| spvim_ics {vimp} | R Documentation |
Influence function estimates for SPVIMs
Description
Compute the influence functions for the contribution from sampling observations and subsets.
Usage
spvim_ics(Z, z_counts, W, v, psi, G, c_n, ics, measure)
Arguments
Z |
the matrix of presence/absence of each feature (columns) in each sampled subset (rows) |
z_counts |
the number of times each unique subset was sampled |
W |
the matrix of weights |
v |
the estimated predictiveness measures |
psi |
the estimated SPVIM values |
G |
the constraint matrix |
c_n |
the constraint values |
ics |
a list of influence function values for each predictiveness measure |
measure |
the type of measure (e.g., "r_squared" or "auc") |
Details
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).
Value
a named list of length 2; contrib_v is the contribution from estimating V, while contrib_s is the contribution from sampling subsets.