.frame(time = c(0, 25, 50, 75, 100), 
                      logN = c(2, 2.5, 7, 8, 8))
                      
models <- list(primary = "Baranyi")

known <- c(mu = .2)

start <- c(logNmax = 8, lambda = 25, logN0 = 2)

primary_fit <- fit_growth(my_data, models, start, known,
                          environment = "constant",
                          )
                          
plot(primary_fit)

time_to_size(primary_fit, 4)

## Example 3 - Global fitting -----------------------------------------------

## We need a model first

data("multiple_counts")
data("multiple_conditions")

sec_models <- list(temperature = "CPM", pH = "CPM")

known_pars <- list(Nmax = 1e8, N0 = 1e0, Q0 = 1e-3,
                   temperature_n = 2, temperature_xmin = 20, 
                   temperature_xmax = 35,
                   temperature_xopt = 30,
                   pH_n = 2, pH_xmin = 5.5, pH_xmax = 7.5, pH_xopt = 6.5)
                   
my_start <- list(mu_opt = .8)

global_fit <- fit_growth(multiple_counts, 
                         sec_models, 
                         my_start, 
                         known_pars,
                         environment = "dynamic",
                         algorithm = "regression",
                         approach = "global",
                         env_conditions = multiple_conditions
                         ) 
                         
plot(global_fit)

## The function calculates the time for each experiment

time_to_size(global_fit, 3)

## It returns NA for the particular experiment if the size is not reached

time_to_size(global_fit, 4.5)