# Time for the population to reach a given size Calculates the elapsed time required for the population to reach a given size (in log scale) ```r 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. ## Returns If `type="discrete"`, a number. If `type="distribution"`, an instance of TimeDistribution . ## 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 . ## Examples ```r ## 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) ```

time_to_size function