VT.object {aVirtualTwins} | R Documentation |

## VT.object

### Description

A Reference Class to deal with RCT dataset

### Details

Currently working with binary response only. Continous will come, one day.
Two-levels treatment only as well.

`data`

field should be as described, however if virtual twins won't used
interactions, there is no need to transform factors. See
formatRCTDataset for more details.

### Fields

`data`

Data.frame with format: *Y,T,X_{1}, …, X_{p}*. Y must be
two levels factor if type is binary. T must be numeric or integer.

`screening`

Logical, set to `FALSE`

Set to `TRUE`

to use
`varimp`

in trees computation.

`varimp`

Character vector of important variables to use in trees
computation.

`delta`

Numeric representing the difference of incidence between
treatments.

`type`

Character : binary or continous. Only binary is currently
available.

### Methods

`computeDelta()`

Compute delta value.

`getData(interactions = F)`

Return dataset. If interactions is set to T, return data with treatement interactions

`getFormula()`

Return formula : Y~T+X1+...+Xp. Usefull for cforest function.

`getIncidences(rule = NULL)`

Return incidence table of data if rule set to NULL. Otherwise return incidence for the rule.

`getX(interactions = T, trt = NULL)`

Return predictors (T,X,X*T,X*(1-T)). Or (T,X) if interactions is FALSE.
If trt is not NULL, return predictors for T = trt

`getXwithInt()`

Return predictors with interactions. Use VT.object::getX(interactions = T) instead.

`getY(trt = NULL)`

Return outcome. If trt is not NULL, return outcome for T = trt.

`switchTreatment()`

Switch treatment value.

### See Also

`VT.difft`

### Examples

## Not run:
# Default use :
vt.o <- VT.object$new(data = my.rct.dataset)
# Getting data
head(vt.o$data)
# or getting predictor with interactions
vt.o$getX(interactions = T)
# or getting X|T = 1
vt.o$getX(trt = 1)
# or getting Y|T = 0
vt.o$getY(0)
# Print incidences
vt.o$getIncidences()
## End(Not run)

[Package

*aVirtualTwins* version 1.0.1

Index]