An R6 Class to represent a PLNfit in a standard, general framework, with diagonal residual covariance

The function PLNLDA() produces an instance of an object with class PLNLDAfit.

This class comes with a set of methods, some of them being useful for the user: See the documentation for the methods inherited by PLNfit(), the plot() method for LDA visualization and predict() method for prediction

Super class

PLNfit -> PLNfit_diagonal

Active bindings

nb_param: number of parameters in the current PLN model
vcov_model: character: the model used for the residual covariance

Methods

Inherited methods

`PLNfit_diagonal$new()`

Initialize a PLNfit model

Usage

PLNfit_diagonal$new(responses, covariates, offsets, weights, formula, control)

Arguments

responses: the matrix of responses (called Y in the model). Will usually be extracted from the corresponding field in PLNfamily-class
covariates: design matrix (called X in the model). Will usually be extracted from the corresponding field in PLNfamily-class
offsets: offset matrix (called O in the model). Will usually be extracted from the corresponding field in PLNfamily-class
weights: an optional vector of observation weights to be used in the fitting process.
formula: model formula used for fitting, extracted from the formula in the upper-level call
control: a list for controlling the optimization. See details.

`PLNfit_diagonal$clone()`

The objects of this class are cloneable with this method.

Usage

PLNfit_diagonal$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Super classes

PLNfit -> PLNLDAfit -> PLNLDAfit_spherical

Active bindings

vcov_model: character: the model used for the residual covariance
nb_param: number of parameters in the current PLN model

Methods

Public methods

PLNLDAfit_spherical$new()
PLNLDAfit_spherical$clone()

Inherited methods

`PLNLDAfit_spherical$new()`

Initialize a PLNfit model

Usage

PLNLDAfit_spherical$new(
  grouping,
  responses,
  covariates,
  offsets,
  weights,
  formula,
  control
)

Arguments

grouping: a factor specifying the class of each observation used for discriminant analysis.
responses: the matrix of responses (called Y in the model). Will usually be extracted from the corresponding field in PLNfamily-class
covariates: design matrix (called X in the model). Will usually be extracted from the corresponding field in PLNfamily-class
offsets: offset matrix (called O in the model). Will usually be extracted from the corresponding field in PLNfamily-class
weights: an optional vector of observation weights to be used in the fitting process.
formula: model formula used for fitting, extracted from the formula in the upper-level call
control: a list for controlling the optimization. See details.

`PLNLDAfit_spherical$clone()`

The objects of this class are cloneable with this method.

Usage

PLNLDAfit_spherical$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Examples

if (FALSE) { # \dontrun{
data(trichoptera)
trichoptera <- prepare_data(trichoptera$Abundance, trichoptera$Covariate)
myPLN <- PLN(Abundance ~ 1, data = trichoptera)
class(myPLN)
print(myPLN)
} # }
if (FALSE) { # \dontrun{
data(trichoptera)
trichoptera <- prepare_data(trichoptera$Abundance, trichoptera$Covariate)
myPLNLDA <- PLNLDA(Abundance ~ 1, data = trichoptera, control = PLN_param(covariance = "spherical"))
class(myPLNLDA)
print(myPLNLDA)
} # }