predictability: Compute Node Predictability

In Nestimate: Network Estimation, Bootstrap, and Higher-Order Analysis

Compute Node Predictability

Description

Computes the proportion of variance explained (R^2) for each node in the network, following Haslbeck & Waldorp (2018).

For method = "glasso" or "pcor", predictability is computed analytically from the precision matrix:

R^2_j = 1 - 1 / \Omega_{jj}

where \Omega is the precision (inverse correlation) matrix.

For method = "cor", predictability is the multiple R^2 from regressing each node on its network neighbors (nodes with non-zero edges).

Usage

predictability(object, ...)

## S3 method for class 'netobject'
predictability(object, ...)

## S3 method for class 'netobject_ml'
predictability(object, ...)

## S3 method for class 'netobject_group'
predictability(object, ...)

Arguments

object	A netobject or netobject_ml object.
...	Additional arguments (ignored).

Value

For netobject: a named numeric vector of R^2 values (one per node, between 0 and 1).

For netobject_ml: a list with elements $between and $within, each a named numeric vector.

A named numeric vector of predictability values per node.

A list with within and between predictability vectors.

A named list of per-group predictability vectors.

References

Haslbeck, J. M. B., & Waldorp, L. J. (2018). How well do network models predict observations? On the importance of predictability in network models. Behavior Research Methods, 50(2), 853–861. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.3758/s13428-017-0910-x")}

Examples

set.seed(42)
mat <- matrix(rnorm(60), ncol = 4)
colnames(mat) <- LETTERS[1:4]
net <- build_network(as.data.frame(mat), method = "glasso")
predictability(net)

Nestimate documentation built on April 20, 2026, 5:06 p.m.