FitMultipleGrowthMCMC: FitMultipleGrowthMCMC class

Description

The class FitMultipleGrowthMCMC has been superseded by the top-level class GlobalGrowthFit, which provides a unified approach for growth modelling.

Still, it is still returned if the superseded fit_multiple_growth_MCMC() is called.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 FitMultipleGrowthMCMC
print(x, ...)
# S3 method for FitMultipleGrowthMCMC
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 FitMultipleGrowthMCMC
summary(object, ...)
# S3 method for FitMultipleGrowthMCMC
residuals(object, ...)
# S3 method for FitMultipleGrowthMCMC
coef(object, ...)
# S3 method for FitMultipleGrowthMCMC
vcov(object, ...)
# S3 method for FitMultipleGrowthMCMC
deviance(object, ...)
# S3 method for FitMultipleGrowthMCMC
fitted(object, ...)
# S3 method for FitMultipleGrowthMCMC
predict(object, env_conditions, times = NULL, ...)
# S3 method for FitMultipleGrowthMCMC
logLik(object, ...)
# S3 method for FitMultipleGrowthMCMC
AIC(object, ..., k = 2)
# S3 method for FitMultipleGrowthMCMC
predictMCMC(
  model,
  times,
  env_conditions,
  niter,
  newpars = NULL,
  formula = . ~ time
)

Value

An instance of MCMCgrowth().

Arguments

x: an instance of FitMultipleGrowthMCMC.
...: 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 FitMultipleGrowthMCMC
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.
times: Numeric vector of storage times for the predictions.
k: penalty for the parameters (k=2 by default)
model: An instance of FitMultipleGrowthMCMC
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(FitMultipleGrowthMCMC): print of the model
plot(FitMultipleGrowthMCMC): comparison between the model fitted and the data.
summary(FitMultipleGrowthMCMC): statistical summary of the fit.
residuals(FitMultipleGrowthMCMC): model residuals. They are returned as a tibble with 4 columns: time (storage time), logN (observed count), exp (name of the experiment) and res (residual).
coef(FitMultipleGrowthMCMC): vector of fitted model parameters.
vcov(FitMultipleGrowthMCMC): variance-covariance matrix of the model, estimated as the variance of the samples from the Markov chain.
deviance(FitMultipleGrowthMCMC): deviance of the model, calculated as the sum of squared residuals of the prediction with the lowest standard error.
fitted(FitMultipleGrowthMCMC): fitted values of the model. They are returned as a tibble with 3 columns: time (storage time), exp (experiment identifier) and fitted (fitted value).
predict(FitMultipleGrowthMCMC): model predictions. They are returned as a tibble with 3 columns: time (storage time), logN (observed count), and exp (name of the experiment).
logLik(FitMultipleGrowthMCMC): loglikelihood of the model
AIC(FitMultipleGrowthMCMC): Akaike Information Criterion
predictMCMC(FitMultipleGrowthMCMC): prediction including parameter uncertainty