gg_vimp: Variable Importance (VIMP) data object

In ggRandomForests: Visually Exploring Random Forests

Variable Importance (VIMP) data object

Description

gg_vimp Extracts the variable importance (VIMP) information from a rfsrc or randomForest object and reshapes it into a tidy data set.

Usage

gg_vimp(object, nvar, ...)

Arguments

object	A rfsrc object, the output from vimp, or a fitted randomForest.
nvar	argument to control the number of variables included in the output.
...	arguments passed to the vimp.rfsrc function if the rfsrc object does not contain importance information.

Details

gg_vimp() reports whatever importance the forest stored; it computes nothing itself. Usually that is permutation (Breiman-Cutler) variable importance: the forest permutes a variable's observed values across the out-of-bag (OOB) cases, runs those perturbed cases down the already-grown trees, and measures how much the OOB prediction error climbs. That perturbation is synthetic (the variable's link to the response is broken on purpose) so a large increase means the variable was carrying genuine signal; near-zero or negative values mean it added noise or nothing at all.

A randomForest fit needs importance = TRUE to give you this. randomForest::randomForest() defaults to importance = FALSE, and that fit stores only IncNodePurity – a node-impurity (RSS or Gini) measure, which is not a permutation quantity and is not comparable to one. It is the only importance the forest kept, so it is what gg_vimp() reports, in the vimp column, same as any other. Nothing marks the difference in the plot. So gg_vimp(randomForest(y ~ ., data)) ranks by node purity; pass importance = TRUE and you get permutation VIMP (%IncMSE), and colnames(object$importance) tells you which one you have. randomForestSRC::rfsrc() has no such trap: its importance argument yields permutation VIMP.

When a randomForest fit carries both measures, gg_vimp() reports the permutation one and leaves node purity out of the ranking – the two run on different scales and mean different things, so putting them in one ordering would be meaningless. Read randomForest::importance(object) if you want both. A classification fit names that pair MeanDecreaseAccuracy and MeanDecreaseGini, and stores a permutation column per class besides. Those per-class columns are all permutation measures on one scale, so gg_vimp() keeps them together, names each in the set column, and drops only the Gini one.

gg_varpro() takes the opposite route, comparing local estimators on real observed data through varPro's release rules, with no permutation and no synthetic features. The two approaches answer "which variables matter?" by opposite mechanisms, so a variable can rank differently under each, and that disagreement is itself informative: it often signals interaction structure or non-monotone effects that one mechanism surfaces and the other obscures.

For survival forests, VIMP is measured against the ensemble cumulative hazard function (CHF); the error metric is one minus the concordance index (C-statistic). Variables with non-positive VIMP are flagged in the positive column and colored differently by plot.gg_vimp.

Value

gg_vimp object. A data.frame of VIMP measures, in rank order, optionally containing class-specific scores and a relative importance column. When randomForest objects lack stored importance values a warning is issued and NA placeholders are returned so plots remain reproducible.

References

Ishwaran H. (2007). Variable importance in binary regression trees and forests, Electronic J. Statist., 1:519-537.

See Also

plot.gg_vimp rfsrc

vimp gg_varpro

Examples

## ------------------------------------------------------------
## classification example
## ------------------------------------------------------------
## -------- iris data
rfsrc_iris <- randomForestSRC::rfsrc(Species ~ .,
  data = iris,
  importance = TRUE
)
gg_dta <- gg_vimp(rfsrc_iris)
plot(gg_dta)

## ------------------------------------------------------------
## regression example
## ------------------------------------------------------------

## -------- air quality data
rfsrc_airq <- randomForestSRC::rfsrc(Ozone ~ ., airquality,
  importance = TRUE
)
gg_dta <- gg_vimp(rfsrc_airq)
plot(gg_dta)


## -------- Boston data
if (requireNamespace("MASS", quietly = TRUE)) {
  data(Boston, package = "MASS")
  rfsrc_boston <- randomForestSRC::rfsrc(medv ~ ., Boston,
    importance = TRUE
  )
  gg_dta <- gg_vimp(rfsrc_boston)
  plot(gg_dta)

  ## importance = TRUE for permutation VIMP; without it randomForest stores
  ## only IncNodePurity, which is what you would be ranking (see Details).
  rf_boston <- randomForest::randomForest(medv ~ ., Boston,
    importance = TRUE
  )
  gg_dta <- gg_vimp(rf_boston)
  plot(gg_dta)
}


## -------- mtcars data
rfsrc_mtcars <- randomForestSRC::rfsrc(mpg ~ .,
  data = mtcars,
  importance = TRUE
)
gg_dta <- gg_vimp(rfsrc_mtcars)
plot(gg_dta)

## ------------------------------------------------------------
## survival example
## ------------------------------------------------------------

## -------- veteran data
data(veteran, package = "randomForestSRC")
rfsrc_veteran <- randomForestSRC::rfsrc(Surv(time, status) ~ .,
  data = veteran,
  ntree = 100,
  importance = TRUE
)

gg_dta <- gg_vimp(rfsrc_veteran)
plot(gg_dta)

## -------- pbc data
# We need to create this dataset
data(pbc, package = "randomForestSRC", )
# For whatever reason, the age variable is in days...
# makes no sense to me
for (ind in seq_len(dim(pbc)[2])) {
  if (!is.factor(pbc[, ind])) {
    if (length(unique(pbc[which(!is.na(pbc[, ind])), ind])) <= 2) {
      if (sum(range(pbc[, ind], na.rm = TRUE) == c(0, 1)) == 2) {
        pbc[, ind] <- as.logical(pbc[, ind])
      }
    }
  } else {
    if (length(unique(pbc[which(!is.na(pbc[, ind])), ind])) <= 2) {
      if (sum(sort(unique(pbc[, ind])) == c(0, 1)) == 2) {
        pbc[, ind] <- as.logical(pbc[, ind])
      }
      if (sum(sort(unique(pbc[, ind])) == c(FALSE, TRUE)) == 2) {
        pbc[, ind] <- as.logical(pbc[, ind])
      }
    }
  }
  if (!is.logical(pbc[, ind]) &
    length(unique(pbc[which(!is.na(pbc[, ind])), ind])) <= 5) {
    pbc[, ind] <- factor(pbc[, ind])
  }
}
# Convert age to years
pbc$age <- pbc$age / 364.24

pbc$years <- pbc$days / 364.24
pbc <- pbc[, -which(colnames(pbc) == "days")]
pbc$treatment <- as.numeric(pbc$treatment)
pbc$treatment[which(pbc$treatment == 1)] <- "DPCA"
pbc$treatment[which(pbc$treatment == 2)] <- "placebo"
pbc$treatment <- factor(pbc$treatment)
dta_train <- pbc[-which(is.na(pbc$treatment)), ]
# Create a test set from the remaining patients
pbc_test <- pbc[which(is.na(pbc$treatment)), ]

# ========
# build the forest:
rfsrc_pbc <- randomForestSRC::rfsrc(
  Surv(years, status) ~ .,
  dta_train,
  nsplit = 10,
  na.action = "na.impute",
  forest = TRUE,
  importance = TRUE,
  save.memory = TRUE
)

gg_dta <- gg_vimp(rfsrc_pbc)
plot(gg_dta)

# Restrict to only the top 10.
gg_dta <- gg_vimp(rfsrc_pbc, nvar = 10)
plot(gg_dta)

ggRandomForests documentation built on Aug. 4, 2026, 5:09 p.m.