ggRandomForests (version 2.0.1)

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

"gg_rfsrc"(object, oob = TRUE, by, ...)

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

Value

gg_rfsrc object

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
## ------------------------------------------------------------
## Not run: 
# ## -------- 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)
# ## End(Not run)
## Not run: 
# ## -------- Boston data
# data(rfsrc_Boston, package="ggRandomForests")
# plot(rfsrc_Boston) 
# ## End(Not run)
## Not run: 
# ## -------- mtcars data
# data(rfsrc_mtcars, package="ggRandomForests")
# gg_dta<- gg_rfsrc(rfsrc_mtcars)
# 
# plot(gg_dta)
# ## End(Not run)
## ------------------------------------------------------------
## Survival example
## ------------------------------------------------------------
## Not run: 
# ## -------- 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)
# ## End(Not run)
## Not run: 
# ## -------- pbc data
# ## We don't run this because of bootstrap confidence limits
# data(rfsrc_pbc, package = "ggRandomForests")
# ## End(Not run)
## Not run: 
# gg_dta <- gg_rfsrc(rfsrc_pbc)
# plot(gg_dta)
# 
# gg_dta <- gg_rfsrc(rfsrc_pbc, conf.int=.95)
# plot(gg_dta)
# ## End(Not run)
## Not run: 
# gg_dta <- gg_rfsrc(rfsrc_pbc, by="treatment")
# plot(gg_dta)
# ## End(Not run)