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

PLNmodels::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

Public methods

• PLNfit_diagonal$new()

• PLNfit_diagonal$clone()

Inherited methods

• PLNmodels::PLNfit$optimize()
• PLNmodels::PLNfit$optimize_vestep()
• PLNmodels::PLNfit$postTreatment()
• PLNmodels::PLNfit$predict()
• PLNmodels::PLNfit$predict_cond()
• PLNmodels::PLNfit$print()
• PLNmodels::PLNfit$show()
• PLNmodels::PLNfit$update()

---

Method 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.

---

Method 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

PLNmodels::PLNfit -> PLNmodels::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

• PLNmodels::PLNfit$optimize_vestep()
• PLNmodels::PLNfit$predict_cond()
• PLNmodels::PLNfit$print()
• PLNmodels::PLNfit$update()
• PLNmodels::PLNLDAfit$optimize()
• PLNmodels::PLNLDAfit$plot_LDA()
• PLNmodels::PLNLDAfit$plot_correlation_map()
• PLNmodels::PLNLDAfit$plot_individual_map()
• PLNmodels::PLNLDAfit$postTreatment()
• PLNmodels::PLNLDAfit$predict()
• PLNmodels::PLNLDAfit$setVisualization()
• PLNmodels::PLNLDAfit$show()

---

Method 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.

---

Method 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)
} # }