DynTxRegime (version 3.01)

.newIQLearnFS_ME: Third Step of IQ-Learning Algorithm

Description

Regression of Estimated Main Effects of Second Stage Outcome Step of IQ-Learning Algorithm. Method is not exported.

Usage

.newIQLearnFS_ME(moMain, moCont, response, ...)
# S4 method for modelObj,modelObj
.newIQLearnFS_ME(moMain, moCont, response, data, txName, iter, suppress)
# S4 method for modelObj,NULL
.newIQLearnFS_ME(moMain, moCont, response, data, txName, iter, suppress)
# S4 method for NULL,modelObj
.newIQLearnFS_ME(moMain, moCont, response, data, txName, iter, suppress)

Arguments

moMain

"modelObj" for main effects component of estimated main effects.

moCont

"modelObj" for contrasts component of estimated main effects.

response

An object of class "IQLearnSS-class."

...

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.

txName

"character" name of treatment variable in data.

iter

Maximum number of iterations if iterative algorithm used.

suppress

T/F indicating if screen prints are generated.

Examples

data(bmiData)

bmiData$A2 <- as.factor(bmiData$A2)
bmiData$A1 <- as.factor(bmiData$A1)

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

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

obj1 <- DynTxRegime:::.newIQLearnSS(moMain = moMain, 
                                    moCont = moCont, 
                                    response = y,
                                    data = bmiData, 
                                    txName = 'A2', 
                                    iter = 0L, 
                                    suppress = TRUE)


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

obj2 <- DynTxRegime:::.newIQLearnFS_ME(moMain = moMain, 
                                       moCont = moCont, 
                                       response = obj1,
                                       data = bmiData, 
                                       txName = 'A1', 
                                       iter = 0L, 
                                       suppress = TRUE)

is(obj2)

coef(obj2)
DTRstep(obj2)
fitObject(obj2)
outcome(obj2)
plot(obj2)
propen(obj2)
show(obj2)
summary(obj2)