VT.object {aVirtualTwins}

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}, \ldots, 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)