time_to_size {biogrowth} | R Documentation |

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

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

`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. |

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.

If `type="discrete"`

, a number. If `type="distribution"`

, an instance of
TimeDistribution.

```
## 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)
```

[Package *biogrowth* version 1.0.1 Index]