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Split-half stability of inter-muscle coordination networks

Source: R/emg-network.R
splitHalfStability.Rd

Tests whether an inter-muscle coordination network is real signal or a split-specific artefact: the recording is split into two independent halves, the same network is built on each with coordinationNetwork(), and the edge weights (upper triangle) and node strengths (row sums) are correlated between the halves. A correlation near 1 means each half rebuilds the same network. Different estimators trade sensitivity for specificity differently, and a sparse graph's node strengths are more split-sensitive than its edges, so build several and trust what reproduces in BOTH edges and topology.

Usage

splitHalfStability(
  x,
  methods = c("coherence", "partial_coherence", "wpli", "directed_gc"),
  freq_band = NULL,
  channels = NULL,
  nperseg = 256L,
  noverlap = NULL,
  max_lag = 10L,
  debiased = FALSE,
  assay_name = NULL
)

Arguments

x

A PhysioExperiment holding a multi-channel EMG assay.

methods

Estimators to test (default: all four; see coordinationNetwork()).

freq_band, channels, nperseg, noverlap, max_lag, debiased, assay_name

Passed to coordinationNetwork() for each half.

Value

A data frame (class split_half_stability), one row per method, with method, split_half_edge_correlation, split_half_node_strength_correlation, first_half_mean_edge, and second_half_mean_edge.

See also

coordinationNetwork(), emgCoordinationStructure()

Examples

pe <- make_emg(n_time = 2000, n_channels = 4, sr = 1000)
splitHalfStability(pe, methods = "coherence", freq_band = c(20, 150))
#>      method split_half_edge_correlation split_half_node_strength_correlation
#> 1 coherence                   0.4699161                            0.5115727
#>   first_half_mean_edge second_half_mean_edge
#> 1            0.4870532             0.3375243