VT.tree {aVirtualTwins} | R Documentation |

An abstract reference class to compute tree

`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`

.

`vt.difft`

`VT.difft`

object`outcome`

outcome vector from

`rpart`

function`threshold`

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`

or`difft`

<`threshold`

`name`

character Names of the tree

`tree`

rpart Rpart object to construct the tree

`Ahat`

vector Indicator of beglonging to Ahat

`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

[Package *aVirtualTwins* version 1.0.1 Index]