get.tree.rfsrc.RdExtracts a single tree from a forest which can then be plotted on the users browser. Works for all families. Missing data not permitted.
An object of class (rfsrc, grow).
Integer value specifying the tree to be extracted.
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.
Character value for multivariate families specifying the target outcome to be used. If left unspecified, the algorithm will choose a default target.
For survival, the time at which the predicted
survival value is evaluated at (depends on surv.type).
For survival, specifies the predicted value. See details below.
For classification, specifies the predicted value. See details below.
Use the ensemble (of all trees) for prediction, or use the requested tree for prediction (this is the default).
OOB (TRUE) or in-bag (FALSE) predicted values. Only
applies when ensemble=TRUE.
Should plots be displayed?
Number of seconds between updates to the user on approximate time to completion.
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:
For regression, the mean of the response.
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".
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.
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.
Invisibly, returns an object with hierarchical structure formatted for use with the data.tree package.
# \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))
# }