randomForestSRC (version 3.4.1)

get.tree.rfsrc: Extract a Single Tree from a Forest and plot it on your browser

Description

Extracts a single tree from a forest which can then be plotted on the users browser. Works for all families. Missing data not permitted.

Usage

# S3 method for rfsrc
get.tree(object, tree.id, target, m.target = NULL,
   time, surv.type = c("mort", "rel.freq", "surv", "years.lost", "cif", "chf"),
   class.type = c("bayes", "rfq", "prob"),
   ensemble = FALSE, oob = TRUE, show.plots = TRUE, do.trace = FALSE)

Value

Invisibly, returns an object with hierarchical structure formatted for use with the data.tree package.

Arguments

object

An object of class (rfsrc, grow).

tree.id

Integer specifying the tree to extract.

target

For classification: integer or character indicating the class of interest (defaults to the first class). For competing risks: integer between 1 and J (number of event types) specifying the event of interest (default is the first event type).

m.target

Character string specifying the target outcome for multivariate families. If unspecified, a default is selected.

time

For survival: time point at which the predicted value is evaluated (depends on surv.type).

surv.type

For survival: specifies the type of predicted value returned. See Details.

class.type

For classification: specifies the type of predicted value. See Details.

ensemble

Logical. If TRUE, prediction is based on the ensemble of all trees. If FALSE (default), prediction is based on the specified tree.

oob

Logical. Use OOB predicted values (TRUE) or in-bag values (FALSE). Only applies when ensemble=TRUE.

show.plots

Logical. Should plots be displayed?

do.trace

Number of seconds between progress updates.

Author

Hemant Ishwaran and Udaya B. Kogalur

Many thanks to @dbarg1 on GitHub for the initial prototype of this function

Details

Extracts a specified tree from a forest and converts it into a hierarchical structure compatible with the data.tree package. Plotting the resulting object renders an interactive tree visualization in the user's web browser.

Left-hand splits are shown. For continuous variables, the left split is displayed as an inequality (e.g., x < value); the right split is the reverse. For factor variables, the left daughter node is defined by a set of levels assigned to it; the right daughter is its complement.

Terminal nodes are highlighted with color and display both sample size and predicted value. By default, the predicted value corresponds to the prediction from the selected tree, and the sample size refers to the in-bag cases reaching the terminal node. If ensemble = TRUE, the predicted value equals the forest ensemble prediction, allowing visualization of the full forest predictor over the selected tree's partition. In this case, sample sizes refer to all observations (not just in-bag cases).

Predicted values displayed in terminal nodes are defined as follows:

1. For regression: the mean of the response.

2. For classification: depends on the class.type argument and target class:

   • If class.type = "bayes", the predicted class with the most votes, or the RFQ classifier threshold in two-class problems.

   • If class.type = "prob", the class probability for the target class.

3. For multivariate families: the predicted value for the outcome specified by m.target, using the logic above depending on whether the outcome is continuous or categorical.

4. For survival:

   • mort: estimated mortality (Ishwaran et al., 2008).

   • rel.freq: relative frequency of mortality.

   • surv: predicted survival probability at the specified time (time).

5. For competing risks:

   • years.lost: expected number of life years lost.

   • cif: cumulative incidence function.

   • chf: cause-specific cumulative hazard function.

   For cif and chf, predictions are evaluated at the time point given by time, and all metrics are specific to the event type indicated by target.

Examples

# \donttest{
## ------------------------------------------------------------
## survival/competing risk
## ------------------------------------------------------------

## survival - veteran data set but with factors
## note that diagtime has many levels
data(veteran, package = "randomForestSRC")
vd <- veteran
vd$celltype=factor(vd$celltype)
vd$diagtime=factor(vd$diagtime)
vd.obj <- rfsrc(Surv(time,status)~., vd, ntree = 100, nodesize = 5)
plot(get.tree(vd.obj, 3))

## competing risks
data(follic, package = "randomForestSRC")
follic.obj <- rfsrc(Surv(time, status) ~ ., follic, nsplit = 3, ntree = 100)
plot(get.tree(follic.obj, 2))

## ------------------------------------------------------------
## regression
## ------------------------------------------------------------

airq.obj <- rfsrc(Ozone ~ ., data = airquality)
plot(get.tree(airq.obj, 10))

## ------------------------------------------------------------
## two-class imbalanced data (see imbalanced function)
## ------------------------------------------------------------

data(breast, package = "randomForestSRC")
breast <- na.omit(breast)
f <- as.formula(status ~ .)
breast.obj <- imbalanced(f, breast)

## compare RFQ to Bayes Rule
plot(get.tree(breast.obj, 1, class.type = "rfq", ensemble = TRUE))
plot(get.tree(breast.obj, 1, class.type = "bayes", ensemble = TRUE))

## ------------------------------------------------------------
## classification
## ------------------------------------------------------------

iris.obj <- rfsrc(Species ~., data = iris, nodesize = 10)

## equivalent
plot(get.tree(iris.obj, 25))
plot(get.tree(iris.obj, 25, class.type = "bayes"))

## predicted probability displayed for terminal nodes
plot(get.tree(iris.obj, 25, class.type = "prob", target = "setosa"))
plot(get.tree(iris.obj, 25, class.type = "prob", target = "versicolor"))
plot(get.tree(iris.obj, 25, class.type = "prob", target = "virginica"))


## ------------------------------------------------------------
## multivariate regression
## ------------------------------------------------------------

mtcars.mreg <- rfsrc(Multivar(mpg, cyl) ~., data = mtcars)
plot(get.tree(mtcars.mreg, 10, m.target = "mpg"))
plot(get.tree(mtcars.mreg, 10, m.target = "cyl"))


## ------------------------------------------------------------
## multivariate mixed outcomes
## ------------------------------------------------------------

mtcars2 <- mtcars
mtcars2$carb <- factor(mtcars2$carb)
mtcars2$cyl <- factor(mtcars2$cyl)
mtcars.mix <- rfsrc(Multivar(carb, mpg, cyl) ~ ., data = mtcars2)
plot(get.tree(mtcars.mix, 5, m.target = "cyl"))
plot(get.tree(mtcars.mix, 5, m.target = "carb"))

## ------------------------------------------------------------
## unsupervised analysis
## ------------------------------------------------------------

mtcars.unspv <- rfsrc(data = mtcars)
plot(get.tree(mtcars.unspv, 5))



# }