Skip to contents

An R6 Class to represent a collection of PLNPCAfit

Source: R/PLNPCAfamily-class.R
PLNPCAfamily.Rd

The function PLNPCA() produces an instance of this class.

This class comes with a set of methods, some of them being useful for the user: See the documentation for getBestModel(), getModel() and plot().

See also

The function PLNPCA(), the class PLNPCAfit()

Super class

PLNfamily -> PLNPCAfamily

Active bindings

ranks

the dimensions of the successively fitted models

Methods

Public methods

Inherited methods

PLNPCAfamily$new()

Initialize all models in the collection. A single SVD of the residual matrix M - X*B is computed once and shared across all ranks. M and B come from either a user-provided PLNfit inception or a fast LM on log-transformed counts (default, controlled by init_method).

Usage

PLNPCAfamily$new(
  ranks,
  responses,
  covariates,
  offsets,
  weights,
  formula,
  control
)

Arguments

ranks

the dimensions of the successively fitted models

responses

the matrix of responses common to every models

covariates

the matrix of covariates common to every models

offsets

the matrix of offsets common to every models

weights

the vector of observation weights

formula

model formula used for fitting, extracted from the formula in the upper-level call

control

list controlling the optimization and the model

PLNPCAfamily$optimize()

Call to the C++ optimizer on all models of the collection

Usage

PLNPCAfamily$optimize(config)

Arguments

config

list controlling the optimization.

PLNPCAfamily$getModel()

Extract model from collection and add "PCA" class for compatibility with factoextra::fviz()

Usage

PLNPCAfamily$getModel(var, index = NULL)

Arguments

var

value of the parameter (rank for PLNPCA, sparsity for PLNnetwork) that identifies the model to be extracted from the collection. If no exact match is found, the model with closest parameter value is returned with a warning.

index

Integer index of the model to be returned. Only the first value is taken into account.

Returns

a PLNPCAfit object

PLNPCAfamily$getBestModel()

Extract best model in the collection

Usage

PLNPCAfamily$getBestModel(crit = c("ICL", "BIC"))

Arguments

crit

a character for the criterion used to performed the selection. Either "ICL", "BIC". Default is ICL

Returns

a PLNPCAfit object

PLNPCAfamily$plot()

Lineplot of selected criteria for all models in the collection

Usage

PLNPCAfamily$plot(criteria = c("loglik", "BIC", "ICL"), reverse = FALSE)

Arguments

criteria

A valid model selection criteria for the collection of models. Any of "loglik", "BIC" or "ICL" (all).

reverse

A logical indicating whether to plot the value of the criteria in the "natural" direction (loglik - penalty) or in the "reverse" direction (-2 loglik + penalty). Default to FALSE, i.e use the natural direction, on the same scale as the log-likelihood.

Returns

A ggplot2::ggplot object

PLNPCAfamily$show()

User friendly print method

Usage

PLNPCAfamily$show()

PLNPCAfamily$clone()

The objects of this class are cloneable with this method.

Usage

PLNPCAfamily$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

data(trichoptera)
trichoptera <- prepare_data(trichoptera$Abundance, trichoptera$Covariate)
myPCAs <- PLNPCA(Abundance ~ 1 + offset(log(Offset)), data = trichoptera, ranks = 1:5)
#> 
#>  Initialization...
#> 
#>  Adjusting 5 PLN models for PCA analysis.
#> 	 Rank approximation = 1 
	 Rank approximation = 2 
	 Rank approximation = 3 
	 Rank approximation = 4 
	 Rank approximation = 5 

#>  Post-treatments
#>  DONE!
class(myPCAs)
#> [1] "PLNPCAfamily" "PLNfamily"    "R6"