Variable Importance (VIMP) data object

gg_vimp Extracts the variable importance (VIMP) information from a a rfsrc object.

Usage

gg_vimp(object, nvar, ...)

Arguments

object

A rfsrc object or output from vimp

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.

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)
#> Warning: All aesthetics have length 1, but the data has 16 rows.
#> ℹ Please consider using `annotate()` or provide this layer with data containing
#>   a single row.


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

## -------- air quality data
rfsrc_airq <- rfsrc(Ozone ~ ., airquality,
  importance = TRUE
)
gg_dta <- gg_vimp(rfsrc_airq)
plot(gg_dta)
#> Warning: All aesthetics have length 1, but the data has 5 rows.
#> ℹ Please consider using `annotate()` or provide this layer with data containing
#>   a single row.



## -------- Boston data
data(Boston, package = "MASS")
rfsrc_boston <- randomForestSRC::rfsrc(medv ~ ., Boston,
  importance = TRUE
)
gg_dta <- gg_vimp(rfsrc_boston)
plot(gg_dta)
#> Warning: All aesthetics have length 1, but the data has 13 rows.
#> ℹ Please consider using `annotate()` or provide this layer with data containing
#>   a single row.


## -------- Boston data
rf_boston <- randomForest::randomForest(medv ~ ., Boston)
gg_dta <- gg_vimp(rf_boston)
plot(gg_dta)
#> Warning: All aesthetics have length 1, but the data has 13 rows.
#> ℹ Please consider using `annotate()` or provide this layer with data containing
#>   a single row.



## -------- mtcars data
rfsrc_mtcars <- rfsrc(mpg ~ .,
  data = mtcars,
  importance = TRUE
)
gg_dta <- gg_vimp(rfsrc_mtcars)
plot(gg_dta)
#> Warning: All aesthetics have length 1, but the data has 10 rows.
#> ℹ Please consider using `annotate()` or provide this layer with data containing
#>   a single row.


## ------------------------------------------------------------
## 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)
#> Warning: All aesthetics have length 1, but the data has 6 rows.
#> ℹ Please consider using `annotate()` or provide this layer with data containing
#>   a single row.


## -------- pbc data
# We need to create this dataset
data(pbc, package = "randomForestSRC", )
#> Warning: data set ‘’ not found
# 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)
#> Warning: All aesthetics have length 1, but the data has 17 rows.
#> ℹ Please consider using `annotate()` or provide this layer with data containing
#>   a single row.


# Restrict to only the top 10.
gg_dta <- gg_vimp(rfsrc_pbc, nvar = 10)
plot(gg_dta)
#> Warning: All aesthetics have length 1, but the data has 10 rows.
#> ℹ Please consider using `annotate()` or provide this layer with data containing
#>   a single row.