Graph selection for explore Objects — select.explore • BGGM

Provides the selected graph based on the Bayes factor (Williams and Mulder 2019) .

Usage

# S3 method for class 'explore'
select(object, BF_cut = 3, alternative = "two.sided", ...)

Arguments

object: An object of class explore.default
BF_cut: Numeric. Threshold for including an edge (defaults to 3).
alternative: A character string specifying the alternative hypothesis. It must be one of "two.sided" (default), "greater", "less", or "exhaustive". See note for further details.
...: Currently ignored.

Value

The returned object of class select.explore contains a lot of information that is used for printing and plotting the results. For users of BGGM, the following are the useful objects:

alternative = "two.sided"

pcor_mat_zero Selected partial correlation matrix (weighted adjacency).
pcor_mat Partial correlation matrix (posterior mean).
Adj_10 Adjacency matrix for the selected edges.
Adj_01 Adjacency matrix for which there was evidence for the null hypothesis.

alternative = "greater" and "less"

pcor_mat_zero Selected partial correlation matrix (weighted adjacency).
pcor_mat Partial correlation matrix (posterior mean).
Adj_20 Adjacency matrix for the selected edges.
Adj_02 Adjacency matrix for which there was evidence for the null hypothesis (see note).

alternative = "exhaustive"

post_prob A data frame that included the posterior hypothesis probabilities.
neg_mat Adjacency matrix for which there was evidence for negative edges.
pos_mat Adjacency matrix for which there was evidence for positive edges.
neg_mat Adjacency matrix for which there was evidence for the null hypothesis (see note).
pcor_mat Partial correlation matrix (posterior mean). The weighted adjacency matrices can be computed by multiplying pcor_mat with an adjacency matrix.

Details

Exhaustive provides the posterior hypothesis probabilities for a positive, negative, or null relation (see Table 3 in Williams and Mulder 2019) .

Note

Care must be taken with the options alternative = "less" and alternative = "greater". This is because the full parameter space is not included, such, for alternative = "greater", there can be evidence for the "null" when the relation is negative. This inference is correct: the null model better predicted the data than the positive model. But note this is relative and does not provide absolute evidence for the null hypothesis.

References

Williams DR, Mulder J (2019). “Bayesian Hypothesis Testing for Gaussian Graphical Models: Conditional Independence and Order Constraints.” PsyArXiv. doi:10.31234/osf.io/ypxd8 .

Examples


# \donttest{
#################
### example 1 ###
#################

#  data
Y <- bfi[,1:10]

# fit model
fit <- explore(Y, progress = FALSE)

# edge set
E <- select(fit,
            alternative = "exhaustive")

# }

Graph selection for `explore` Objects