Mean-Block Model with Unknown Clustering

R6 class for a Normal-Block-Mean model with a fixed number of clusters (but unknown clustering), inferred by variational EM.

Super classes

NormalBlockBase -> NormalBlockMeanBase -> NormalBlockMeanUnknownClusters

Active bindings

fitted

Y values predicted by the model, in Y's original units

var_par

a list with the variational parameter: tau (posterior group probabilities)

nb_param

number of parameters in the model

entropy

Entropy of the conditional distribution The only latent variable is the clustering, so the entropy of the variational distribution reduces to -sum(tau * log(tau)).

who_am_I

a method to print what model is being fitted

Methods

Public methods

• NormalBlockMeanUnknownClusters$new()

• NormalBlockMeanUnknownClusters$clone()

Inherited methods

• NormalBlockBase$best_of_inits()
• NormalBlockBase$candidates_merge()
• NormalBlockBase$candidates_split()
• NormalBlockBase$latent_network()
• NormalBlockBase$optimize()
• NormalBlockBase$plot()
• NormalBlockBase$plot_loglik()
• NormalBlockBase$plot_network()
• NormalBlockBase$print()
• NormalBlockBase$update()
• NormalBlockMeanBase$merge()
• NormalBlockMeanBase$predict()
• NormalBlockMeanBase$split()
• NormalBlockMeanBase$warm_start_from()

---

NormalBlockMeanUnknownClusters$new()

Create a new [`NormalBlockMeanUnknownClusters`] object.

Usage

NormalBlockMeanUnknownClusters$new(
  data,
  q,
  sparsity = 0,
  control = NB_control()
)

Arguments

data

object of NormalBlockData class, with responses and design matrix

q

number of clusters

sparsity

to apply on variance matrix when calling GLASSO

control

structured list of more specific parameters, to generate with NB_control

Returns

A new [`NormalBlockMeanUnknownClusters`] object

---

NormalBlockMeanUnknownClusters$clone()

The objects of this class are cloneable with this method.

Usage

NormalBlockMeanUnknownClusters$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

ex <- generate_normal_block_mean_data(n = 50, p = 20, d = 1, q = 3)
data <- NormalBlockData$new(ex$Y, ex$X)
model <- normal_block(data, blocks = 3, model = "mean")
#> Fitting a diagonal normal-block-mean model with 3 unknown blocks 
#> 
#> DONE
model$clustering
#>  [1] 3 2 2 2 2 3 1 2 2 1 2 2 2 2 1 3 1 3 1 3