R6 class for normal-block model with fixed number of groups

R6 class for normal-block model with fixed number of groups

R6 class for normal-block model with fixed number of groups

Super class

normalblockr::NB -> NB_fixed_Q

Public fields

fixed_tau

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

Active bindings

model_par

a list with the matrices of the model parameters: B (covariates), dm1 (species variance), OmegaQ (groups precision matrix))

nb_param

number of parameters in the model

var_par

a list with the matrices of the variational parameters: M (means), S (variances), tau (posterior group probabilities)

entropy

Entropy of the conditional distribution

fitted

Y values predicted by the model

who_am_I

a method to print what model is being fitted

Methods

Public methods

• NB_fixed_Q$new()

• NB_fixed_Q$clone()

Inherited methods

• normalblockr::NB$candidates_merge()
• normalblockr::NB$candidates_split()
• normalblockr::NB$latent_network()
• normalblockr::NB$merge()
• normalblockr::NB$optimize()
• normalblockr::NB$plot_loglik()
• normalblockr::NB$plot_network()
• normalblockr::NB$split()
• normalblockr::NB$update()

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Method new()

Create a new [`NB_fixed_Q`] object.

Usage

NB_fixed_Q$new(data, Q, sparsity = 0, control = NB_control())

Arguments

data

contains the matrix of responses (Y) and the design matrix (X).

Q

required number of groups

sparsity

sparsity penalty to add on blocks precision matrix for sparsity

control

structured list for specific parameters

Returns

A new [`NB_fixed_Q`] object

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Method clone()

The objects of this class are cloneable with this method.

Usage

NB_fixed_Q$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.