Normal-Block Model with Known Clustering

R6 class for a normal-block model with known clustering.

Super classes

NormalBlockBase -> NormalBlockVarBase -> NormalBlockVarKnownClusters

Active bindings

posterior_par

a list with the parameters of posterior distribution W | Y

entropy

Entropy of the conditional distribution

fitted

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

who_am_I

a method to print what model is being fitted

Methods

Public methods

• NormalBlockVarKnownClusters$new()

• NormalBlockVarKnownClusters$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$predict()
• NormalBlockBase$print()
• NormalBlockBase$update()
• NormalBlockVarBase$merge()
• NormalBlockVarBase$split()
• NormalBlockVarBase$warm_start_from()

---

NormalBlockVarKnownClusters$new()

Create a new [`NormalBlockVarKnownClusters`] object.

Usage

NormalBlockVarKnownClusters$new(data, C, sparsity = 0, control = NB_control())

Arguments

data

object of NormalBlockVarData class, with responses and design matrix

C

clustering matrix C_jk = 1 if species j belongs to cluster k

sparsity

to apply on variance matrix when calling GLASSO

control

structured list of more specific parameters, to generate with NB_control

Returns

A new [`NormalBlockVarKnownClusters`] object

---

NormalBlockVarKnownClusters$clone()

The objects of this class are cloneable with this method.

Usage

NormalBlockVarKnownClusters$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

ex <- generate_normal_block_var_data(n = 50, p = 20, d = 1, q = 3)
data <- NormalBlockData$new(ex$Y, ex$X)
model <- normal_block(data, blocks = ex$parameters$C, control = NB_control(verbose = FALSE))
model$clustering
#>  [1] 2 3 2 1 1 3 2 3 3 3 3 1 3 2 1 1 3 1 3 2