DynTxRegime (version 3.2)

.newOutcomeRegression: Complete Outcome Regression Step When Subset Modeling Not Used In Regression.

Description

Completes an outcome regression step when subset modeling is not incorporated into modeling. Method is not exported.

Usage

.newOutcomeRegression(moMain, moCont, txInfo, ...)
# S4 method for modelObj,modelObj,TxInfoBasic
.newOutcomeRegression(moMain, moCont, txInfo, data, response, iter, suppress)
# S4 method for modelObj,NULL,TxInfoBasic
.newOutcomeRegression(moMain, moCont, txInfo, data, response, iter, suppress)
# S4 method for NULL,modelObj,TxInfoBasic
.newOutcomeRegression(moMain, moCont, txInfo, data, response, iter, suppress)
# S4 method for ModelObj_SubsetList,ModelObj_SubsetList,TxInfoWithSubsets
.newOutcomeRegression(moMain, moCont, txInfo, data, response, iter, suppress)
# S4 method for ModelObj_SubsetList,NULL,TxInfoWithSubsets
.newOutcomeRegression(moMain, moCont, txInfo, data, response, iter, suppress)
# S4 method for NULL,ModelObj_SubsetList,TxInfoWithSubsets
.newOutcomeRegression(moMain, moCont, txInfo, data, response, iter, suppress)
# S4 method for ModelObj_DecisionPointList,ModelObj_DecisionPointList,TxInfoList
.newOutcomeRegression(moMain, moCont, txInfo, data, response, iter, suppress)
# S4 method for ModelObj_DecisionPointList,NULL,TxInfoList
.newOutcomeRegression(moMain, moCont, txInfo, data, response, iter, suppress)
# S4 method for NULL,ModelObj_DecisionPointList,TxInfoList
.newOutcomeRegression(moMain, moCont, txInfo, data, response, iter, suppress)
# S4 method for ModelObj_SubsetList_DecisionPointList,ModelObj_SubsetList_DecisionPointList,TxInfoList
.newOutcomeRegression(moMain, moCont, txInfo, data, response, iter, suppress)
# S4 method for ModelObj_SubsetList_DecisionPointList,NULL,TxInfoList
.newOutcomeRegression(moMain, moCont, txInfo, data, response, iter, suppress)
# S4 method for NULL,ModelObj_SubsetList_DecisionPointList,TxInfoList
.newOutcomeRegression(moMain, moCont, txInfo, data, response, iter, suppress)

Arguments

moMain

"modelObj"(s) for main effects component.

moCont

"modelObj"(s) for contrasts component.

txInfo

Treatment information.

...

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 histories.

response

Outcome of interest.

iter

Maximum number of iterations if iterative algorithm used.

suppress

T/F indicating if screen prints are generated.

Examples

# NOT RUN {
data(bmiData)

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

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

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

# Treatment Object
txInfo <- DynTxRegime:::.newTxInfo(fSet = NULL, 
                                   txName = "A1", 
                                   data = bmiData, 
                                   suppress = TRUE, 
                                   verify = TRUE)

obj <- DynTxRegime:::.newOutcomeRegression(moMain = moMain, 
                                           moCont = moCont, 
                                           txInfo = txInfo, 
                                           data = bmiData, 
                                           response = y, 
                                           iter = 100L, 
                                           suppress = TRUE)

is(obj)
coef(obj)
fitObject(obj)
plot(obj)
predict(obj)
predict(obj, bmiData)
DynTxRegime:::.predictAllTreatments(object = obj, data = bmiData)
print(obj)
show(obj)
summary(obj)

fSet1 <- function(data){
           subsets <- list(list("subset1",c("CD","MR")),
                           list("subset2",c("CD","MR")))
           txOpts <- character(nrow(data))
           txOpts[data$baselineBMI <= 35] <- "subset2"
           txOpts[data$baselineBMI > 35] <- "subset1"
           return(list("subsets" = subsets, "txOpts" = txOpts))
         }

moMain <- list()
moMain[[1L]] <- buildModelObjSubset(model = ~parentBMI+baselineBMI+month4BMI, 
                                    solver.method = 'lm',
                                    subset = "subset1")
moMain[[2L]] <- buildModelObjSubset(model = ~parentBMI+baselineBMI+month4BMI, 
                                    solver.method = 'lm',
                                    subset = "subset1")
moCont <- list()
moCont[[1L]] <- buildModelObjSubset(model = ~parentBMI+baselineBMI+month4BMI, 
                                    solver.method = 'lm',
                                    subset = "subset1")
moCont[[2L]] <- buildModelObjSubset(model = ~parentBMI+baselineBMI+month4BMI, 
                                    solver.method = 'lm',
                                    subset = "subset2")

