Zero-Inflated Normal-Block Model with Unknown Clustering

R6 class for a zero-inflated normal-block model with a fixed number of clusters (but unknown clustering).

Super classes

NormalBlockBase -> NormalBlockVarBase -> ZINormalBlockVarUnknownClusters

Public fields

fixed_tau

whether tau should be fixed at clustering_init during optimization, useful for stability selection

Active bindings

nb_param

number of parameters in the model

var_par

a list with variational parameters

model_par

a list with model parameters: B (covariates), dm1 (species variance), Omega (blocks precision matrix), kappa (zero-inflation probabilities)

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

• ZINormalBlockVarUnknownClusters$new()

• ZINormalBlockVarUnknownClusters$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()

---

ZINormalBlockVarUnknownClusters$new()

Create a new [`ZINormalBlockVarUnknownClusters`] object.

Usage

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

Arguments

data

object of NormalBlockVarData class, with responses and design matrix

q

required number of groups

sparsity

to apply on variance matrix when calling GLASSO

control

structured list of more specific parameters

Returns

A new [`ZINormalBlockVarUnknownClusters`] object

---

ZINormalBlockVarUnknownClusters$clone()

The objects of this class are cloneable with this method.

Usage

ZINormalBlockVarUnknownClusters$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, kappa = rep(0.3, 20))
data <- NormalBlockData$new(ex$Y, ex$X)
model <- normal_block(data, blocks = 3, zero_inflation = TRUE,
                      control = NB_control(verbose = FALSE))
model$clustering
#>  [1] 1 2 3 3 2 1 3 3 1 2 2 2 1 1 2 3 3 3 3 2