Run differential correlation analysis for all interacting metabolites and functions.

Can either take continuous or categorical data. Typically, the main anansi() function will run this for you.

Usage

anansiDiffCor(web, sat_model, errorterm, int.terms, metadata, verbose)

web: An AnansiWeb object, containing two tables with omics data and a dictionary that links them. See weaveWebFromTables() for how to weave a web.
sat_model: A formula object, containing the full model
errorterm: A character vector, containing the metadata col.name denoting repeated measures.
int.terms: A character vector, containing the metadata col.names denoting covariates interacting with X to be tested for differential associations.
metadata: A vector or data.frame of categorical or continuous value necessary for differential correlations. Often a state or treatment score. If no argument provided, anansi will let you know and still to regular correlations according to your dictionary.
verbose: A boolean. Toggles whether to print diagnostic information while running. Useful for debugging errors on large datasets.

a list of anansiTale result objects, one for the total model, one for emergent correlations and one for disjointed correlations.