ggRandomForests (version 1.1.2)

plot.gg_rfsrc: Predicted response plot from a gg_rfsrc object.

Description

Plot the predicted response from a gg_rfsrc object, the randomForestSRC::rfsrc prediction, using the OOB prediction from the forest.

Usage

## S3 method for class 'gg_rfsrc':
plot(x, level, strat, ...)

Arguments

x

gg_rfsrc object created from a randomForestSRC::rfsrc object

level

plot confidence bands

strat

variable name or vector for comparing response values.

...

arguments passed to gg_rfsrc.

Value

• ggplot object

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

gg_rfsrc randomForestSRC::rfsrc

Examples

## ------------------------------------------------------------
## classification example
## ------------------------------------------------------------
# iris_rf <- rfsrc(Species ~ ., data = iris)
data(iris_rf, package="ggRandomForests")
gg_dta<- gg_rfsrc(iris_rf)

plot.gg_rfsrc(gg_dta)

## ------------------------------------------------------------
## Regression example
## ------------------------------------------------------------
# airq.obj <- rfsrc(Ozone ~ ., data = airquality, na.action = "na.impute")
data(airq_rf, package="ggRandomForests")
gg_dta<- gg_rfsrc(airq_rf)

plot.gg_rfsrc(gg_dta)

## ------------------------------------------------------------
## 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)
data(veteran_rf, package = "ggRandomForests")
gg_dta <- gg_rfsrc(veteran_rf)
plot(gg_dta)