fit_secondary_growth {biogrowth} R Documentation

## Fit secondary growth models

### Description

Fits a secondary growth model to a set of growth rates obtained experimentally. Modelling is done according to the gamma concept proposed by Zwietering (1992) and cardinal parameter models.

### Usage

fit_secondary_growth(
fit_data,
starting_point,
known_pars,
sec_model_names,
transformation = "sq",
...,
check = TRUE,
formula = mu ~ .
)


### Arguments

 fit_data Tibble with the data used for the fit. It must have one column with the observed growth rate (named mu by default; can be changed using the "formula" argument) and as many columns as needed with the environmental factors. starting_point Named vector with initial values for the model parameters to estimate from the data. The growth rate under optimum conditions must be named mu_opt. The rest must be called 'env_factor'+'_'+'parameter'. For instance, the minimum pH for growth is 'pH_xmin'. known_pars Named vector of fixed model parameters. Must be named using the same convention as starting_point. sec_model_names Named character vector defining the secondary model for each environmental factor. transformation Character defining the transformation of mu for model fitting. One of sq (square root; default), log (log-transform) or none (no transformation). ... Additional arguments passed to modFit(). check Whether to do some basic checks (TRUE by default). formula an object of class "formula" describing the y variable. The right hand side must be ".". By default mu ~ ..

### Value

An instance of FitSecondaryGrowth().

### Examples

## We use the data included in the package

data("example_cardinal")

## Define the models to fit

sec_model_names <- c(temperature = "Zwietering", pH = "CPM")

## Any model parameter can be fixed

known_pars <- list(mu_opt = 1.2, temperature_n = 1,
pH_n = 2, pH_xmax = 6.8, pH_xmin = 5.2)

## Initial values must be given for every other parameter

my_start <- list(temperature_xmin = 5, temperature_xopt = 35,
pH_xopt = 6.5)

## We can now call the fitting function

fit_cardinal <- fit_secondary_growth(example_cardinal, my_start, known_pars, sec_model_names)

## With summary, we can look at the parameter estimates

summary(fit_cardinal)

## The plot function compares predictions against observations

plot(fit_cardinal)

## Passing which = 2, generates a different kind of plot

plot(fit_cardinal, which = 2)
plot(fit_cardinal, which = 2, add_trend = TRUE)
plot(fit_cardinal, which = 2, add_segment = TRUE)



[Package biogrowth version 1.0.1 Index]