Benchmarking imputation methods with makeMissing() and evaluation()

VIM ships the two pieces a fair imputation benchmark needs: makeMissing() generates missingness with a known mechanism in complete data, and evaluation()/nrmse() score imputations against the withheld truth. This vignette wires them into a small but complete benchmark harness. The chunks below were run when the vignette was precomputed (vignettes/precompute.R in the source repository; the code is shown unchanged and runs as is) with

Three replications only make a smoke test, not evidence: rerun with NREP <- 200 (identical code) for stable rankings; that is the setting used for the accompanying paper.

Data and scenarios

The tao data (Tropical Atmosphere Ocean project) offer strongly correlated real measurements — exactly the structure conditional imputation can exploit.

library(VIM)
set.seed(2026)

data(tao, package = "VIM")
vars  <- c("Sea.Surface.Temp", "Air.Temp", "Humidity", "UWind", "VWind")
full  <- na.omit(tao[, vars])
targets <- c("Sea.Surface.Temp", "Air.Temp", "Humidity")
nrow(full)
#> [1] 565

Each replication draws N rows, amputes 20% of the three target variables under MCAR or MAR (missingness driven by the observed wind variables), lets every method impute, and scores the per-variable NRMSE on the amputed cells.

The harness

Each method is one function data.frame -> data.frame; adding a competitor is one more list entry.

methods <- list(
  "VIM ranger" = function(d) {
    vimpute(d, spec = list(.default = vs_ranger(num.trees = 100)),
            sequential = FALSE, imp_var = FALSE, verbose = FALSE)
  },
  "VIM robust" = function(d) {
    suppressWarnings(
      vimpute(d, method = "robust", sequential = FALSE,
              imp_var = FALSE, verbose = FALSE))
  },
  "VIM kNN" = function(d) kNN(d, k = 5, imp_var = FALSE)
)
if (has_mice) {
  methods[["mice pmm"]] <- function(d) {
    mice::complete(mice::mice(d, m = 1, maxit = 5, printFlag = FALSE))
  }
}
if (has_missRanger) {
  methods[["missRanger"]] <- function(d) {
    missRanger::missRanger(d, num.trees = 100, verbose = 0)
  }
}

score_run <- function(truth, amputed, imputed) {
  w <- attr(amputed, "where")
  vapply(targets, function(v) {
    nrmse(x = truth[[v]], y = imputed[[v]], m = w[, v])
  }, numeric(1))
}

run_benchmark <- function(mechanism) {
  out <- list()
  for (r in seq_len(NREP)) {
    truth <- full[sample(nrow(full), N), ]
    amp <- makeMissing(truth, prop = 0.2, mechanism = mechanism,
                       vars = targets, seed = 1000 + r)
    for (mth in names(methods)) {
      t0 <- proc.time()[["elapsed"]]
      imp <- as.data.frame(methods[[mth]](amp))
      secs <- proc.time()[["elapsed"]] - t0
      out[[length(out) + 1L]] <- data.frame(
        mechanism = mechanism, rep = r, method = mth,
        nrmse = mean(score_run(truth, amp, imp)), seconds = secs)
    }
  }
  do.call(rbind, out)
}

Results

res <- rbind(run_benchmark("MCAR"), run_benchmark("MAR"))

summary_tab <- aggregate(cbind(nrmse, seconds) ~ method + mechanism,
                         data = res, FUN = mean)
summary_tab <- summary_tab[order(summary_tab$mechanism, summary_tab$nrmse), ]
knitr::kable(summary_tab, digits = 3, row.names = FALSE,
             caption = sprintf("Mean NRMSE over the amputed cells and mean runtime (seconds), %d replications -- demo scale.", NREP))

Mean NRMSE over the amputed cells and mean runtime (seconds), 3 replications – demo scale.
method	mechanism	nrmse	seconds
missRanger	MAR	0.723	0.078
VIM kNN	MAR	0.730	0.036
VIM ranger	MAR	0.803	0.295
mice pmm	MAR	0.840	0.017
VIM robust	MAR	0.855	0.290
VIM kNN	MCAR	0.530	0.038
missRanger	MCAR	0.566	0.083
mice pmm	MCAR	0.762	0.023
VIM ranger	MCAR	0.773	0.327
VIM robust	MCAR	0.815	0.312

mar <- summary_tab[summary_tab$mechanism == "MAR", ]
dotchart(rev(mar$nrmse), labels = rev(mar$method), pch = 19,
         xlab = "mean NRMSE (MAR, lower is better)")

plot of chunk mar-dotchart

At this demo scale the ordering is indicative only; with NREP <- 200 the Monte-Carlo error of the means becomes negligible and the same code produces publication-grade comparisons. Other packages drop in the same way — e.g. a mixgb entry (mixgb::mixgb(d, m = 1)), when that package is installed.

Notes on fairness

All methods see identical amputed datasets (seed in makeMissing()).
NRMSE is computed on the amputed cells only, against the withheld truth, and is scale-free (RMSE divided by the standard deviation of the true values), so it can be averaged over variables.
Point-prediction accuracy is one criterion; it systematically favours conditional-mean methods over draw-based ones. For inference the relevant yardstick is the coverage of pooled confidence intervals – see the companion vignette Validating multiple-imputation properness.
overimpute() provides the complementary calibration check on real data where no truth is available.

Benchmarking imputation methods with makeMissing() and evaluation()

Matthias Templ

2026-08-28

Data and scenarios

The harness

Results

Notes on fairness