Plots an SurvCART or LongCART object on the current graphics device.
# S3 method for SurvCART
plot(x, uniform = FALSE, branch = 1, compress = FALSE,
nspace = branch, margin = 0, minbranch = 0.3, ...)
# S3 method for LongCART
plot(x, uniform = FALSE, branch = 1, compress = FALSE,
nspace = branch, margin = 0, minbranch = 0.3, ...)a fitted object of class "SurvCART", containing a survival tree or "LongCART", containing a longitudinal tree.
similar to plot.rpart; if TRUE, uniform vertical spacing
of the nodes is used; this may be less cluttered when fitting a large plot
onto a page. The default is to use a non-uniform spacing proportional to the
error in the fit.
similar to plot.rpart;
controls the shape of the branches from parent to child node. Any number from
0 to 1 is allowed. A value of 1 gives square shouldered branches, a value of
0 give V shaped branches, with other values being intermediate.
similar to plot.rpart;
if FALSE, the leaf nodes will be at the horizontal plot coordinates of 1:nleaves. If TRUE, the routine attempts a more compact arrangement of the tree.
similar to plot.rpart; the amount of extra space between a node with children
and a leaf, as compared to the minimal space between leaves. Applies to compressed trees only.
The default is the value of branch.
similar to plot.rpart; an extra fraction of white space to
leave around the borders of the tree. (Long labels sometimes get cut off by the default computation).
similar to plot.rpart; set the minimum length for a branch to minbranch
times the average branch length. This parameter is ignored if uniform=TRUE. Sometimes a split will
give very little improvement, or even (in the classification case) no improvement at all. A tree with
branch lengths strictly proportional to improvement leaves no room to squeeze in node labels.
arguments to be passed to or from other methods.
The coordinates of the nodes are returned as a list, with components x
and y.
This function is a method for the generic function plot, for objects of class
SurvCART. The y-coordinate of the top node of the tree will always be 1.
Kundu, M. G., and Harezlak, J. (2019). Regression trees for longitudinal data with baseline covariates. Biostatistics & Epidemiology, 3(1):1-22.
Kundu, M. G., and Ghosh, S. (2021). Survival trees based on heterogeneity in time-to-event and censoring distributions using parameter instability test. Statistical Analysis and Data Mining: The ASA Data Science Journal, 14(5), 466-483.
# NOT RUN {
#--- Get the data
data(GBSG2)
#numeric coding of character variables
GBSG2$horTh1<- as.numeric(GBSG2$horTh)
GBSG2$tgrade1<- as.numeric(GBSG2$tgrade)
GBSG2$menostat1<- as.numeric(GBSG2$menostat)
#Add subject id
GBSG2$subjid<- 1:nrow(GBSG2)
#--- Run SurvCART()
out<- SurvCART(data=GBSG2, patid="subjid", censorvar="cens", timevar="time", event.ind=1,
gvars=c('horTh1', 'age', 'menostat1', 'tsize', 'tgrade1', 'pnodes', 'progrec', 'estrec'),
tgvars=c(0,1,0,1,0,1, 1,1),
alpha=0.05, minsplit=80,
minbucket=40, print=TRUE)
#--- Plot tree
par(xpd = TRUE)
plot(out, compress = TRUE)
text(out, use.n = TRUE)
# }
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