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_model`

character: the model used for the residual covariance

`nb_param`

number 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 )

`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.

`clone()`

The objects of this class are cloneable with this method.

PLNLDAfit_diagonal$clone(deep = FALSE)

`deep`

Whether 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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