# Model comparison and selection for growth models This function is a constructor for GrowthComparison or GlobalGrowthComparison , a class that provides several functions for model comparison and model selection for growth models fitted using `fit_growth()`. Please see the help pages for GrowthComparison or GlobalGrowthComparison for further details. Although it is not necessary, we recommend passing the models as a named list, as these names will later be kept in plots and tables. ```r compare_growth_fits(models) ``` ## Arguments - `models`: a (we recommend named) list of models fitted using `fit_growth()`. Every model should be of the same class. Otherwise, some functions may give unexpected results. ## Examples ```r ## Example 1 - Fitting under static environmental conditions ---------------- ## We will use the data on growth of Salmonella included in the package data("growth_salmonella") ## We will fit 3 different models to the data fit1 <- fit_growth(growth_salmonella, list(primary = "Baranyi"), start = c(lambda = 0, logNmax = 8, mu = .1, logN0 = 2), known = c(), environment = "constant", ) fit2 <- fit_growth(growth_salmonella, list(primary = "Baranyi"), start = c(logNmax = 8, mu = .1, logN0 = 2), known = c(lambda = 0), environment = "constant", ) fit3 <- fit_growth(growth_salmonella, list(primary = "modGompertz"), start = c(C = 8, mu = .1, logN0 = 2), known = c(lambda = 0), environment = "constant", ) ## We can now put them in a (preferably named) list my_models <- list(`Baranyi` = fit1, `Baranyi no lag` = fit2, `Gompertz no lag` = fit3) ## And pass them to compare_growth_fits model_comparison <- compare_growth_fits(my_models) ## The instance of GrowthComparison has useful S3 methods print(model_comparison) plot(model_comparison) plot(model_comparison, type = 2) plot(model_comparison, type = 3) ## The statistical indexes can be accessed through summary and coef summary(model_comparison) coef(model_comparison) ## Example 2 - Fitting under dynamic environmental conditions --------------- ## We will use one of the example datasets data("example_dynamic_growth") data("example_env_conditions") ## First model fitted sec_models <- list(temperature = "CPM", aw = "CPM") known_pars <- list(Nmax = 1e4, N0 = 1e0, Q0 = 1e-3, mu_opt = 4, temperature_n = 1, aw_xmax = 1, aw_xmin = .9, aw_n = 1 ) my_start <- list(temperature_xmin = 25, temperature_xopt = 35, temperature_xmax = 40, aw_xopt = .95) dynamic_fit <- fit_growth(example_dynamic_growth, sec_models, my_start, known_pars, environment = "dynamic", env_conditions = example_env_conditions ) ## Second model (different secondary model for temperature) sec_models <- list(temperature = "Zwietering", aw = "CPM") known_pars <- list(Nmax = 1e4, N0 = 1e0, Q0 = 1e-3, mu_opt = 4, temperature_n = 1, aw_xmax = 1, aw_xmin = .9, aw_n = 1 ) my_start <- list(temperature_xmin = 25, temperature_xopt = 35, aw_xopt = .95) dynamic_fit2 <- fit_growth(example_dynamic_growth, sec_models, my_start, known_pars, environment = "dynamic", env_conditions = example_env_conditions ) ## Once both models have been fitted, we can call the function dynamic_comparison <- compare_growth_fits(list(m1 = dynamic_fit, m2 = dynamic_fit2)) ## Which also returns an instance of GrowthComparison with the same S3 methods print(dynamic_comparison) plot(dynamic_comparison) plot(dynamic_comparison, type = 2) plot(dynamic_comparison, type = 3) ## The statistical indexes can be accessed through summary and coef summary(dynamic_comparison) coef(dynamic_comparison) ## Example 3 - Global fitting ----------------------------------------------- ## We use the example data data("multiple_counts") data("multiple_conditions") ## We need to fit (at least) two models 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, pH_n = 2, pH_xmin = 5.5, pH_xmax = 7.5, pH_xopt = 6.5) my_start <- list(mu_opt = .8, temperature_xopt = 30) global_fit <- fit_growth(multiple_counts, sec_models, my_start, known_pars, environment = "dynamic", algorithm = "regression", approach = "global", env_conditions = multiple_conditions ) sec_models <- list(temperature = "CPM", pH = "CPM") known_pars <- list(Nmax = 1e8, N0 = 1e0, Q0 = 1e-3, temperature_n = 1, temperature_xmin = 20, temperature_xmax = 35, pH_n = 2, pH_xmin = 5.5, pH_xmax = 7.5, pH_xopt = 6.5) my_start <- list(mu_opt = .8, temperature_xopt = 30) global_fit2 <- fit_growth(multiple_counts, sec_models, my_start, known_pars, environment = "dynamic", algorithm = "regression", approach = "global", env_conditions = multiple_conditions ) ## We can now pass both models to the function as a (named) list global_comparison <- compare_growth_fits(list(`n=2` = global_fit, `n=1` = global_fit2) ) ## The residuals and model fits plots are divided by experiments plot(global_comparison) plot(global_comparison, type = 3) ## The remaining S3 methods are the same as before print(global_comparison) plot(global_comparison, type = 2) summary(global_comparison) coef(global_comparison) ```

compare_growth_fits function