Reports how well each node is predicted by the others in a fitted network.
For Gaussian graphical models this is the closed-form variance explained
(R-squared) from the precision matrix and needs no data. For the nodewise
models (ising_fit(), ising_sampler(), mgm_fit()) it requires the data
and reports R-squared for Gaussian nodes and classification accuracy (CC)
plus normalized accuracy (nCC) for binary nodes.
Arguments
- x
A psychnet object.
- data
The data the network was estimated from; required for the nodewise models (ising / IsingSampler / mgm), ignored for the GGMs.
- ...
Unused.
Value
A tidy data.frame, one row per node, with columns node, type
("gaussian" or "binary"), metric ("R2" or "nCC"),
predictability, and accuracy (classification accuracy for binary nodes,
NA for Gaussian).
Examples
S <- 0.4^abs(outer(1:6, 1:6, "-"))
net_predict(ebic_glasso(cor_matrix = S, n = 250))
#> node type metric predictability accuracy
#> 1 V1 gaussian R2 0.1568160 NA
#> 2 V2 gaussian R2 0.2711166 NA
#> 3 V3 gaussian R2 0.2711166 NA
#> 4 V4 gaussian R2 0.2711166 NA
#> 5 V5 gaussian R2 0.2711166 NA
#> 6 V6 gaussian R2 0.1568160 NA