View source: R/model_comparison.R

compare_growth_fits | R Documentation |

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.

```
compare_growth_fits(models)
```

`models` |
a (we recommend named) list of models fitted using |

```
## 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)
```

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