importance.varpro: Calculate VarPro Importance

In kogalur/varPro: Model-Independent Variable Selection via the Rule-Based Variable Priority (VarPro)

Calculate VarPro Importance

Description

Calculates variable importance using results from previous varpro call.

Usage

## S3 method for class 'varpro'
importance(o, local.std = TRUE, y.external = NULL,
  cutoff = 0.79, trim = 0.1, plot.it = FALSE, conf = TRUE, sort = TRUE,
  ylab = if (conf) "Importance" else "Standardized Importance",
  max.rules.tree, max.tree,
  papply = mclapply,
  ...)

Arguments

o	varpro object returned from a previous call to varpro.
local.std	Logical. If TRUE, uses locally standardized importance values.
y.external	Optional user-supplied response vector. Must match the expected dimension and outcome family.
cutoff	Threshold used to highlight significant variables in the importance plot. Applies only when plot.it = TRUE.
trim	Windsorization trim value used to robustify the mean and standard deviation calculations.
plot.it	Logical. If TRUE, generates a plot of importance values.
conf	Logical. If TRUE, displays importance values with standard errors as a boxplot (providing an informal confidence region). If FALSE, plots standardized importance values.
sort	Logical. If TRUE, sorts results in decreasing order of importance.
ylab	Character string specifying the y-axis label.
max.rules.tree	Optional. Maximum number of rules per tree. Defaults to the value stored in the varpro object if unspecified.
max.tree	Optional. Maximum number of trees used for rule extraction. Defaults to the value from the varpro object if unspecified.
papply	Apply method for parallelization; typically mclapply or lapply.
...	Additional arguments passed to internal methods.

Details

Calculates standardized importance values for identifying and ranking variables. Optionally, graphical output is provided, including confidence-style boxplots.

Value

Invisibly, table summarizing the results. Contains mean importance 'mean', the standard deviation 'std', and standardized importance 'z'.

For classification, conditional 'z' tables are additionally provided, where the $z$ standardized importance values are conditional on the class label.

See cv.varpro for a data driven cross-validation method for selecting the cutoff value, cutoff.

Author(s)

Min Lu and Hemant Ishwaran

References

Lu, M. and Ishwaran, H., (2024). Model-independent variable selection via the rule-based variable priority. arXiv e-prints, pp.arXiv-2409.

See Also

cv.varpro varpro

Examples

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

data(BostonHousing, package = "mlbench")

## call varpro
o <- varpro(medv~., BostonHousing)

## extract importance values
imp <- importance(o)
print(imp)

## plot the results
imp <- importance(o, plot.it = TRUE)
print(imp)


## ------------------------------------------------------------
## illustrates y-external: regression example
## ------------------------------------------------------------

## friedman1 - standard application of varpro
d <- data.frame(mlbench:::mlbench.friedman1(250),noise=matrix(runif(250*10,-1,1),250))
o <- varpro(y~.,d)
print(importance(o))

## importance using external rf predictor
print(importance(o,y.external=randomForestSRC::rfsrc(y~.,d)$predicted.oob))

## importance using external lm predictor
print(importance(o,y.external=lm(y~.,d)$fitted))

## importance using external randomized predictor
print(importance(o,y.external=sample(o$y)))

## ------------------------------------------------------------
## illustrates y-external: classification example
## ------------------------------------------------------------

## iris - standard application of varpro
o <- varpro(Species~.,iris)
print(importance(o))

## importance using  external rf predictor
print(importance(o,y.external=randomForestSRC::rfsrc(Species~.,iris)$class.oob))

## importance using  external randomized predictor
print(importance(o,y.external=sample(o$y)))

## ------------------------------------------------------------
## illustrates y-external: survival
## ------------------------------------------------------------
data(pbc, package = "randomForestSRC")
o <- varpro(Surv(days, status)~., pbc)
print(importance(o))

## importance using  external rsf predictor
print(importance(o,y.external=randomForestSRC::rfsrc(Surv(days, status)~., pbc)$predicted.oob))

## importance using  external randomized predictor
print(importance(o,y.external=sample(o$y)))

kogalur/varPro documentation built on June 2, 2025, 6:24 a.m.