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R6 class for normal-block model with unknown Q (number of groups)

Source: R/NB_unknown_Q_changing_sparsity_collection_class.R
NB_unknown_Q_changing_sparsity.Rd

R6 class for normal-block model with unknown Q (number of groups)

R6 class for normal-block model with unknown Q (number of groups)

Public fields

models

list of NB_fixed_Q models corresponding to each nb_block value

control

store the list of user-defined model settings and optimization parameters

Active bindings

Q_list

number of blocks

criteria

a data frame with the values of some criteria ((approximated) log-likelihood, BIC, ICL) for the collection of models

sparsity

list of penalties used for each Q

who_am_I

a method to print what model is being fitted

Methods

Public methods

Method new()

Create a new [`NB_unknown_Q_changing_sparsity`] object.

Usage

NB_unknown_Q_changing_sparsity$new(
  mydata,
  Q_list,
  zero_inflation = FALSE,
  control = NB_control()
)

Arguments

mydata

object of NBData class, with responses and design matrix

Q_list

list of Q values (number of groups) in the collection

zero_inflation

boolean to specify whether data is zero-inflated

control

structured list of parameters to handle sparsity control

Returns

A new [`NB_unknown_Q_changing_sparsity`] object

Method optimize()

optimizes an NB_changing_sparsity object for each penalty value

Usage

NB_unknown_Q_changing_sparsity$optimize(
  control = list(niter = 100, threshold = 1e-04, verbose = TRUE)
)

Arguments

control

optimization parameters (niter and threshold)

Method get_model()

returns a collection of NB_unknown models corresponding to given Q or one single model if penalty is also given

Usage

NB_unknown_Q_changing_sparsity$get_model(Q, sparsity = NA)

Arguments

Q

number of blocks asked by user.

sparsity

sparsity penalty penalty asked by user

Returns

either a NB_changing_sparsity or a NB_fixed_Q object

Method get_best_model()

Extract best model in the collection

Usage

NB_unknown_Q_changing_sparsity$get_best_model(crit = c("ICL", "BIC", "EBIC"))

Arguments

crit

a character for the criterion used to performed the selection. Either "BIC", "EBIC" or "ICL. "ICL" is the default criterion

Returns

a [`NB_unknown`] object

Method plot()

Display various outputs (goodness-of-fit criteria, robustness, diagnostic) associated with a collection of network fits (a [`Networkfamily`])

Usage

NB_unknown_Q_changing_sparsity$plot(
  criterion = c("deviance", "ICL", "BIC", "EBIC"),
  n_intervals = NULL
)

Arguments

criterion

The criteria to plot in `c("deviance", BIC", "EBIC", "ICL")`. Defaults deviance.

n_intervals

number of intervals into which the penalties range should be splitted

Returns

a [`ggplot`] heatmap

Method clone()

The objects of this class are cloneable with this method.

Usage

NB_unknown_Q_changing_sparsity$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.