interaction_data: Cached `find.interaction` matrix objects for examples, diagnostics and vignettes. Data sets storing `find.interaction` matrix objects corresponding to training data according to the following naming convention:

`interaction_iris` - from a randomForestSR[C] for the `iris` data set.

`interaction_boston` - from a randomForestS[R]C for the `Boston` housing data set (`MASS` package).

`interaction_pbc` - from a randomForest[S]RC for the `pbc` data set (`randomForestSRC` package)

Description

Cached find.interaction matrix objects for examples, diagnostics and vignettes.

Data sets storing find.interaction matrix objects corresponding to training data according to the following naming convention:

interaction_iris - from a randomForestSR[C] for the iris data set.
interaction_boston - from a randomForestS[R]C for the Boston housing data set (MASS package).
interaction_pbc - from a randomForest[S]RC for the pbc data set (randomForestSRC package)

Arguments

Format

find.interaction matrix

Details

Constructing the minimal depth interaction matrices on randomForestSRC objects are computationally expensive. We cache find.interaction matrix 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 (see rfsrc_data), then calculate the minimal depth variable interaction table with find.interaction. Each data set is built with the cache_rfsrc_datasets with the randomForestSRC version listed in the ggRandomForests DESCRIPTION file.

interaction_iris - The famous (Fisher's or Anderson's) iris data 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).
interaction_airq - The airquality data set is from the New York State Department of Conservation (ozone data) and the National Weather Service (meteorological data) collected in New York, from May to September 1973. Build regression random forest for predicting Ozone on 5 covariates and 153 observations.
interaction_mtcars - The mtcars data was extracted from the 1974 Motor Trend US magazine, and comprises fuel consumption and 10 aspects of automobile design and performance for 32 automobiles (1973-74 models). Build a regression random forest for predicting mpg on 10 covariates and 32 observations.
interaction_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.
interaction_pbc - The pbc data 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 the randomForestSRC package. Build a survival random forest for time-to-event death data with 17 covariates and 312 observations (remaining 106 observations are held out).
interaction_veteran - Veteran's Administration randomized trial of two treatment regimens for lung cancer. Build a survival random forest for time-to-event death data with 6 covariates and 137 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.

#--------------------- 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 ---------------------

Fleming 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

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

# The interaction table 
interaction_iris <- find.interaction(rfsrc_iris)

# plot the forest interaction table
gg_dta <- gg_interaction(interaction_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 interaction table 
interaction_boston <- find.interaction(rfsrc_boston)

# plot the forest interaction table
gg_dta <- gg_interaction(interaction_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 interaction table 
interaction_pbc <- find.interaction(rfsrc_pbc)

# plot the forest interaction table
gg_dta <- gg_interaction(interaction_pbc)
plot(gg_dta, panel=TRUE)
                   
}