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The class FitMultipleDynamicGrowth has been superseded by the top-level class GlobalGrowthFit, which provides a unified approach for growth modelling.

Still, it is still returned if the superseded fit_multiple_growth() is called.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 FitMultipleDynamicGrowth
print(x, ...)
# S3 method for 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 FitMultipleDynamicGrowth
summary(object, ...)
# S3 method for FitMultipleDynamicGrowth
residuals(object, ...)
# S3 method for FitMultipleDynamicGrowth
coef(object, ...)
# S3 method for FitMultipleDynamicGrowth
vcov(object, ...)
# S3 method for FitMultipleDynamicGrowth
deviance(object, ...)
# S3 method for FitMultipleDynamicGrowth
fitted(object, ...)
# S3 method for FitMultipleDynamicGrowth
predict(object, env_conditions, times = NULL, ...)
# S3 method for FitMultipleDynamicGrowth
logLik(object, ...)
# S3 method for 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: 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()
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