moMain <- DynTxRegime:::.newModelObjSubset(moMain)
moCont <- DynTxRegime:::.newModelObjSubset(moCont)

txInfo <- DynTxRegime:::.newTxInfo(fSet = fSet1, 
                                   txName = "A2", 
                                   data = bmiData, 
                                   suppress = TRUE, 
                                   verify = TRUE)

obj <- DynTxRegime:::.newOutcomeRegression(moMain = moMain, 
                                           moCont = moCont, 
                                           txInfo = txInfo, 
                                           data = bmiData, 
                                           response = y, 
                                           iter = 100L, 
                                           suppress = TRUE)

is(obj)
coef(obj)
fitObject(obj)
plot(obj)
predict(obj)
predict(obj, bmiData)
DynTxRegime:::.predictAllTreatments(object = obj, data = bmiData)
print(obj)
show(obj)
summary(obj)

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

# Treatment Object
txInfo <- DynTxRegime:::.newTxInfo(fSet = NULL, 
                                   txName = "A2", 
                                   data = bmiData, 
                                   suppress = TRUE, 
                                   verify = TRUE)

obj <- DynTxRegime:::.newOutcomeRegression(moMain = moMain, 
                                           moCont = moCont, 
                                           txInfo = txInfo, 
                                           data = bmiData, 
                                           response = y, 
                                           iter = 0L, 
                                           suppress = TRUE)

is(obj)
coef(obj)
fitObject(obj)
plot(obj)
predict(obj)
predict(obj, bmiData)
DynTxRegime:::.predictAllTreatments(object = obj, data = bmiData)
print(obj)
show(obj)
summary(obj)

obj <- DynTxRegime:::.newOutcomeRegression(moMain = moMain, 
                                           moCont = NULL, 
                                           txInfo = txInfo, 
                                           data = bmiData, 
                                           response = y, 
                                           iter = 0L, 
                                           suppress = TRUE)

is(obj)
print(obj)

obj <- DynTxRegime:::.newOutcomeRegression(moMain = NULL, 
                                           moCont = moCont, 
                                           txInfo = txInfo, 
                                           data = bmiData, 
                                           response = y, 
                                           iter = 0L, 
                                           suppress = TRUE)

is(obj)
print(obj)

bmiData$A3 <- bmiData$A2
bmiData$A3[bmiData$A1 == "CD"] <- "CD"

fSet1 <- function(data){
           subsets <- list(list("subset1",c("CD","MR")),
                           list("subset2",c("CD")))
           txOpts <- character(nrow(data))
           txOpts[data$A1 == "CD"] <- "subset2"
           txOpts[data$A1 == "MR"] <- "subset1"
           return(list("subsets" = subsets, "txOpts" = txOpts))
         }

# Integer treatment with subsetting
txInfo <- DynTxRegime:::.newTxInfo(fSet = fSet1, 
                                   txName = "A3",  
                                   data = bmiData,  
                                   suppress = TRUE,  
                                   verify = TRUE)

obj <- DynTxRegime:::.newOutcomeRegression(moMain = moMain, 
                                           moCont = moCont, 
                                           txInfo = txInfo, 
                                           data = bmiData, 
                                           response = y, 
                                           suppress = TRUE, 
                                           iter = 0L)

is(obj)
coef(obj)
fitObject(obj)
plot(obj)
predict(obj)
predict(obj, bmiData)
DynTxRegime:::.predictAllTreatments(object = obj, data = bmiData)
print(obj)
show(obj)
summary(obj)


obj <- DynTxRegime:::.newOutcomeRegression(moMain = moMain, 
                                           moCont = NULL, 
                                           txInfo = txInfo, 
                                           data = bmiData, 
                                           response = y, 
                                           suppress = TRUE, 
                                           iter = 0L)

is(obj)
print(obj)

obj <- DynTxRegime:::.newOutcomeRegression(moMain = NULL, 
                                           moCont = moCont, 
                                           txInfo = txInfo, 
                                           data = bmiData, 
                                           response = y, 
                                           suppress = TRUE, 
                                           iter = 0L)

is(obj)
print(obj)

# }