ggRandomForests (version 2.2.0)

gg_minimal_depth: Minimal depth data object ([randomForestSRC]{var.select})

Description

the [randomForestSRC]{var.select} function implements random forest variable selection using tree minimal depth methodology. The gg_minimal_depth function takes the output from [randomForestSRC]{var.select} and creates a data.frame formatted for the plot.gg_minimal_depth function.

Usage

gg_minimal_depth(object, ...)

Value

gg_minimal_depth object, A modified list of variables from the [randomForestSRC]{var.select} function, ordered by minimal depth rank.

Arguments

object

A [randomForestSRC]{rfsrc} object, [randomForestSRC]{predict} object or the list from the [randomForestSRC]{var.select.rfsrc} function.

...

optional arguments passed to the [randomForestSRC]{var.select} function if operating on an [randomForestSRC]{rfsrc} object.

See Also

[randomForestSRC]{var.select}

plot.gg_minimal_depth

Examples

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

# Get a data.frame containing minimaldepth measures
gg_dta<- gg_minimal_depth(varsel_iris)

# Plot the gg_minimal_depth object
plot(gg_dta)

## ------------------------------------------------------------
## Regression example
## ------------------------------------------------------------
if (FALSE) {
## -------- air quality data
# rfsrc_airq <- rfsrc(Ozone ~ ., data = airquality, na.action = "na.impute")
# varsel_airq <- randomForestSRC::var.select(rfsrc_airq)
# ... or load a cached randomForestSRC object
data(varsel_airq, package="ggRandomForests")

# Get a data.frame containing error rates
gg_dta<- gg_minimal_depth(varsel_airq)

# Plot the gg_minimal_depth object
plot(gg_dta)
}

## -------- Boston data
data(varsel_boston, package="ggRandomForests")

# Get a data.frame containing error rates
plot(gg_minimal_depth(varsel_boston))

if (FALSE) {
## -------- mtcars data
data(varsel_mtcars, package="ggRandomForests")

# Get a data.frame containing error rates
plot.gg_minimal_depth(varsel_mtcars)
}

## ------------------------------------------------------------
## Survival example
## ------------------------------------------------------------
if (FALSE) {
## -------- veteran data
## 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)
# varsel_veteran <- randomForestSRC::var.select(rfsrc_veteran)
# Load a cached randomForestSRC object
data(varsel_veteran, package="ggRandomForests")

gg_dta <- gg_minimal_depth(varsel_veteran)
plot(gg_dta)


## -------- pbc data
data(varsel_pbc, package="ggRandomForests")

gg_dta <- gg_minimal_depth(varsel_pbc)
plot(gg_dta)
}