ggRandomForests (version 1.1.2)

partial_coplot_data: Cached randomForestSRC::plot.variable objects for examples, diagnostics and vignettes.

Description

Data sets storing randomForestSRC::rfsrc objects corresponding to training data according to the following naming convention:

• partial_coplot_Boston- randomForestS[R]C for theBostonhousing data set (MASSpackage).

format

List of randomForestSRC::plot.variable objects

Details

Constructing random forests are computationally expensive. We cache randomForestSRC::rfsrc objects to improve the ggRandomForests examples, diagnostics and vignettes run times. (see rebuild_cache_datasets to rebuild a complete set of these data sets.)

For each data set listed, we build a randomForestSRC::rfsrc. Tuning parameters used in each case are documented in the examples. Each data set is built with the rebuild_cache_datasets with the randomForestSRC version listed in the ggRandomForests DESCRIPTION file.

• partial_coplot_Boston- TheBostonhousing values in suburbs of Boston from theMASSpackage. Build a regression random forest for predicting medv (median home values) on 13 covariates and 506 observations.

References

#--------------------- randomForestSRC ---------------------

Ishwaran H. and Kogalur U.B. (2014). Random Forests for Survival, Regression and Classification (RF-SRC), R package version 1.5.5.

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.

#--------------------- Boston data set ---------------------

Belsley, D.A., E. Kuh, and R.E. Welsch. 1980. Regression Diagnostics. Identifying Influential Data and Sources of Collinearity. New York: Wiley.

Harrison, D., and D.L. Rubinfeld. 1978. "Hedonic Prices and the Demand for Clean Air." J. Environ. Economics and Management 5: 81-102.

See Also

MASS::Boston randomForestSRC::plot.variable rebuild_cache_datasets

Examples

#---------------------------------------------------------------------
# MASS::Boston data - regression random forest
#---------------------------------------------------------------------
data(Boston_rfsrc, package="ggRandomForests")

# Cut the codependent variable
rm_pts <- cut_distribution(rfsrc_Boston$xvar$rm, groups=6)
rm_grp <- cut(rfsrc_Boston$xvar$rm, breaks=rm_pts)

# plot.variable for lstat on subsets of rm (this will take some time.)
 partial_coplot_Boston <- gg_partial_coplot(rfsrc_Boston, xvar="lstat",
                                            groups=rm_grp,
                                            show.plots=FALSE)