R6 class for normal-block model with unknown Q (number of groups)

R6 class for normal-block model with unknown Q (number of groups)

R6 class for normal-block model with unknown Q (number of groups)

Public fields

best_models

list of models explored so far indexed by the cluster sizes

ICL_explored

list of ICL reached so far, indexed by the cluster sizes

n_clusters_range

the range of the cluster sizes to browse

control

store the list of user-defined model settings and optimization parameters

Active bindings

best_model

best model explored so far in term of ICL

Methods

Public methods

• selection_n_clusters$new()

• selection_n_clusters$fit()

• selection_n_clusters$train_best_candidates()

• selection_n_clusters$explore_forward()

• selection_n_clusters$explore_backward()

• selection_n_clusters$plot()

• selection_n_clusters$clone()

---

Method new()

Explores and selects the optimal number of classes

Usage

selection_n_clusters$new(
  mydata,
  n_clusters_range,
  zero_inflation = FALSE,
  sparsity = 0,
  control = NB_control()
)

Arguments

mydata

object of NBData class, with responses and design matrix

n_clusters_range

a size-2 vector with the range of cluster size to browse

zero_inflation

whether the models in the collection should be zero-inflated or not

sparsity

sparsity penalty on the network density

control

structured list of more specific parameters, to generate with NB_control

Returns

The best model in the ICL sense

---

Method fit()

perform model selection with forward/backward exploration with split and merge strategy

Usage

selection_n_clusters$fit()

---

Method train_best_candidates()

perform model selection with forward/backward exploration with split and merge strategy

Usage

selection_n_clusters$train_best_candidates(model, strategy, max_training = 3)

Arguments

model

a normal-block model

strategy

a character, either "split" or "merge"

max_training

maximal of model fully trained at each step of the exploration

---

Method explore_forward()

perform forward exploration with a split strategy

Usage

selection_n_clusters$explore_forward()

---

Method explore_backward()

perform backward exploration with a merge strategy

Usage

selection_n_clusters$explore_backward()

---

Method plot()

Display the ICL for all the best models explored per number of cluster, and the winner

Usage

selection_n_clusters$plot()

Returns

a [`ggplot2::ggplot`] graph

---

Method clone()

The objects of this class are cloneable with this method.

Usage

selection_n_clusters$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.