Repeatedly splits the sample into two halves, estimates a network on each, and compares their edge-weight vectors. Reports, across splits, the edge-weight correlation between halves plus the mean/median/maximum absolute edge deviation - a psychometric reliability view of the estimated structure.
Usage
network_reliability(
data,
method = "glasso",
iter = 100L,
split = 0.5,
cor_method = c("pearson", "spearman", "kendall"),
labels = NULL,
...
)Arguments
- data
Numeric data frame or matrix (rows = observations), or a
psychnet_group(split-half per level).- method
Estimator (see
psychnet()). Default"glasso".- iter
Number of split-half iterations. Default 100.
- split
Fraction of rows in the first half. Default 0.5.
- cor_method
Correlation method for the between-halves edge comparison:
"pearson"(default),"spearman", or"kendall".- labels
Optional node labels.
- ...
Passed to the estimator.
Value
A tidy data.frame (class psychnet_reliability), one row per metric
with columns metric, mean, sd, lower, upper. The per-split draws
are carried in attr(x, "iterations") for plot.psychnet_reliability().
Examples
# `iter` is kept small here so the example runs quickly; the default
# (iter = 100) is what a real reliability assessment should use.
network_reliability(SRL_Claude, iter = 10)
#> # split-half reliability: glasso | 10 iterations (50/50 split)
#> metric mean sd lower upper
#> 1 mean_abs_dev 0.06505893 0.02720464 0.02471090 0.10065454
#> 2 median_abs_dev 0.05980563 0.02943647 0.01557087 0.09904948
#> 3 correlation 0.98131883 0.01347642 0.95790513 0.99665236
#> 4 max_abs_dev 0.15438705 0.06804919 0.06582753 0.26122348