DynTxRegime (version 3.2)

.newRWL: Residual Weighted Learning.

Description

Estimates the optimal treatment regime using residual weighted learning.

Usage

.newRWL(moPropen, moMain, ...)
# S4 method for modelObj,modelObj
.newRWL(moPropen, moMain, data, response, txName, regime, lambdas, cvFolds, 
        kernel, kparam, responseType, txVec, guess, suppress)

Arguments

moPropen

"modelObj" for propensity score regression.

moMain

"modelObj" for outcome regression.

...

Used to pass arguments that are required but the class of which does not determine the method that is selected.

data

"data.frame" of covariates and treatment history.

response

Outcome of interest.

txName

Treatment variable column header in data.

regime

"formula" description of decision function.

lambdas

Tuning parameter(s).

cvFolds

Number of cross-validation folds.

kernel

"character" description of kernel function.

kparam

"numeric" parameter for kernel function.

responseType

"character" indicating if outcome is continuous or count data.

txVec

Treatment vector recast as +/- 1.

guess

Inital parameter guesses for optimization routine.

suppress

T/F indicating if prints to screen are executed.

Examples

# NOT RUN {
data(bmiData)

y <- -(bmiData$month12BMI - bmiData$month4BMI) / bmiData$month4BMI * 100

moPropen <- buildModelObj(model = ~1, 
                          solver.method = 'glm',
                          solver.args = list("family" = "binomial"),
                          predict.args = list("type" = "response"))

moMain <- buildModelObj(model = ~ parentBMI + baselineBMI + month4BMI, 
                        solver.method = 'lm')

regime <- ~ parentBMI + baselineBMI + gender

txVec <- numeric(nrow(bmiData)) - 1L
txVec[bmiData$A2 == "MR"] <- 1L
bmiData$A2 <- as.factor(bmiData$A2)
# }
# NOT RUN {
obj <- DynTxRegime:::.newRWL(moPropen = moPropen,
                             moMain = moMain,
                             data = bmiData,
                             response = y,
                             txName = "A2",
                             regime = regime,
                             lambdas = 0.1,
                             cvFolds = 0L,
                             kernel = "linear",
                             kparam = NULL,
                             responseType = "continuous",
                             txVec = txVec,
                             guess = NULL,
                             suppress = TRUE)

is(obj)
coef(obj)
cvInfo(obj)
DTRstep(obj)
estimator(obj)
fitObject(obj)
optimObj(obj)
optTx(obj)
optTx(obj,bmiData)
outcome(obj)
print(obj)
propen(obj)
regimeCoef(obj)
show(obj)
summary(obj)

obj <- DynTxRegime:::.newRWL(moPropen = moPropen,
                             moMain = moMain,
                             data = bmiData,
                             response = y,
                             txName = "A2",
                             regime = regime,
                             lambdas = c(0.1,0.2),
                             cvFolds = 4L,
                             kernel = "linear",
                             kparam = NULL,
                             responseType = "continuous",
                             txVec = txVec,
                             guess = NULL,
                             suppress = TRUE)

is(obj)
coef(obj)
cvInfo(obj)
DTRstep(obj)
estimator(obj)
fitObject(obj)
optimObj(obj)
optTx(obj)
optTx(obj,bmiData)
outcome(obj)
print(obj)
propen(obj)
regimeCoef(obj)
show(obj)
summary(obj)


obj <- DynTxRegime:::.newRWL(moPropen = moPropen,
                             moMain = moMain,
                             data = bmiData,
                             response = y,
                             txName = "A2",
                             regime = regime,
                             lambdas = c(0.1,0.2),
                             cvFolds = 4L,
                             kernel = "radial",
                             kparam = c(1,2),
                             responseType = "continuous",
                             txVec = txVec,
                             guess = NULL,
                             suppress = TRUE)

is(obj)
coef(obj)
cvInfo(obj)
DTRstep(obj)
estimator(obj)
fitObject(obj)
optimObj(obj)
optTx(obj)
optTx(obj,bmiData)
outcome(obj)
print(obj)
propen(obj)
regimeCoef(obj)
show(obj)
summary(obj)

obj <- DynTxRegime:::.newRWL(moPropen = moPropen,
                             moMain = moMain,
                             data = bmiData,
                             response = y,
                             txName = "A2",
                             regime = regime,
                             lambdas = 0.1,
                             cvFolds = 4L,
                             kernel = "radial",
                             kparam = c(1,2),
                             responseType = "continuous",
                             txVec = txVec,
                             guess = NULL,
                             suppress = TRUE)

is(obj)
coef(obj)
cvInfo(obj)
DTRstep(obj)
estimator(obj)
fitObject(obj)
optimObj(obj)
optTx(obj)
optTx(obj,bmiData)
outcome(obj)
print(obj)
propen(obj)
regimeCoef(obj)
show(obj)
summary(obj)
# }