ggRandomForests (version 1.1.1)

airq_vs: Minimal depth variable selection object (randomForestSRC::var.select).

Description

A cached object from randomForestSRC::var.select function for the New York Air Quality Measurements randomForestSRC regression forest airq_rf.

format

A var.select object for a regression random forest

Details

For ggRandomForest testing and the R CMD checks, we cache the computationally expensive parts of running a randomForest.

We build a regression randomForest (airq_rf) with the airquality measurements data, then run the randomForestSRC::var.select function to determine minimal depth variable selection.

This "data set" is a cache of the var.select function, which runs the minimal depth variable selection method from the airq_rf random forest model.

The data were from New York, from May to September 1973. The data was obtained from the New York State Department of Conservation (ozone data) and the National Weather Service (meteorological data).

References

Chambers, J. M., Cleveland, W. S., Kleiner, B. and Tukey, P. A. (1983) Graphical Methods for Data Analysis. Belmont, CA: Wadsworth.

Ishwaran H. and Kogalur U.B. (2014). Random Forests for Survival, Regression and Classification (RF-SRC), R package version 1.5.4.

Ishwaran H. and Kogalur U.B. (2007). Random survival forests for R. R News 7(2), 25-31.

Ishwaran H., Kogalur U.B., Blackstone E.H. and Lauer M.S. (2008). Random survival forests. Ann. Appl. Statist. 2(3), 841-860.

See Also

airquality var.select rfsrc airq_rf gg_minimal_depth plot.gg_minimal_depth

Examples

## The data was built with the following command
airq_rf <- rfsrc(Ozone ~ ., data = airquality, na.action = "na.impute")
airq_vs <- var.select(airq_rf)

gg_dta <- gg_minimal_depth(airq_vs)
plot(gg_dta)