compare_growth_fits: Model comparison and selection for growth models

In biogrowth: Modelling of Population Growth

Model comparison and selection for growth models

Description

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.

Usage

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

## 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)

biogrowth documentation built on May 29, 2024, 4:17 a.m.