ggRandomForests (version 2.2.0)

gg_rfsrc.rfsrc: Predicted response data object

Description

Extracts the predicted response values from the rfsrc object, and formats data for plotting the response using plot.gg_rfsrc.

Usage

# S3 method for rfsrc
gg_rfsrc(object, oob = TRUE, by, ...)

Value

gg_rfsrc object

Arguments

object

rfsrc object

oob

boolean, should we return the oob prediction , or the full forest prediction.

by

stratifying variable in the training dataset, defaults to NULL

...

extra arguments

Details

surv_type ("surv", "chf", "mortality", "hazard") for survival forests

oob boolean, should we return the oob prediction , or the full forest prediction.

See Also

plot.gg_rfsrc rfsrc plot.rfsrc gg_survival

Examples

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

plot(gg_dta)

## ------------------------------------------------------------
## Regression example
## ------------------------------------------------------------
if (FALSE) {
## -------- air quality data
# rfsrc_airq <- rfsrc(Ozone ~ ., data = airquality, na.action = "na.impute")
data(rfsrc_airq, package="ggRandomForests")
gg_dta<- gg_rfsrc(rfsrc_airq)

plot(gg_dta)
}

## -------- Boston data
data(rfsrc_boston, package="ggRandomForests")
plot(gg_rfsrc(rfsrc_boston))

### randomForest example
data(Boston, package="MASS")
rf_boston <- randomForest::randomForest(medv ~ ., data = Boston)
plot(gg_rfsrc(rf_boston))

if (FALSE) {
## -------- mtcars data
data(rfsrc_mtcars, package="ggRandomForests")
gg_dta<- gg_rfsrc(rfsrc_mtcars)

plot(gg_dta)
}
## ------------------------------------------------------------
## Survival example
## ------------------------------------------------------------
if (FALSE) {
## -------- veteran data
## randomized trial of two treatment regimens for lung cancer
# data(veteran, package = "randomForestSRC")
# rfsrc_veteran <- rfsrc(Surv(time, status) ~ ., data = veteran, ntree = 100)
data(rfsrc_veteran, package = "ggRandomForests")
gg_dta <- gg_rfsrc(rfsrc_veteran)
plot(gg_dta)

gg_dta <- gg_rfsrc(rfsrc_veteran, conf.int=.95)
plot(gg_dta)

gg_dta <- gg_rfsrc(rfsrc_veteran, by="trt")
plot(gg_dta)


## -------- pbc data
## We don't run this because of bootstrap confidence limits
data(rfsrc_pbc, package = "ggRandomForests")

gg_dta <- gg_rfsrc(rfsrc_pbc)
plot(gg_dta)

gg_dta <- gg_rfsrc(rfsrc_pbc, conf.int=.95)
plot(gg_dta)


gg_dta <- gg_rfsrc(rfsrc_pbc, by="treatment")
plot(gg_dta)
}