MI (predictive mean matching)

Mass imputation by predictive mean matching: a regression y ~ x1 + x2 is fit on the non-probability sample, donors are chosen by closeness in either predicted-y (pmm_match_type = 1) or observed-y (pmm_match_type = 2) space, and the donor outcomes are aggregated over the probability sample.

nonprobsvy version: 0.3.0  |  R: 4.6.0  |  run: 2026-05-24 08:08:18  |  commit: 4b2ba9a

script pmm_match_type var_method rep_type num_boot alpha n_reps bias rmse mc_se mean_se coverage ci_width
mi_pmm 1 analytic NA NA 0.05 500 0.011 0.084 0.004 0.054 0.794 0.213
mi_pmm 2 analytic NA NA 0.05 500 0.005 0.079 0.004 0.051 0.802 0.199
mi_pmm_boot 1 bootstrap subbootstrap 50 0.05 100 0.006 0.080 0.008 0.052 0.780 0.202
mi_pmm_boot 2 bootstrap subbootstrap 50 0.05 100 0.003 0.074 0.007 0.046 0.780 0.180

Notes

  • DGP: default.
  • Analytical SE typically under-states the variance for PMM for the same reason as NN imputation — donor selection variability is unmodelled.
  • Sweeps pmm_match_type ∈ {1, 2} at k = 5.