Plots an itree
object on the current graphics device. This is
based on the plotting function from rpart but modifies/extends
it in various ways to deal with some of itree
's capabilities.
1 2 3 
x 
a fitted object of class 
uniform 
if 
branch 
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. 
compress 
if 
nspace 
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

margin 
an extra fraction of white space to leave around the borders of the tree. (Long labels sometimes get cut off by the default computation). 
minbranch 
set the minimum length for a branch to 
highlight.color 
If the 
do_node_re 
Set to 
... 
arguments to be passed to or from other methods. 
This function is a method for the generic function plot
, for objects
of class itree
. The ycoordinate of the top node of the tree will always be 1.
The coordinates of the nodes are returned as a list, with
components x
and y
.
An unlabeled plot is produced on the current graphics device.
itree
, text.itree
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27  #the rpart example:
fit < itree(Price ~ Mileage + Type + Country, cu.summary)
plot(fit, compress=TRUE)
text(fit, use.n=TRUE)
### new to itree, plotting node risk:
require(mlbench); data(BostonHousing)
#fit a tree:
cart < itree(medv~.,BostonHousing,minsplit=25,minbucket=25,cp=0)
#generate thetahat values by computing average outofbag loss:
## Not run:
theta_hats < getOOBLoss(model_tree.obj=cart.bh,data=bh,nboot=100)
# Then for each leaf we estimate local risk by the mean innode thetahat.
lre < estNodeRisk(tree.obj=cart.bh,est_observation_loss=theta_hats$avgOOBloss)
# to add the lre to the plot:
plot(cart.bh, do_node_re= TRUE, uniform=TRUE)
text(cart.bh, est_node_risk = lre)
## End(Not run)
#plot using highlighting for onesided methods:
purity.tree < itree(medv~.,BostonHousing,minsplit=25,minbucket=25,cp=0,method="purity")
plot(purity.tree,highlight.color="blue")
text(purity.tree)

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