ggRandomForests (version 1.0.0)

gg_error.ggRandomForests: randomForestSRC error rate data object

Description

Extract the cumulative (OOB) randomForestSRC error rate as a function of number of trees.

Usage

gg_error.ggRandomForests(object, ...)

Arguments

object

randomForestSRC object.

...

optional arguments (not used).

Value

• gg_error data.frame with one column indicating the tree number, and the remaining columns from the rfsrc$err.rate return value.

Details

The gg_error function simply returns the rfsrc$err.rate object as a data.frame, and assigns the class for connecting to the plot.gg_error function.

References

Breiman L. (2001). Random forests, Machine Learning, 45:5-32.

Ishwaran H. and Kogalur U.B. (2007). Random survival forests for R, Rnews, 7(2):25-31.

Ishwaran H. and Kogalur U.B. (2013). Random Forests for Survival, Regression and Classification (RF-SRC), R package version 1.4.

See Also

plot.gg_error rfsrc plot.rfsrc

Examples

## Examples from RFSRC package...
## ------------------------------------------------------------
## classification example
## ------------------------------------------------------------
## You can build a randomForest
# iris_rf <- rfsrc(Species ~ ., data = iris)
# ... or load a cached randomForestSRC object
data(iris_rf, package="ggRandomForests")

# Get a data.frame containing error rates
ggrf.obj<- gg_error(iris_rf)

# Plot the gg_error object
plot(ggrf.obj)

## ------------------------------------------------------------
## Regression example
## ------------------------------------------------------------
# airq_rf <- rfsrc(Ozone ~ ., data = airquality, na.action = "na.impute")
# ... or load a cached randomForestSRC object
data(airq_rf, package="ggRandomForests")

# Get a data.frame containing error rates
ggrf.obj<- gg_error(airq_rf)

# Plot the gg_error object
plot(ggrf.obj)

## ------------------------------------------------------------
## Survival example
## ------------------------------------------------------------
## veteran data
## randomized trial of two treatment regimens for lung cancer
# data(veteran, package = "randomForestSRC")
# veteran_rf <- rfsrc(Surv(time, status) ~ ., data = veteran, ntree = 100)

# Load a cached randomForestSRC object
data(veteran_rf, package="ggRandomForests")

ggrf.obj <- gg_error(veteran_rf)
plot(ggrf.obj)