ggRandomForests (version 2.2.0)

plot.gg_minimal_depth: Plot a gg_minimal_depth object for random forest variable ranking.

Description

Plot a gg_minimal_depth object for random forest variable ranking.

Usage

# S3 method for gg_minimal_depth
plot(x, selection = FALSE, type = c("named", "rank"), lbls, ...)

Value

ggplot object

Arguments

x

gg_minimal_depth object created from a rfsrc object

selection

should we restrict the plot to only include variables selected by the minimal depth criteria (boolean).

type

select type of y axis labels c("named","rank")

lbls

a vector of alternative variable names.

...

optional arguments passed to gg_minimal_depth

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. (2014). Random Forests for Survival, Regression and Classification (RF-SRC), R package version 1.5.

See Also

var.select gg_minimal_depth

Examples

if (FALSE) {
## Examples from RFSRC package...
## ------------------------------------------------------------
## classification example
## ------------------------------------------------------------
## -------- iris data
## You can build a randomForest
# rfsrc_iris <- rfsrc(Species ~ ., data = iris)
# varsel_iris <- 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
## ------------------------------------------------------------
## -------- air quality data
# rfsrc_airq <- rfsrc(Ozone ~ ., data = airquality, na.action = "na.impute")
# varsel_airq <- 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))

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

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

## ------------------------------------------------------------
## Survival example
## ------------------------------------------------------------
## -------- 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 <- 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)

}