Compute the influence functions for the contribution from sampling observations and subsets.
spvim_ics(Z, z_counts, W, v, psi, G, c_n, ics, measure)the matrix of presence/absence of each feature (columns) in each sampled subset (rows)
the number of times each unique subset was sampled
the matrix of weights
the estimated predictiveness measures
the estimated SPVIM values
the constraint matrix
the constraint values
a list of influence function values for each predictiveness measure
the type of measure (e.g., "r_squared" or "auc")
a named list of length 2; contrib_v is the contribution from estimating V, while contrib_s is the contribution from sampling subsets.
The processes for sampling observations and sampling subsets are independent. Thus, we can compute the influence function separately for each sampling process. For further details, see the paper by Williamson and Feng (2020).