VT.tree {aVirtualTwins} | R Documentation |
Tree to find subgroup
Description
An abstract reference class to compute tree
Details
VT.tree.class
and VT.tree.reg
are children of VT.tree
.
VT.tree.class
and VT.tree.reg
try to find a strong association
between difft
(in VT.difft
object) and RCT variables.
In VT.tree.reg
, a regression tree is computed on difft
values.
Then, thanks to the threshold
it flags leafs of the tree
which
are above the threshold
(when sens
is ">"). Or it flags leafs
which are below the threshold
(when sens
= "<").
In VT.tree.class
, it first flags difft
above or below
(depending on the sens
) the given threshold
. Then a
classification tree is computed to find which variables explain flagged
difft
.
To sum up, VT.tree
try to understand which variables are associated
with a big change of difft
.
Results are shown with getRules()
function. only.leaf
parameter
allows to obtain only the leaf of the tree
. only.fav
parameter
select only favorable nodes. tables
shows incidence table of the rule.
verbose
allow getRules()
to be quiet. And compete
show
also rules with maxcompete
competitors from the tree
.
Fields
vt.difft
VT.difft
objectoutcome
outcome vector from
rpart
functionthreshold
numeric Threshold for difft calculation (c)
screening
Logical. TRUE if using varimp. Default is VT.object screening field
sens
character Sens can be ">" (default) or "<". Meaning :
difft
>threshold
ordifft
<threshold
name
character Names of the tree
tree
rpart Rpart object to construct the tree
Ahat
vector Indicator of beglonging to Ahat
Methods
computeNameOfTree(type)
return label of response variable of the tree
createCompetitors()
Create competitors table
getAhatIncidence()
Return Ahat incidence
getAhatQuality()
Return Ahat quality
getData()
Return data used for tree computation
getIncidences(rule, rr.snd = T)
Return incidence of the rule
getInfos()
Return infos about tree
getRules(only.leaf = F, only.fav = F, tables = T, verbose = T, compete = F)
Return subgroups discovered by the tree. See details.
run(...)
Compute tree with rpart parameters