check_growth_guess {biogrowth}

Visual check of an initial guess of the model parameters

Description

Generates a plot comparing a set of data points against the model prediction corresponding to an initial guess of the model parameters

Usage

check_growth_guess(
  fit_data,
  model_keys,
  guess,
  environment = "constant",
  env_conditions = NULL,
  approach = "single",
  logbase_mu = 10,
  formula = logN ~ time
)

Arguments

fit_data

Tibble (or data.frame) of data for the fit. It must have two columns, one with the elapsed time (time by default) and another one with the decimal logarithm of the populatoin size (logN by default). Different column names can be defined using the formula argument.

model_keys

Named the equations of the secondary model as in fit_growth()

guess

Named vector with the initial guess of the model parameters as in fit_growth()

environment

type of environment. Either "constant" (default) or "dynamic" (see below for details on the calculations for each condition)

env_conditions

Tibble describing the variation of the environmental conditions for dynamic experiments. See fit_growth(). Ignored when environment = "constant"

approach

whether "single" (default) or "global". Please see fit_growth() for details.“

logbase_mu

Base of the logarithm the growth rate is referred to. By default, 10 (i.e. log10). See vignette about units for details.

formula

an object of class "formula" describing the x and y variables. logN ~ time as a default.

Value

A ggplot() comparing the model prediction against the data

Examples

## Examples under constant environmental conditions -------------------------

## We need some data

my_data <- data.frame(time = 0:9,
                      logN = c(2, 2.1, 1.8, 2.5, 3.1, 3.4, 4, 4.5, 4.8, 4.7)
                      )
                      
## We can directly plot the comparison for some values

check_growth_guess(my_data, list(primary = "modGompertz"),
                   c(logN0 = 1.5, mu = .8, lambda = 4, C = 3)
                   )
                   
## Ot it can be combined with the automatic initial guess

check_growth_guess(my_data, list(primary = "modGompertz"),
                   make_guess_primary(my_data, "modGompertz")
                   )
                   
## Examples under dynamic environmental conditions --------------------------

## We will use the datasets included in the package

data("example_dynamic_growth")
data("example_env_conditions")

## Model equations are assigned as in fit_growth

sec_models <- list(temperature = "CPM", aw = "CPM")

## Guesses of model parameters are also defined as in fit_growth

guess <- list(Nmax = 1e4, 
              N0 = 1e0, Q0 = 1e-3,
              mu_opt = 4, 
              temperature_n = 1,
              aw_xmax = 1, aw_xmin = .9, aw_n = 1,
              temperature_xmin = 25, temperature_xopt = 35,
              temperature_xmax = 40, aw_xopt = .95
              )
              
## We can now check our initial guess

check_growth_guess(example_dynamic_growth, sec_models, guess,
                   "dynamic",
                   example_env_conditions)