beam-class: Class beam

In gleday/beam: Fast Bayesian Inference in Large Gaussian Graphical Models

Class beam

Description

An S4 class representing the output of the beam function.

Usage

  ## S4 method for signature 'beam'
print(x, ...)

  ## S4 method for signature 'beam'
show(object)

  ## S4 method for signature 'beam'
summary(object, ...)

  ## S4 method for signature 'beam'
marg(object)

  ## S4 method for signature 'beam'
cond(object)

  ## S4 method for signature 'beam'
mcor(object)

  ## S4 method for signature 'beam'
pcor(object)
  
  ## S4 method for signature 'beam'
postExpSigma(object, vars.method="eb")
  
  ## S4 method for signature 'beam'
postExpOmega(object, vars.method="eb")

  ## S4 method for signature 'beam'
plotML(object, ...)

  ## S4 method for signature 'beam'
plotCor(object, type = object@type, order = 'original', by = "marginal")

  ## S4 method for signature 'beam'
bgraph(object)

  ## S4 method for signature 'beam'
ugraph(object)

Arguments

x	An object of class beam-class
object	An object of class beam-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 correlation matrix are reordered using the cluster memberships obtained by the Louvain clustering algorithm.
by	character. When type ="both" and order = 'clust', specifies whether the clustering has to be performed using the complete weighted marginal or conditional independence graph.
vars.method	character. Method of shrinkage estimation for the variances. Either 'eb', 'mean', 'median' for shrinkage estimation of variance respectively towards an estimated shrinkage target, the mean or the median of the sample variances. Choosing 'none' carries out no shrinkage and uses the sample variances, whereas choosing 'scaled' means that the sample covariance has unit diagonal.
...	further arguments passed to or from other methods.

Slots

table

dat.frame. A data.frame containing marginal and/or partial correlation estimates, Bayes factors and tail probabilities for each edge.

deltaOpt

numeric. Empirical Bayes estimate of hyperparameter delta.

alphaOpt

numeric. Empirical Bayes estimate of hyperparameter alpha.

dimX

numeric. Dimension of the input data matrix X.

type

character. Input argument.)

varlabs

character. Column labels of X.

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.

return.only

character. Input argument.

time

numeric. Running time (in seconds).

TinvStdev

numeric. Square root of partial variances.

s

numeric. Sample variances.

rzij

numeric. Statistics.

Author(s)

Gwenael G.R. Leday and Ilaria Speranza

gleday/beam documentation built on June 10, 2025, 9:41 a.m.