FitDynamicGrowthMCMC: FitDynamicGrowthMCMC class

Description

The class FitDynamicGrowthMCMC has been superseded by the top-level class GrowthFit, which provides a unified approach for growth modelling.

Still, it is returned if the superseded fit_MCMC_growth() is called.

It is a subclass of list with the items:

fit_results: the object returned by modMCMC.
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 FitDynamicGrowthMCMC
print(x, ...)
# S3 method for FitDynamicGrowthMCMC
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 FitDynamicGrowthMCMC
summary(object, ...)
# S3 method for FitDynamicGrowthMCMC
residuals(object, ...)
# S3 method for FitDynamicGrowthMCMC
coef(object, ...)
# S3 method for FitDynamicGrowthMCMC
vcov(object, ...)
# S3 method for FitDynamicGrowthMCMC
deviance(object, ...)
# S3 method for FitDynamicGrowthMCMC
fitted(object, ...)
# S3 method for FitDynamicGrowthMCMC
predict(object, times = NULL, newdata = NULL, ...)
# S3 method for FitDynamicGrowthMCMC
logLik(object, ...)
# S3 method for FitDynamicGrowthMCMC
AIC(object, ..., k = 2)
# S3 method for FitDynamicGrowthMCMC
predictMCMC(
  model,
  times,
  env_conditions,
  niter,
  newpars = NULL,
  formula = . ~ time
)

Value

An instance of MCMCgrowth().

Arguments

x: The object of class FitDynamicGrowthMCMC to plot.
...: ignored
y: ignored
add_factor: whether to plot also one environmental factor. If NULL (default), no environmenta 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::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 FitDynamicGrowthMCMC
times: Numeric vector of storage times for the predictions.
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)
model: An instance of FitDynamicGrowthMCMC
env_conditions: Tibble with the (dynamic) environmental conditions during the experiment. It must have one column named 'time' with the storage time and as many columns as required with the environmental conditions.
niter: Number of iterations.
newpars: A named list defining new values for the some model parameters. The name must be the identifier of a model already included in the model. These parameters do not include variation, so defining a new value for a fitted parameters "fixes" it. NULL by default (no new parameters).
formula: A formula stating the column named defining the elapsed time in env_conditions. By default, . ~ time.

Methods (by generic)

print(FitDynamicGrowthMCMC): print of the model
plot(FitDynamicGrowthMCMC): compares the model fitted against the data.
summary(FitDynamicGrowthMCMC): statistical summary of the fit.
residuals(FitDynamicGrowthMCMC): model residuals.
coef(FitDynamicGrowthMCMC): vector of fitted model parameters.
vcov(FitDynamicGrowthMCMC): variance-covariance matrix of the model, estimated as the variance of the samples from the Markov chain.
deviance(FitDynamicGrowthMCMC): deviance of the model, calculated as the sum of squared residuals for the parameter values resulting in the best fit.
fitted(FitDynamicGrowthMCMC): vector of fitted values.
predict(FitDynamicGrowthMCMC): vector of model predictions.
logLik(FitDynamicGrowthMCMC): loglikelihood of the model
AIC(FitDynamicGrowthMCMC): Akaike Information Criterion
predictMCMC(FitDynamicGrowthMCMC): prediction including parameter uncertainty