VT.object: VT.object
In aVirtualTwins: Adaptation of Virtual Twins Method from Jared Foster

Description Details Fields Methods See Also Examples

A Reference Class to deal with RCT dataset

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.

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.

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.

VT.difft

## 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)