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Permutation Test for Network Differences

Source: R/permutation_htna.R
permutation_htna.Rd

htna-named alias of Nestimate::permutation(). Tests whether observed edge-weight differences between two networks (or all pairwise differences within a netobject_group) exceed what would be expected under a null of identical generating processes.

Usage

permutation_htna(
  x,
  y = NULL,
  iter = 1000L,
  alpha = 0.05,
  paired = FALSE,
  adjust = "none",
  measures = NULL,
  nlambda = 50L,
  seed = NULL
)

Arguments

x

A netobject (from build_network) or a net_edge_betweenness object.

y

A netobject (from build_network) or a net_edge_betweenness object. Must use the same method and have the same nodes as x.

iter

Integer. Number of permutation iterations (default: 1000).

alpha

Numeric. Significance level (default: 0.05).

paired

Logical. If TRUE, permute within pairs (requires equal number of observations in x and y). Default: FALSE.

adjust

Character. p-value adjustment method passed to p.adjust (default: "none"). Common choices: "holm", "BH", "bonferroni".

measures

Character vector of centrality measures to permutation-test in addition to the edges, or "all" for every built-in measure. Default NULL (edges only). When supplied, the result gains a $centralities block matching the layout of tna::permutation_test(measures = ): per state and measure it reports the observed difference, an effect size (difference / SD of the permutation null), and a permutation p-value, all using the same permuted networks as the edge test. Not supported for net_edge_betweenness inputs.

nlambda

Integer. Number of lambda values for the glassopath regularisation path (only used when method = "glasso"). Higher values give finer lambda resolution at the cost of speed. Default: 50.

seed

Integer or NULL. RNG seed for reproducibility.

Value

An object of class net_permutation (single pair) or net_permutation_group (multiple pairs). See Nestimate::permutation() for the full slot list.

Details

Works on htna networks: the actor partition ($node_groups, $actor_levels, htna class) is preserved on result$x / result$y, so plot_htna_diff() can render the result with htna's colour and layout conventions.

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

See also

plot_htna_diff() to plot the result.

Examples

# \donttest{
data(human_ai)
grp <- build_htna(human_ai, actor_type = "actor_type", group = "phase")
#> 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 'cluster' (24 sessions), 'actor_type' (9 sessions)
#> 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' (18 sessions), 'actor_type' (15 sessions)
permutation_htna(grp$Early, grp$Late, iter = 50)
#> Permutation Test:Transition Network (relative probabilities) [directed]
#>   Iterations: 50  |  Alpha: 0.05
#>   Nodes: 12  |  Edges tested: 135  |  Significant: 24
# }