r2_distribution {SEset}

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

Ryan O, Bringmann LF, Schuurman NK (upcoming). “The challenge of generating causal hypotheses using network models.” in preperation. Haslbeck JM, Waldorp LJ (2018). “How well do network models predict observations? On the importance of predictability in network models.” Behavior Research Methods, 50(2), 853–861.

See Also

network_to_SEset, find_parents

Examples

# 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)