Split-Half Reliability for Network Estimates

Assesses the stability of network estimates by repeatedly splitting sequences into two halves, building networks from each half, and comparing them. Supports single-model reliability assessment and multi-model comparison with optional scaling for cross-method comparability.

For transition methods ("relative", "frequency", "co_occurrence"), uses pre-computed per-sequence count matrices for fast resampling (same infrastructure as bootstrap_network).

Usage

reliability(..., iter = 1000L, split = 0.5, scale = "none", seed = NULL)

Arguments

...

One or more netobjects (from build_network). If unnamed, each model is auto-named from its $method. A netobject_group is flattened into its constituent models.

iter

Integer. Number of split-half iterations (default: 1000).

split

Numeric. Fraction of sequences assigned to the first half (default: 0.5).

scale

Character. Scaling applied to both split-half matrices before computing metrics. One of "none" (default), "minmax", "standardize", or "proportion". Use scaling when comparing models on different scales (e.g. frequency vs relative).

seed

Integer or NULL. RNG seed for reproducibility.

Value

An object of class "net_reliability" containing:

iterations

Data frame with columns model, mean_dev, median_dev, cor, max_dev (one row per iteration per model).

summary

Data frame with columns model, metric, mean, sd.

models

Named list of the original netobjects.

iter

Number of iterations.

split

Split fraction.

scale

Scaling method used.

See also

build_network, bootstrap_network

Examples

# \donttest{
seqs <- data.frame(
  V1 = sample(LETTERS[1:4], 30, TRUE), V2 = sample(LETTERS[1:4], 30, TRUE),
  V3 = sample(LETTERS[1:4], 30, TRUE), V4 = sample(LETTERS[1:4], 30, TRUE)
)
net <- build_network(seqs, method = "relative")
rel <- reliability(net, iter = 100, seed = 42)
print(rel)
#> Split-Half Reliability (100 iterations, split = 50%)
#>   Mean Abs. Dev.      mean = 0.1487  sd = 0.0310
#>   Median Abs. Dev.    mean = 0.1290  sd = 0.0348
#>   Correlation         mean = 0.3350  sd = 0.1949
#>   Max Abs. Dev.       mean = 0.3851  sd = 0.0974
# }