gg_vimp: Variable Importance (VIMP) data object

Description

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

Usage

gg_vimp(object, nvar, ...)

Value

gg_vimp object. A data.frame of VIMP measures, in rank order.

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.

References

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

Examples

Run this code

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

## ------------------------------------------------------------
## regression example
## ------------------------------------------------------------
if (FALSE) {
## -------- 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)

if (FALSE) {
## -------- mtcars data
rfsrc_mtcars <- rfsrc(mpg ~ ., data = mtcars,
                      importance = TRUE)
gg_dta <- gg_vimp(rfsrc_mtcars)
plot(gg_dta)
}
## ------------------------------------------------------------
## survival example
## ------------------------------------------------------------
if (FALSE) {
## -------- 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)
}