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 multiplyingpcor_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 .
See also
explore
and ggm_compare_explore
for several examples.