Calculate variable importance (VIMP) for a single variable or group of variables for training or test data.
# S3 method for rfsrc
vimp(object, xvar.names, m.target = NULL,
importance = c("permute", "random", "anti"), block.size = 10,
joint = FALSE, subset, seed = NULL, do.trace = FALSE, ...)An object of class (rfsrc, grow) or
(rfsrc, forest). Requires forest=TRUE in the
original rfsrc call.
Names of the x-variables to be used. If not specified all variables are used.
Character value for multivariate families specifying the target outcome to be used. If left unspecified, the algorithm will choose a default target.
Type of VIMP.
Specifies number of trees in a block when calculating VIMP.
Individual or joint VIMP?
Vector indicating which rows of the grow data to
restrict VIMP calculations to; i.e. this option yields VIMP which is
restricted to a specific subset of the data. Note that the vector
should correspond to the rows of object$xvar and not the
original data passed in the grow call. All rows used if not
specified.
Negative integer specifying seed for the random number generator.
Number of seconds between updates to the user on approximate time to completion.
Further arguments passed to or from other methods.
An object of class (rfsrc, predict) containing importance
values.
Using a previously grown forest, calculate the VIMP for variables
xvar.names. By default, VIMP is calculated for the original
data, but the user can specify a new test data for the VIMP
calculation using newdata. See rfsrc for more
details about how VIMP is calculated.
Joint VIMP is requested using joint and equals importance for a group of variables when the group is perturbed simultaneously.
Ishwaran H. (2007). Variable importance in binary regression trees and forests, Electronic J. Statist., 1:519-537.
# NOT RUN {
## ------------------------------------------------------------
## classification example
## showcase different vimp
## ------------------------------------------------------------
iris.obj <- rfsrc(Species ~ ., data = iris)
# Permutation vimp
print(vimp(iris.obj)$importance)
# Random daughter vimp
print(vimp(iris.obj, importance = "random")$importance)
# Joint permutation vimp
print(vimp(iris.obj, joint = TRUE)$importance)
# Paired vimp
print(vimp(iris.obj, c("Petal.Length", "Petal.Width"), joint = TRUE)$importance)
print(vimp(iris.obj, c("Sepal.Length", "Petal.Width"), joint = TRUE)$importance)
## ------------------------------------------------------------
## regression example
## ------------------------------------------------------------
airq.obj <- rfsrc(Ozone ~ ., airquality)
print(vimp(airq.obj))
## ------------------------------------------------------------
## regression example where vimp is calculated on test data
## ------------------------------------------------------------
set.seed(100080)
train <- sample(1:nrow(airquality), size = 80)
airq.obj <- rfsrc(Ozone~., airquality[train, ])
#training data vimp
print(airq.obj$importance)
print(vimp(airq.obj)$importance)
#test data vimp
print(vimp(airq.obj, newdata = airquality[-train, ])$importance)
## ------------------------------------------------------------
## survival example
## study how vimp depends on tree imputation
## makes use of the subset option
## ------------------------------------------------------------
data(pbc, package = "randomForestSRC")
# determine which records have missing values
which.na <- apply(pbc, 1, function(x){any(is.na(x))})
# impute the data using na.action = "na.impute"
pbc.obj <- rfsrc(Surv(days,status) ~ ., pbc, nsplit = 3,
na.action = "na.impute", nimpute = 1)
# compare vimp based on records with no missing values
# to those that have missing values
# note the option na.action="na.impute" in the vimp() call
vimp.not.na <- vimp(pbc.obj, subset = !which.na, na.action = "na.impute")$importance
vimp.na <- vimp(pbc.obj, subset = which.na, na.action = "na.impute")$importance
print(data.frame(vimp.not.na, vimp.na))
# }
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