beam.select-class: Class beam.select

Description

An S4 class representing the output of the beam.select function.

Usage

# S4 method for beam.select
print(x, ...)
  # S4 method for beam.select
show(object)
  # S4 method for beam.select
summary(object, ...)
  
  # S4 method for beam.select
marg(object)
  # S4 method for beam.select
cond(object)
  # S4 method for beam.select
mcor(object)
  
  # S4 method for beam.select
pcor(object)
  # S4 method for beam.select
plotML(object, ...)
  # S4 method for beam.select
plotAdj(object, type=object@type, order = "original")
  # S4 method for beam.select
bgraph(object)
  # S4 method for beam.select
ugraph(object)

Arguments

x: An object of class beam.select-class
object: An object of class beam.select-class
type: character. Type of correlation to be displayed (marginal, conditional or both)
order: character. Either 'original' or 'clust'. If 'clust' the rows and columns of the adjacency matrix are reordered using the cluster memberships obtained by the Louvain clustering algorithm.
...: further arguments passed to or from other methods.

Slots

marginal: data.frame. A data.frame containing the marginal correlation estimates, Bayes factors and tail probabilities for the selected edges only.
conditional: data.frame. A data.frame containing the partial correlation estimates, Bayes factors and tail probabilities for the selected edges only.
dimX: numeric. Dimension of the input data matrix X.
type: character. Input type (marginal, conditional or both)
varlabs: character. Column labels of X.
alphaOpt: numeric. Empirical Bayes estimates of hyperparameter alpha.
gridAlpha: matrix. A matrix containing the log-marginal likelihood of the Gaussian conjugate model as a function of a grid of values of alpha and delta.
valOpt: numeric. Maximum value of the log-marginal likelihood of the Gaussian conjugate model
method: character. Input method.
thres: numeric. Input threshold

Author

Gwenael G.R. Leday and Ilaria Speranza