randomForestSRC (version 2.9.3)

holdout.vimp.rfsrc: Hold out variable importance (VIMP)

Description

Hold out VIMP is calculated from the error rate for trees grown with and without a variable. Applies to all families.

Usage

# S3 method for rfsrc
holdout.vimp(formula, data,
  ntree = function(p, vtry){1000 * p / vtry},
  ntree.max = 2000,
  ntree.allvars = NULL,
  nsplit = 10,
  ntime = 50,
  mtry = NULL,
  vtry = 1,
  fast = FALSE,
  verbose = TRUE, 
  ...)

Arguments

formula

A symbolic description of the model to be fit.

data

Data frame containing the y-outcome and x-variables.

ntree

Function specifying requested number of trees used for growing the forest. Inputs are dimension and number of holdout variables. The requested number of trees can also be a number.

ntree.max

Maximum number of trees used when calculating prediction error for determing hold out VIMP.

ntree.allvars

Grow this many additional trees and use them for calculating the baseline error rate. Ignored if NULL.

nsplit

Non-negative integer value specifying number of random split points used to split a node (deterministic splitting corresponds to the value zero and is much slower).

ntime

Integer value used for survival to constrain ensemble calculations to a grid of ntime time points.

mtry

Number of variables randomly selected as candidates for splitting a node.

vtry

Number of variables randomly selected to be held out when growning a tree.

fast

Use fast random forests, rfsrc.fast, in place of rfsrc? Improves speed but is less accurate.

verbose

Provide verbose output?

...

Further arguments to be passed to rfsrc.

Value

Hold out VIMP for each variable. For multivariate forests, hold out VIMP is calculated for each of the target outcomes.

Details

Prior to growing a tree, a random set of vtry features are held out. Tree growing proceeds as usual with the remaining features. Once the forest is grown, hold out VIMP for a given variable v is calculated as follows. Gather all trees where v was held out and calculate OOB prediction error. Next gather all trees were v was not held out and calculate OOB prediction error. Hold out VIMP for v is the difference between these two values. Thus hold out VIMP measures the importance of a variable when that variable is truly removed from tree growing.

If ntree.allvars is set to an integer value, then a total of this many trees are grown using all variables. The above procedure is then implemented with the following change. Determine the error rate for these additional trees. Hold out VIMP for v is the difference between this value and the error rate for trees where v was held out. Unlike the above procedure, this makes sure that the baseline used for calculating holdout VIMP is the same for all v. This feature is probably most useful in low-dimensional settings.

Note that accuracy of hold out VIMP depends heavily on the size of the forest. If the number of trees is too small, then number of times a variable is held out will be small and OOB error may suffer from high variance. Thus, ntree should be set fairly high - we recommend using 1000 times the number of features. Increasing vtry is another way to increase number of hold out trees. In particular, number of trees needed should decrease linearly with vtry. For this reason the default ntree equals 1000 trees for each feature divided by vtry. Keep in mind that intrepretation of holdout VIMP is altered when vtry is different than 1.

References

Ishwaran H. (2019). Holdout variable importance for random forest models.

Lu M. and Ishwaran H. (2018). Expert Opinion: A prediction-based alternative to p-values in regression models. J. Thoracic and Cardiovascular Surgery, 155(3), 1130--1136.

See Also

vimp.rfsrc

Examples

# NOT RUN {
## ------------------------------------------------------------
## boston housing example
## ------------------------------------------------------------

if (library("mlbench", logical.return = TRUE)) {

  data(BostonHousing)
  hv <- holdout.vimp(medv ~ ., BostonHousing)
  print(hv)

}

## ------------------------------------------------------------
## iris example illustrating vtry
## ------------------------------------------------------------

print(100 * holdout.vimp(Species ~ ., iris))
print(100 * holdout.vimp(Species ~ ., iris, vtry=2))

## ------------------------------------------------------------
## example involving class imbalanced data
## illustrates the new RFQ classifier
## see the function "imbalanced" for more information about RFQ
## ------------------------------------------------------------

data(breast, package = "randomForestSRC")
breast <- na.omit(breast)
f <- as.formula(status ~ .)
hv <- holdout.vimp(f, breast, rfq=TRUE, perf.type="g.mean")
print(10 * hv)


## ------------------------------------------------------------
## multivariate regression analysis example
## ------------------------------------------------------------

print(holdout.vimp(cbind(mpg, cyl) ~., mtcars))

## ------------------------------------------------------------
## white wine classification example
## ------------------------------------------------------------

data(wine, package = "randomForestSRC")
wine$quality <- factor(wine$quality)
hv <- holdout.vimp(quality ~ ., wine, vtry = 5)
print(100 * hv)


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

data(pbc, package = "randomForestSRC")
hv <- holdout.vimp(Surv(days, status) ~ ., pbc, splitrule = "random")
print(100 * hv)

## ------------------------------------------------------------
## WIHS competing risk example
## ------------------------------------------------------------

data(wihs, package = "randomForestSRC")
hv <- holdout.vimp(Surv(time, status) ~ ., wihs, ntree = 1000)
print(100 * hv)

# }