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Compute community scores from a fitted MixMashNet model

Source: R/community_scores.R
community_scores.Rd

Computes subject-level community scores. Community scores are obtained as weighted sums of the variables belonging to each detected community, where weights correspond to the standardized community loadings estimated via EGAnet::net.loads and stored in the fitted mixMN_fit object. Scores are computed using the dataset provided via the data argument. If data = NULL, the original dataset used to fit the model (fit$model$data) is used by default. Optionally, percentile bootstrap quantile regions for the community scores can be computed if bootstrap community loadings are available in fit$community_loadings$boot. Community scores are only available if community loadings were computed in the fitted model. This requires that all variables in the community subgraph are of MGM type Gaussian ("g"), Poisson ("p"), or binary categorical ("c" with level == 2).

Usage

community_scores(
  fit,
  data = NULL,
  layer = NULL,
  scale = TRUE,
  quantile_level = NULL,
  return_quantile_region = FALSE,
  na_action = c("stop", "omit")
)

Arguments

fit

A fitted object of class c("mixmashnet","mixMN_fit", "multimixMN_fit") returned by mixMN() or multimixMN().

data

Optional data.frame with variables in columns. If NULL, uses fit$model$data. Errors if both are NULL.

layer

Optional. If fit is a multimixMN_fit, specify which layer to score (name or index). If NULL, scores are computed for all layers and returned as a named list.

scale

Logical; if TRUE (default), z-standardize variables used for scoring, using the mean/SD computed from the dataset used for scoring.

quantile_level

Optional numeric from 0 to 1, e.g. 0.95 or 0.99. If provided, percentile bootstrap quantile regions are computed for community scores (requires fit$community_loadings$boot).

return_quantile_region

Logical; if TRUE, return quantile regions.

na_action

Character. How to handle missing values in the scoring data: "stop" (default) stops if any missing value is present in the required variables; "omit" computes scores using row-wise omission within each community (i.e., uses available variables only, re-normalizing weights within community for that row).

Value

A list with class c("mixmashnet","community_scores") containing:

call

The matched call.

settings

List with scale, quantile_level, and na_action.

ids

Character vector of subject IDs (rownames of data).

communities

Character vector of community score names.

scores

Numeric matrix of scores (n × K).

quantile_region

If requested and available, a list with lower and upper matrices (n × K) for percentile bootstrap quantile regions; otherwise NULL.

details

List containing nodes_used, loadings_true, loadings_boot_available, and scaling parameters (center, scale).

If fit is a mixMN_fit (or a multimixMN_fit with layer specified), returns a c("mixmashnet","community_scores") object. If fit is a multimixMN_fit and layer = NULL, returns a named list of community_scores objects (one per layer).

Details

The function requires that fit$community_loadings$true exists and that the input data contains all required variables in fit$community_loadings$nodes. It errors otherwise.

References

Christensen, A. P., Golino, H., Abad, F. J., & Garrido, L. E. (2025). Revised network loadings. Behavior Research Methods, 57(4), 114. doi:10.3758/s13428-025-02640-3