partial_coplot_data: Cached 'plot.variable' objects for examples, diagnostics and...
In ehrlinger/ggRFVignette: Vignettes for ggRandomForests package

Description Format Details References See Also Examples

Cached plot.variable objects for examples, diagnostics and vignettes.

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

partial_coplot_Boston - randomForestS[R]C for the Boston housing data set (MASS package).

List of plot.variable objects

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

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

partial_coplot_Boston - The Boston housing values in suburbs of Boston from the MASS package. Build a regression random forest for predicting medv (median home values) on 13 covariates and 506 observations.

#——————— 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.

#——————— pbc data set ———————

Flemming T.R and Harrington D.P., (1991) Counting Processes and Survival Analysis. New York: Wiley.

T Therneau and P Grambsch (2000), Modeling Survival Data: Extending the Cox Model, Springer-Verlag, New York. ISBN: 0-387-98784-3.

Boston plot.variable cache_rfsrc_datasets

## Not run: 
#---------------------------------------------------------------------
# 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)
                                            

## End(Not run)