| PLNLDAfit_diagonal | R Documentation |
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
PLNmodels::PLNfit -> PLNmodels::PLNLDAfit -> PLNLDAfit_diagonal
vcov_modelcharacter: the model used for the residual covariance
nb_paramnumber of parameters in the current PLN model
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()new()Initialize a PLNfit model
PLNLDAfit_diagonal$new( grouping, responses, covariates, offsets, weights, formula, control )
groupinga factor specifying the class of each observation used for discriminant analysis.
responsesthe matrix of responses (called Y in the model). Will usually be extracted from the corresponding field in PLNfamily-class
covariatesdesign matrix (called X in the model). Will usually be extracted from the corresponding field in PLNfamily-class
offsetsoffset matrix (called O in the model). Will usually be extracted from the corresponding field in PLNfamily-class
weightsan optional vector of observation weights to be used in the fitting process.
formulamodel formula used for fitting, extracted from the formula in the upper-level call
controla list for controlling the optimization. See details.
clone()The objects of this class are cloneable with this method.
PLNLDAfit_diagonal$clone(deep = FALSE)
deepWhether to make a deep clone.
## Not run:
data(trichoptera)
trichoptera <- prepare_data(trichoptera$Abundance, trichoptera$Covariate)
myPLNLDA <- PLNLDA(Abundance ~ 1, data = trichoptera, control = PLN_param(covariance = "diagonal"))
class(myPLNLDA)
print(myPLNLDA)
## End(Not run)
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