Edge-Weight Case-Dropping Stability

htna-named alias of Nestimate::casedrop_reliability(). Computes the CS-coefficient for the edge-weight vector of a network: the maximum proportion of cases (rows of x$data) that can be dropped while the flattened edge-weight vector of the re-estimated network still correlates with the original above threshold in at least certainty of iterations.

Usage

casedrop_reliability_htna(
  x,
  iter = 1000L,
  drop_prop = seq(0.1, 0.9, by = 0.1),
  threshold = 0.7,
  certainty = 0.95,
  method = c("spearman", "pearson", "kendall"),
  include_diag = FALSE,
  seed = NULL
)

Arguments

x

A net_casedrop_reliability_group object.

iter

Integer. Iterations per drop proportion. Default 1000.

drop_prop

Drop proportion at which to report the four metrics (mean +/- sd per network). Must be one of the drop proportions the object was built with. Defaults to the object's median grid value (the stored grid is used, not an assumed 0.7); pass an explicit value not in the grid to get an error listing the available proportions.

threshold

Numeric in [0, 1]. Minimum edge-vector correlation for an iteration to count as stable. Default 0.7.

certainty

Numeric in [0, 1]. Required fraction of iterations whose correlation must exceed threshold for a drop proportion to qualify. Default 0.95.

method

Correlation method: "pearson" (weight magnitudes), "spearman" (ranks, robust to scale), or "kendall". Default "spearman" because edge weights often span several orders of magnitude and rank stability is the typical target.

include_diag

Logical. Include diagonal (self-loop) edges in the edge vector. Default FALSE.

seed

Optional integer for reproducibility.

Value

An object of class net_casedrop_reliability (single network) or net_casedrop_reliability_group (grouped htna). See Nestimate::casedrop_reliability() for the full component list and the corresponding plot() method.

Details

Works on htna networks and grouped htna networks directly.

Suffixed _htna to avoid clashing with Nestimate::casedrop_reliability() when both packages are loaded.

See also

reliability_htna(), centrality_stability_htna(), bootstrap_htna().

Examples

# \donttest{
data(human_ai)
net <- build_htna(human_ai, actor_type = "actor_type")
#> Warning: A network with one long sequence is not recommended and can't be validated using bootstrap and other confirmatory testings.
#> Metadata aggregated per session: ties resolved by first occurrence in 'session_date' (1 sessions), 'cluster' (42 sessions), 'actor_type' (24 sessions)
casedrop_reliability_htna(net, iter = 20, seed = 1)
#> Edge-weight Case-dropping Stability
#>   Cases (rows of $data) : 429
#>   Edges assessed        : 132 (diagonal excluded)
#>   Iterations / prop     : 20
#>   Correlation method    : spearman
#>   CS-coefficient (r)    : 0.90  (threshold=0.70, certainty=0.95)
#> 
#> Model-level reliability across iterations (mean +/- sd per drop):
#>   drop_prop      p=0.1        p=0.2        p=0.3        p=0.4        p=0.5        p=0.6        p=0.7        p=0.8        p=0.9      
#>   mean|diff|      0.002+- 0.000   0.003+- 0.000   0.004+- 0.000   0.005+- 0.001   0.007+- 0.001   0.008+- 0.001   0.010+- 0.001   0.013+- 0.002   0.019+- 0.003
#>   MAD             0.001+- 0.000   0.002+- 0.000   0.003+- 0.000   0.003+- 0.000   0.004+- 0.000   0.005+- 0.001   0.006+- 0.001   0.008+- 0.001   0.012+- 0.002
#>   cor             0.998+- 0.001   0.997+- 0.001   0.995+- 0.001   0.992+- 0.002   0.989+- 0.003   0.984+- 0.004   0.976+- 0.005   0.962+- 0.007   0.926+- 0.020
#>   max|diff|       0.016+- 0.006   0.024+- 0.008   0.028+- 0.009   0.039+- 0.017   0.052+- 0.016   0.059+- 0.018   0.076+- 0.021   0.101+- 0.039   0.118+- 0.028
# }