Split-Half Reliability for an LSA Fit

Estimates the reliability of an LSA network by repeated random split-half resampling of sequences. Each replicate draws two disjoint halves of the sequences without replacement, refits the engine on each half, and computes the correlation between the two half-network edge-weight vectors. Returns the distribution of replicate correlations plus a point summary.

Usage

reliability_lsa(fit, ...)

# S3 method for class 'lsa'
reliability_lsa(
  fit,
  R = 100L,
  weights = c("prob", "count", "adj_res"),
  method = c("pearson", "spearman"),
  parallel = FALSE,
  n_cores = NULL,
  verbose = FALSE,
  ...
)

# S3 method for class 'lsa_group'
reliability_lsa(fit, ...)

Arguments

fit

An lsa object returned by lsa(). Must be built from event-level data (sequences), not from a pre-computed transition matrix.

...

Reserved.

R

Integer. Number of split-half replicates. Default 100.

weights

Character. Which edge matrix to correlate across halves: "prob" (default), "count", or "adj_res".

method

Character. Correlation method: "pearson" (default) or "spearman".

parallel

Logical. Use multi-core resampling. Default FALSE.

n_cores

Integer. Worker count when parallel = TRUE.

verbose

Logical. Print progress every 100 replicates.

Value

An object of class c("lsa_reliability", "list") with:

correlations

Numeric vector of length R: the split-half correlation of each replicate.

mean, sd

Mean and standard deviation of the finite replicate correlations.

ci_low, ci_high

Empirical 2.5% and 97.5% quantiles.

R, weights, method, n_sequences

Recipe metadata.

fit

Reference to the original fit.

References

Epskamp, S., Borsboom, D., & Fried, E. I. (2018). Estimating psychological networks and their accuracy: A tutorial paper. Behavior Research Methods, 50(1), 195-212.

For a grouped fit (lsa_group), reliability is estimated separately within each group and the per-group lsa_reliability objects are returned in an lsa_reliability_group container with its own print method.

See also

bootstrap_lsa(), stability_lsa(), permute_lsa()

Examples

# \donttest{
fit <- lsa(engagement, engine = "classical")
rel <- reliability_lsa(fit, R = 50)
rel
#> <lsa_reliability>
#>   engine:        classical
#>   replicates:    50
#>   weights:       prob
#>   method:        pearson
#>   n sequences:   136
#>   split-half r:  0.971  (sd = 0.022)
#>   95% CI:        [0.921, 0.993]
# }