SEset (version 1.0.1)

r2_distribution: Compute Controllability Distribution in the SE-set

Description

A function used to analyse the SEset results. For each member of the SE-set, calculate the proportion of explained variance in each child node, when predicted by all of its parent nodes

Usage

r2_distribution(SEmatrix, cormat, names = NULL, indices = NULL)

Arguments

SEmatrix

An \(n \times p\) matrix containing the SEset, where each row represents a \(p \times p\) weights matrix stacked column-wise

cormat

A \(p \times p\) matrix containing the marginal covariances or correlations

names

optional character vector containing dimension names

indices

option vector of matrix indices, indicating which variables to compute the R^2 distribution for

Value

Returns an \(n \times p\) matrix of \(R^2\) values. For each member of the SE-set, this represents the variance explained in node \(X_i\) by it's parents in that weighted DAG.

References

ryan2019SEset haslbeck2018wellSEset

See Also

network_to_SEset, find_parents

Examples

# NOT RUN {
# first estimate the precision matrix
data(riskcor)
omega <- (qgraph::EBICglasso(riskcor, n = 69, returnAllResults = TRUE))$optwi
# qgraph method estimates a non-symmetric omega matrix, but uses forceSymmetric to create
# a symmetric matrix (see qgraph:::EBICglassoCore line 65)
omega <- as.matrix(Matrix::forceSymmetric(omega)) # returns the precision matrix

SEmatrix <- network_to_SEset(omega, digits=3)

r2set  <- r2_distribution(SEmatrix, cormat = riskcor, names = NULL, indices = c(1,3,4,5,6))
# Plot results
apply(r2set,2,hist)
# For ggplot format, execute
# r2set <- tidyr::gather(r2set)
# }