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

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)

## 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.

ensemble

Use the ensemble (of all trees) for prediction, or use the requested tree for prediction (this is the default).

oob

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

show.plots

Should plots be displayed?

do.trace

Number of seconds between updates to the user on approximate time to completion.

## 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. By default, predicted value equals the tree predicted value and sample size are terminal node inbag sample sizes. If ensemble=TRUE, then the predicted value equals the forest ensemble value which could be useful as it allows one to visualize the ensemble predictor over a given tree and therefore for a given partition of the feature space. In this case, sample sizes are for all cases and not the tree specific inbag cases.

The predicted value displayed is as follows:

1. For regression, the mean of the response.

2. For classification, for the target class specified by target, either the class with most votes if class.type="bayes"; or in a two-class problem the classifier using the RFQ quantile threshold if class.type="bayes" (see imbalanced for more details); or the relative class frequency when class.type="prob".

3. For multivariate families, the predicted value of the outcome specified by m.target. This being the value for regression or classification described above, depending on whether the outcome is real valued or a factor.

4. 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).

5. 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.

## Value

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

## Author

Hemant Ishwaran and Udaya B. Kogalur

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

## 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))

# }