1. A method for predicting a future response from a large set of dyadic data, the method executable by a computer having a processor and memory, the method comprising: measuring, with the computer, a dyadic response variable associated with a dyad from two different sets of data that define a dyadic space; measuring, with the computer, a vector of covariates that captures the characteristics of the dyad; and modeling, with the computer, a predictive response based on the measurements to more accurately predict future dyadic interaction based on the two different sets of data by: (a) casting the dyadic response variable as a matrix including pre-specified covariates from the vector of covariates; (b) detrending the response variable by performing a co-clustering, or regressing, on the pre-specified covariates to generate residuals that are unexplained; (c) when the response variable is detrended by regression in (b), extracting from the residuals in (b) latent characteristics that induce local structures in the dyadic space by conducting co-clustering on the residuals to determine co-cluster means, else regressing residuals in (b) on the pre-specified covariates: and (d) iteratively performing steps (b) and (c) until arriving at a predictive response that is a composition of optimal regression and co-clusterinq that adequately explains the local structures.