FitDynamicGrowth: FitDynamicGrowth class
In biogrowth: Modelling of Population Growth

FitDynamicGrowth

R Documentation

FitDynamicGrowth class

Description

The class FitDynamicGrowth has been superseded by the top-level class GrowthFit, which provides a unified approach for growth modelling.

Still, it is still returned if the superseded fit_dynamic_growth() is called.

It is a subclass of list with the items:

fit_results: the object returned by modFit.
best_prediction: the model prediction for the fitted parameters.
env_conditions: environmental conditions for the fit.
data: 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 'FitDynamicGrowth'
print(x, ...)

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

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

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

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

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

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

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

## S3 method for class 'FitDynamicGrowth'
predict(object, times = NULL, newdata = NULL, ...)

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

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

Arguments

`x`	The object of class `FitDynamicGrowth` to plot.
`...`	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_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: `ggplot2::geom_line()`
`line_size`	Aesthetic parameter to change the thickness of the line geom in the plot, see: `ggplot2::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: `ggplot2::geom_line()`
`point_col`	Aesthetic parameter to change the colour of the point geom, see: `ggplot2::geom_point()`
`point_size`	Aesthetic parameter to change the size of the point geom, see: `ggplot2::geom_point()`
`point_shape`	Aesthetic parameter to change the shape of the point geom, see: ggplot2::`ggplot2::geom_point()`
`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.
`object`	an instance of FitDynamicGrowth
`times`	A numeric vector with the time points for the simulations. `NULL` by default (using the same time points as those for the simulation).
`newdata`	a tibble describing the environmental conditions (as `env_conditions`) in `predict_dynamic_growth()`. If `NULL` (default), uses the same conditions as those for fitting.
`k`	penalty for the parameters (k=2 by default)

Methods (by generic)

print(FitDynamicGrowth): comparison between the fitted model and the data.
plot(FitDynamicGrowth): comparison between the fitted model and the data.
summary(FitDynamicGrowth): statistical summary of the fit.
residuals(FitDynamicGrowth): residuals of the model.
coef(FitDynamicGrowth): vector of fitted parameters.
vcov(FitDynamicGrowth): (unscaled) variance-covariance matrix of the model, calculated as 1/(0.5*Hessian)
deviance(FitDynamicGrowth): deviance of the model.
fitted(FitDynamicGrowth): fitted values.
predict(FitDynamicGrowth): model predictions.
logLik(FitDynamicGrowth): loglikelihood of the model
AIC(FitDynamicGrowth): Akaike Information Criterion