Edge-weight stability coefficient (case-dropping subset bootstrap)

The edge-vector complement to net_stability(). For each drop proportion the network is re-estimated on random case-dropped subsets and the subset edge-weight vector is compared with the full-sample one. The edge-weight CS-coefficient is the largest drop proportion at which the edge-vector correlation stays >= threshold with probability >= certainty (Epskamp, Borsboom & Fried 2018).

Usage

casedrop_reliability(
  data,
  method = "glasso",
  drop_prop = seq(0.1, 0.9, by = 0.1),
  iter = 100L,
  threshold = 0.7,
  certainty = 0.95,
  cor_method = c("spearman", "pearson", "kendall"),
  labels = NULL,
  ...
)

Arguments

data: Numeric data frame or matrix (rows = observations), or a psychnet_group (case-dropped per level).
method: Estimator (see psychnet()). Default "glasso".
drop_prop: Proportions of cases to drop. Default seq(0.1, 0.9, 0.1).
iter: Subsets per proportion. Default 100.
threshold: Minimum acceptable edge-vector correlation. Default 0.7.
certainty: Probability the correlation must exceed threshold. Default 0.95.
cor_method: Correlation method for the edge-vector comparison: "spearman" (default, robust to the wide range of edge weights), "pearson", or "kendall".
labels: Optional node labels.
...: Passed to the estimator.

Value

A tidy data.frame (class psychnet_casedrop), one row per metric per drop proportion, with columns metric, drop_prop, mean, sd. The edge-weight CS-coefficient is carried in attr(x, "cs") and shown when the result is printed. Visualise it with plot.psychnet_casedrop().

References

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

Examples

# `iter` and `drop_prop` are kept small here so the example runs quickly;
# the defaults (iter = 100, drop_prop = seq(0.1, 0.9, 0.1)) are what a real
# reliability assessment should use.
casedrop_reliability(SRL_Claude, iter = 5, drop_prop = c(0.25, 0.5))
#> # edge-weight stability: glasso | CS = 0.50 (spearman cor >= 0.70 at 95%)
#>           metric drop_prop       mean          sd
#> 1   mean_abs_dev      0.25 0.01887120 0.009120429
#> 2   mean_abs_dev      0.50 0.02206496 0.005387005
#> 3 median_abs_dev      0.25 0.01604977 0.008175395
#> 4 median_abs_dev      0.50 0.01955155 0.006003321
#> 5    correlation      0.25 0.97513583 0.008683012
#> 6    correlation      0.50 0.97512845 0.012960209
#> 7    max_abs_dev      0.25 0.04745924 0.024045972
#> 8    max_abs_dev      0.50 0.05437319 0.015481959