Base Class for Variance-Block Models — NormalBlockVarBase • normalblockr

R6 abstract class for the sparse Normal-Block models, where the clustering structures the latent covariance.

Super class

NormalBlockBase -> NormalBlockVarBase

Active bindings

B_original: regression coefficients (d x p), converted back to Y's original units (undoing `NormalBlockData(scale = TRUE)`'s column-wise rescaling, if any). Use `model_par$B` instead for the coefficients on the internal fitting scale.
d0: number of zi variables (dimensions in X0)
model_par: a list with the matrices of the model parameters: B (covariates), dm1 (species variance), Omega (groups precision matrix)). On the internal fitting scale (`self$data$Y`, possibly column-rescaled by `NormalBlockData(scale = TRUE)`) – use `$B_original`/`$dm1_original` for the same quantities converted back to Y's original units.
nb_param: number of parameters in the model
sparsity_weights: (weights associated to each pair of groups)
dm1_original: inverse residual variance per variable (1 / Var(Y_j)), converted back to Y's original units. Use `model_par$dm1` instead for the internal fitting scale. With `noise_covariance = "spherical"`, `model_par$dm1` is a single value repeated p times; once converted back per-variable, the p values returned here generally differ from one another whenever Y's columns were rescaled by different factors.

Methods

Inherited methods

`NormalBlockVarBase$new()`

Create a new [`NormalBlockVarBase`] object.

Usage

NormalBlockVarBase$new(
  data,
  q,
  sparsity = 0,
  control = NB_control(),
  zero_inflation = FALSE
)

Arguments

data: object of NormalBlockData class, with responses and design matrix
q: number of block/cluster
sparsity: sparsity penalty on the network density
control: structured list of more specific parameters, to generate with NB_control
zero_inflation: whether the concrete subclass models zero-inflation; set by the ZI subclasses themselves, not meant to be set by the end user. When `FALSE`, the (costly) zero-inflation probability fit (`kappa`/`B0`) is skipped entirely, since it would otherwise never be used downstream.

Returns

A new [`NormalBlockVarBase`] object

`NormalBlockVarBase$warm_start_from()`

Seed this model's starting parameters from another, already-optimized model with the same q, instead of a fresh heuristic clustering. Used by [NormalBlockVarCollectionSparsity] to warm-start each penalty in a sparsity path from the previous one's solution.

Usage

NormalBlockVarBase$warm_start_from(other)

Arguments

other: a [NormalBlockVarBase] object, already optimized

Returns

Update the current object in place with `other`'s parameters

`NormalBlockVarBase$split()`

Create a clone of the current [`NormalBlockVarBase`] object after splitting cluster `cl` We split the cluster according to the species variances

Usage

NormalBlockVarBase$split(index, in_place = FALSE)

Arguments

index: index (integer) of the cluster to split
in_place: should the split applied to the object itself, or should a copy be sent? default FALSE (send a copy)

Returns

A new [`NormalBlockVarBase`] object

`NormalBlockVarBase$merge()`

Create a clone of the current [`NormalBlockVarBase`] object after merging clusters `cl1` and `cl2`

Usage

NormalBlockVarBase$merge(indices, in_place = FALSE)

Arguments

indices: indices (couple of integer) of the clusters to merge
in_place: should the split applied to the object itself, or should a copy be sent? default FALSE (send a copy)

Returns

A new [`NormalBlockVarBase`] object

`NormalBlockVarBase$clone()`

The objects of this class are cloneable with this method.

Usage

NormalBlockVarBase$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Examples

# An internal abstract base class, never instantiated directly. See
# normal_block() for how concrete models (NormalBlockVarKnownClusters,
# NormalBlockVarUnknownClusters, and their zero-inflated variants) are
# actually created and fitted.