FitMultipleDynamicGrowth {biogrowth}

FitMultipleDynamicGrowth class

Description

The class FitMultipleDynamicGrowth has been superseded by the top-level class GlobalGrowthFit, which provides a unified approach for growth modelling.

Still, it is still returned if the superseded fit_multiple_growth() is called.

It is a subclass of list with the items:

• fit_results: the object returned by modFit.

• best_prediction: a list with the models predictions for each condition.

• data: a list with the data used for the fit.

• starting: starting values for model fitting

• known: parameter values set as known.

• sec_models: a named vector with the secondary model for each environmental factor.

Usage

## S3 method for class 'FitMultipleDynamicGrowth'
print(x, ...)

## S3 method for class 'FitMultipleDynamicGrowth'
plot(
  x,
  y = NULL,
  ...,
  add_factor = NULL,
  ylims = NULL,
  label_x = "time",
  label_y1 = "logN",
  label_y2 = add_factor,
  line_col = "black",
  line_size = 1,
  line_type = "solid",
  line_col2 = "black",
  line_size2 = 1,
  line_type2 = "dashed",
  point_size = 3,
  point_shape = 16,
  subplot_labels = "AUTO"
)

## S3 method for class 'FitMultipleDynamicGrowth'
summary(object, ...)

## S3 method for class 'FitMultipleDynamicGrowth'
residuals(object, ...)

## S3 method for class 'FitMultipleDynamicGrowth'
coef(object, ...)

## S3 method for class 'FitMultipleDynamicGrowth'
vcov(object, ...)

## S3 method for class 'FitMultipleDynamicGrowth'
deviance(object, ...)

## S3 method for class 'FitMultipleDynamicGrowth'
fitted(object, ...)

## S3 method for class 'FitMultipleDynamicGrowth'
predict(object, env_conditions, times = NULL, ...)

## S3 method for class 'FitMultipleDynamicGrowth'
logLik(object, ...)

## S3 method for class 'FitMultipleDynamicGrowth'
AIC(object, ..., k = 2)

Arguments

x

an instance of FitMultipleDynamicGrowth.

...

ignored

y

ignored

add_factor

whether to plot also one environmental factor. If NULL (default), no environmental factor is plotted. If set to one character string that matches one entry of x$env_conditions, that condition is plotted in the secondary axis

ylims

A two dimensional vector with the limits of the primary y-axis.

label_x

label of the x-axis

label_y1

Label of the primary y-axis.

label_y2

Label of the secondary y-axis.

line_col

Aesthetic parameter to change the colour of the line geom in the plot, see: geom_line()

line_size

Aesthetic parameter to change the thickness of the line geom in the plot, see: geom_line()

line_type

Aesthetic parameter to change the type of the line geom in the plot, takes numbers (1-6) or strings ("solid") see: geom_line()

line_col2

Same as lin_col, but for the environmental factor.

line_size2

Same as line_size, but for the environmental factor.

line_type2

Same as lin_type, but for the environmental factor.

point_size

Size of the data points

point_shape

shape of the data points

subplot_labels

labels of the subplots according to plot_grid.

object

an instance of FitMultipleDynamicGrowth

env_conditions

a tibble describing the environmental conditions (as in fit_multiple_growth().

times

A numeric vector with the time points for the simulations. NULL by default (using the same time points as the ones defined in env_conditions).

k

penalty for the parameters (k=2 by default)

Methods (by generic)

• print(FitMultipleDynamicGrowth): print of the model

• plot(FitMultipleDynamicGrowth): comparison between the fitted model and the experimental data.

• summary(FitMultipleDynamicGrowth): statistical summary of the fit.

• residuals(FitMultipleDynamicGrowth): calculates the model residuals. Returns a tibble with 4 columns: time (storage time), logN (observed count), exp (name of the experiment) and res (residual).

• coef(FitMultipleDynamicGrowth): vector of fitted parameters.

• vcov(FitMultipleDynamicGrowth): (unscaled) variance-covariance matrix, estimated as 1/(0.5*Hessian).

• deviance(FitMultipleDynamicGrowth): deviance of the model.

• fitted(FitMultipleDynamicGrowth): fitted values. They are returned as a tibble with 3 columns: time (storage time), exp (experiment identifier) and fitted (fitted value).

• predict(FitMultipleDynamicGrowth): vector of model predictions

• logLik(FitMultipleDynamicGrowth): loglikelihood of the model

• AIC(FitMultipleDynamicGrowth): Akaike Information Criterion