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("prob", "bayes"), oob = TRUE, show.plots = TRUE)

Arguments

object

An object of class (rfsrc, grow).

tree.id

Integer value specifying the tree to be extracted.

target

For classification, an integer or character value specifying the class to focus on (defaults to the first class). For competing risks, an integer value between 1 and J indicating the event of interest, where J is the number of event types. The default is to use the first event type.

m.target

Character value for multivariate families specifying the target outcome to be used. If left unspecified, the algorithm will choose a default target.

time

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

surv.type

For survival, specifies the predicted value. See details below.

class.type

For classification, specifies the predicted value. See details below.

oob

OOB (TRUE) or in-bag (FALSE) predicted values.

show.plots

Should plots be displayed?

Value

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

Details

Extracts a specified tree from a forest and converts the tree to a hierarchical structure suitable for use with the "data.tree" package. Plotting the object will conveniently render the tree on the users browser. Left tree splits are displayed. For continuous values, left split is displayed as an inequality with right split equal to the reversed inequality. For factors, split values are described in terms of the levels of the factor. In this case, the left daughter split is a set consisting of all levels that are assigned to the left daughter node. The right daughter split is the complement of this set.

Terminal nodes are highlighted by color and display the sample size and predicted value. Here predicted value equals the forest ensemble value and not the tree predicted value. This is done as it allows one to visualize the ensemble predictor over a given tree and therefore a given partition of the feature space. The predicted value displayed is chosen according to the following rules and options:

For regression, the predicted response is used.
For classification, it is the predicted class probability specified by target, or the class of maximum probability depending on whether class.type is set to "prob" or "bayes".
For multivariate families, it is the predicted value of the outcome specified by m.target and if that is a classification outcome, by target.
For survival, the choices are:
- Mortality (mort).
- Relative frequency of mortality (rel.freq).
- Predicted survival (surv), where the predicted survival is for the time point specified using time (the default is the median follow up time).
For competing risks, the choices are:
- The expected number of life years lost (years.lost).
- The cumulative incidence function (cif).
- The cumulative hazard function (chf).
In all three cases, the predicted value is for the event type specified by target. For cif and chf the quantity is evaluated at the time point specified by time.

Examples

Run this code

# NOT RUN {
## ------------------------------------------------------------
## 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))

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

iris.obj <- rfsrc(Species ~., data = iris)
plot(get.tree(iris.obj, 25))
plot(get.tree(iris.obj, 25, class.type = "bayes"))
plot(get.tree(iris.obj, 25, target = "setosa"))
plot(get.tree(iris.obj, 25, target = "versicolor"))
plot(get.tree(iris.obj, 25, 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))



# }

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