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

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

# S3 method for class 'net_reliability'
print(x, ...)

# S3 method for class 'net_reliability'
summary(object, ...)

# S3 method for class 'net_reliability'
plot(x, bins = 60L, combined = TRUE, ...)

Arguments

...

One or more netobjects (from build_network). If unnamed, each model is auto-named from its $method; duplicate names are made unique with make.unique(). A netobject_group is flattened into its constituent models (named by group), and an mcml or cograph_network is converted first. In plot.net_reliability() and print.net_reliability(): Additional arguments (ignored). In summary.net_reliability(): Ignored.

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.

x

For the print() and plot() methods: an object of class net_reliability.

object

For the summary() method: an object of class net_reliability.

bins

Integer. Number of histogram bins per panel (default 60).

combined

When TRUE (default), all four metrics are shown in one ggplot via facet_wrap(~ metric). When FALSE, returns a named list of four single-panel ggplots, one per metric.

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.

In print.net_reliability(): The input object, invisibly.

In summary.net_reliability(): A tidy data frame with columns model, metric, mean, sd summarising the split-half iterations.

In plot.net_reliability(): A ggplot object (invisibly), or a named list of four ggplots when combined = FALSE.

Methods

• plot.net_reliability(): Density plots of split-half metrics faceted by metric type. Multi-model comparisons show overlaid densities colored by model.

See also

build_network, bootstrap_network

Examples

net <- build_network(data.frame(V1 = c("A","B","C","A"),
  V2 = c("B","C","A","B")), method = "relative")
rel <- network_reliability(net, iter = 10)
# \donttest{
set.seed(1)
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 <- network_reliability(net, iter = 100, seed = 42)
print(rel)
#> Split-Half Reliability (100 iterations, split = 50%)
#>   Mean Abs. Diff.     mean = 0.1537  sd = 0.0300
#>   Median Abs. Diff.   mean = 0.1329  sd = 0.0322
#>   Pearson             mean = 0.2006  sd = 0.1882
#>   Max Abs. Diff.      mean = 0.3760  sd = 0.0972
# }