partial_data: Cached `plot.variable` objects for examples, diagnostics and vignettes. Data sets storing `plot.variable` objects corresponding to training data according to the following naming convention:
`partial_iris`- from a randomForestSR[C] for the`iris`data set.
`partial_Boston`- from a randomForestS[R]C for the`Boston`housing data set (`MASS`package).
`partial_pbc`- from a randomForest[S]RC for the`pbc`data set (`randomForestSRC`package)

Description

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

partial_iris- from a randomForestSR[C] for theirisdata set.
partial_Boston- from a randomForestS[R]C for theBostonhousing data set (MASSpackage).
partial_pbc- from a randomForest[S]RC for thepbcdata set (randomForestSRCpackage)

Arguments

format

plot.variable

Details

Constructing partial plot data with the randomForestsSRC::plot.variable function are computationally expensive. We cache plot.variable 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 partial plot data with plot.variable function, setting partial=TRUE. Each data set is built with the rfsrc_cache_datasets with the randomForestSRC version listed in the ggRandomForests DESCRIPTION file.

partial_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).
partial_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.
partial_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.

Examples

Run this code

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

# The plot.variable call
 partial_iris <- plot.variable(rfsrc_iris,
                               partial=TRUE, show.plots=FALSE)

# plot the forest partial plots
gg_dta <- gg_partial(partial_iris)
plot(gg_dta, panel=TRUE)

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

# The plot.variable call
partial_Boston <- plot.variable(rfsrc_Boston,
                                partial=TRUE, show.plots = FALSE )

# plot the forest partial plots
gg_dta <- gg_partial(partial_Boston)
plot(gg_dta, panel=TRUE)

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

# The plot.variable call - 
# survival requires a time point specification.
# for the pbc data, we want 1, 3 and 5 year survival.
partial_pbc <- lapply(c(1,3,5), function(tm){
                      plot.variable(rfsrc_pbc, surv.type = "surv", 
                                    time = tm,
                                    xvar.names = xvar, 
                                    partial = TRUE,
                                    show.plots = FALSE)
                                    })
                                    
# plot the forest partial plots
gg_dta <- gg_partial(partial_pbc)
plot(gg_dta)

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