ggRandomForests (version 1.2.1)

varsel_data: Cached var.select objects for examples, diagnostics and vignettes. Data sets storing var.select objects corresponding to training data according to the following naming convention:

• varsel_iris- from a randomForestSR[C] for theirisdata set.
• varsel_Boston- from a randomForestS[R]C for theBostonhousing data set (MASSpackage).
• varsel_pbc- from a randomForest[S]RC for thepbcdata set (randomForestSRCpackage)

Description

Cached var.select objects for examples, diagnostics and vignettes. Data sets storing var.select objects corresponding to training data according to the following naming convention:

• varsel_iris- from a randomForestSR[C] for theirisdata set.
• varsel_Boston- from a randomForestS[R]C for theBostonhousing data set (MASSpackage).
• varsel_pbc- from a randomForest[S]RC for thepbcdata set (randomForestSRCpackage)

format

var.select object

Details

Constructing minimal depth variable selection with the randomForestsSRC::var.select function is computationally expensive. We cache var.select objects to improve the ggRandomForests examples, diagnostics and vignettes run times. (see rfsrc_cache_datasets to rebuild a complete set of these data sets.)

For each data set listed, we build a rfsrc (see rfsrc_data), then calculate the minimal depth variable selection with var.select function, setting method="md". Each data set is built with the rfsrc_cache_datasets with the randomForestSRC version listed in the ggRandomForests DESCRIPTION file.

• varsel_iris- The famous (Fisher's or Anderson's)irisdata set gives the measurements in centimeters of the variables sepal length and width and petal length and width, respectively, for 50 flowers from each of 3 species of iris. Build a classification random forest for predicting the species (setosa, versicolor, and virginica) on 5 variables (columns) and 150 observations (rows).
• varsel_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.
• varsel_pbc- Thepbcdata from the Mayo Clinic trial in primary biliary cirrhosis (PBC) of the liver conducted between 1974 and 1984. A total of 424 PBC patients, referred to Mayo Clinic during that ten-year interval, met eligibility criteria for the randomized placebo controlled trial of the drug D-penicillamine. 312 cases participated in the randomized trial and contain largely complete data. Data from therandomForestSRCpackage. Build a survival random forest for time-to-event death data with 17 covariates and 312 observations (remaining 106 observations are held out).

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.

#--------------------- Iris data set ---------------------

Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988) The New S Language. Wadsworth & Brooks/Cole. (has iris3 as iris.)

Fisher, R. A. (1936) The use of multiple measurements in taxonomic problems. Annals of Eugenics, 7, Part II, 179-188.

Anderson, Edgar (1935). The irises of the Gaspe Peninsula, Bulletin of the American Iris Society, 59, 2-5.

#--------------------- 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.

See Also

iris Boston pbc var.select rfsrc_data rfsrc_cache_datasets gg_minimal_depth plot.gg_minimal_depth gg_minimal_vimp plot.gg_minimal_vimp

Examples

#---------------------------------------------------------------------
# iris data - classification random forest
#---------------------------------------------------------------------
# load the rfsrc object from the cached data
data(rfsrc_iris, package="ggRandomForests")

# The var.select call
 varsel_iris <- var.select(rfsrc_iris)

# plot the forestminimal depth ranking
gg_dta <- gg_minimal_depth(varsel_iris)
plot(gg_dta)


#---------------------------------------------------------------------
# MASS::Boston data - regression random forest 
#---------------------------------------------------------------------
# load the rfsrc object from the cached data
data(rfsrc_Boston, package="ggRandomForests")

# The var.select call
varsel_Boston <- var.select(rfsrc_Boston)

# plot the forestminimal depth ranking
gg_dta <- gg_minimal_depth(varsel_Boston)
plot(gg_dta)

#---------------------------------------------------------------------
# randomForestSRC::pbc data - survival random forest
#---------------------------------------------------------------------
# load the rfsrc object from the cached data
data(rfsrc_pbc, package="ggRandomForests")

# The var.select call 
varsel_pbc <- var.select(rfsrc_pbc)
                                    
# plot the forestminimal depth ranking
gg_dta <- gg_minimal_depth(varsel_pbc)
plot(gg_dta)