ranktreeEnsemble (version 0.23)

importance: Variable Importance Index for Each Predictor

Description

The function computes variable importance for each predictor from a rank-based random forests model or boosting model. A higher value indicates a more important predictor. The random forest implementation was performed via the function vimp directly imported from the randomForestSRC package. Use the command package?randomForestSRC for more information. The boosting implementation was performed via the function relative.influence directly imported from the gbm package. For technical details, see the vignette: utils::browseVignettes("gbm").

Usage

importance(object, ...)

Value

For the boosting model, a vector of variable importance values is given. For the random forest model, a matrix of variable importance values is given for the variable importance index for all the class labels, followed by the index for each class label.

Arguments

object

An object of class rfsrc generated from the function rforest or gbm generated from the function rboost.

...

Further arguments passed to or from other methods.

Author

Ruijie Yin (Maintainer,<[e-mail]>), Chen Ye and Min Lu

References

Lu M. Yin R. and Chen X.S. Ensemble Methods of Rank-Based Trees for Single Sample Classification with Gene Expression Profiles. Journal of Translational Medicine. 22, 140 (2024). doi: 10.1186/s12967-024-04940-2

Examples

# \donttest{
data(tnbc)
######################################################
# Random Forest
######################################################
obj <- rforest(subtype~., data = tnbc[,c(1:10,337)])
importance(obj)
######################################################
# Boosting
######################################################
obj <- rboost(subtype~., data = tnbc[,c(1:10,337)])
importance(obj)
# }