FitMultipleGrowthMCMC {biogrowth}

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The class FitMultipleGrowthMCMC has been superseded by the top-level class GlobalGrowthFit, which provides a unified approach for growth modelling.

Still, it is still returned if the superseded fit_multiple_growth_MCMC() is called.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 class 'FitMultipleGrowthMCMC'
print(x, ...)

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

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

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

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

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

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

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

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

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

## S3 method for class 'FitMultipleGrowthMCMC'
predictMCMC(
  model,
  times,
  env_conditions,
  niter,
  newpars = NULL,
  formula = . ~ time
)

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: 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 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.

Value

An instance of MCMCgrowth().

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