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.

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

Examples

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

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

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


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

## -------- Boston data
rf_boston <- randomForest::randomForest(medv ~ ., Boston)
gg_dta <- gg_vimp(rf_boston)
plot(gg_dta)


## -------- mtcars data
rfsrc_mtcars <- 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 <- 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 May 2, 2026, 5:06 p.m.