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# October 11, 2018
#
#' Interactive Q-Learning
#'
#' The complete interactive Q-Learning algorithm.
#'
#' @name iqLearn
#'
#' @param ... ignored. Provided to require named inputs.
#' @param moMain An object of class modelObj or a list of objects of class
#' modelObjSubset, which define the models and R methods to be used to
#' obtain parameter estimates and predictions for the main effects component
#' of the outcome regression. See ?modelObj and/or ?modelObjSubset for
#' details. NULL is an acceptable value if moCont is defined.
#' @param moCont An object of class modelObj or a list of objects of class
#' modelObjSubset, which define the models and R methods to be used to
#' obtain parameter estimates and predictions for the contrasts component
#' of the outcome regression. See ?modelObj and/or ?modelObjSubset for
#' details. NULL is an acceptable value if moMain is defined.
#' @param data A data frame of covariates and treatment history.
#' @param response For the second stage analysis, the response vector.
#' For first stage analyses, the value object returned by iqLearnSS().
#' @param object The value object returned by iqLearFSC()
#' @param txName A character string giving column header of treatment variable
#' in data
#' @param iter An integer. See ?iter for details
#' @param verbose A logical. If TRUE, screen prints are generated.
#'
#' @usage
#'
#' ## Second-Stage Analysis
#' iqLearnSS(..., moMain, moCont, data, response, txName, iter = 0L,
#' verbose = TRUE)
#'
#' ## First-Stage Analysis for Fitted Main Effects
#' iqLearnFSM(..., moMain, moCont, data, response, txName, iter = 0L,
#' verbose = TRUE)
#'
#' ## First-Stage Analysis for Fitted Contrasts
#' iqLearnFSC(..., moMain, moCont, data, response, txName, iter = 0L,
#' verbose = TRUE)
#'
#' ## First-Stage Analysis of Contrast Variance Log-Linear Model
#' iqLearnFSV(..., object, moMain, moCont, data, iter = 0L, verbose = TRUE)
#'
#' @family statistical methods
#' @family multiple decision point methods
#'
#' @include E_class_IQLearnSS.R
#' @aliases iqLearnSS iqLearnFSM iqLearnFSV iqLearnFSC
#'
#' @references Laber, EB, Linn, KA, and Stefanski, LA (2014).
#' Interactive model building for Q-Learning.
#' Biometrika, 101, 831--847. PMCID: PMC4274394.
#'
#' @examples
#'
#' # Load and process data set
#' data(bmiData)
#'
#' # define the negative 12 month change in BMI from baseline
#' y12 <- -100*(bmiData[,6L] - bmiData[,4L])/bmiData[,4L]
#'
#' #### Full Interactive Q-Learning Algorithm
#'
#' ### Second-Stage Analysis
#'
#' # outcome model
#' moMain <- buildModelObj(model = ~parentBMI+month4BMI,
#' solver.method = 'lm')
#'
#' moCont <- buildModelObj(model = ~race + parentBMI+month4BMI,
#' solver.method = 'lm')
#'
#' fitSS <- iqLearnSS(moMain = moMain, moCont = moCont,
#' data = bmiData, response = y12, txName = 'A2')
#'
#' ### First-Stage Analysis Main Effects Term
#'
#' # main effects model
#' moMain <- buildModelObj(model = ~parentBMI+baselineBMI,
#' solver.method = 'lm')
#'
#' moCont <- buildModelObj(model = ~race + parentBMI+baselineBMI,
#' solver.method = 'lm')
#'
#' fitFSM <- iqLearnFSM(moMain = moMain, moCont = moCont,
#' data = bmiData, response = fitSS, txName = 'A1')
#'
#' ### First-Stage Analysis Contrasts Term
#'
#' # contrasts model
#' moMain <- buildModelObj(model = ~parentBMI+baselineBMI,
#' solver.method = 'lm')
#'
#' moCont <- buildModelObj(model = ~race + parentBMI+baselineBMI,
#' solver.method = 'lm')
#'
#' fitFSC <- iqLearnFSC(moMain = moMain, moCont = moCont,
#' data = bmiData, response = fitSS, txName = 'A1')
#'
#' ### First-Stage Analysis Contrasts Variance - Log-linear
#'
#' # contrasts variance model
#' moMain <- buildModelObj(model = ~baselineBMI,
#' solver.method = 'lm')
#'
#' moCont <- buildModelObj(model = ~baselineBMI,
#' solver.method = 'lm')
#'
#' fitFSV <- iqLearnFSV(object = fitFSC, moMain = moMain, moCont = moCont,
#' data = bmiData)
#'
#' ####Available methods
#'
#' ### Estimated value
#' estimator(x = fitFSC, y = fitFSM, z = fitFSV, w = fitSS, dens = 'nonpar')
#'
#' ## Estimated optimal treatment and decision functions for training data
#' ## Second stage optimal treatments
#' optTx(x = fitSS)
#'
#' ## First stage optimal treatments when contrast variance is modeled.
#' optTx(x = fitFSM, y = fitFSC, z = fitFSV, dens = 'nonpar')
#'
#' ## First stage optimal treatments when contrast variance is constant.
#' optTx(x = fitFSM, y = fitFSC, dens = 'nonpar')
#'
#' ## Estimated optimal treatment and decision functions for new data
#' ## Second stage optimal treatments
#' optTx(x = fitSS, bmiData)
#'
#' ## First stage optimal treatments when contrast variance is modeled.
#' optTx(x = fitFSM, y = fitFSC, z = fitFSV, dens = 'nonpar', bmiData)
#'
#' ## First stage optimal treatments when contrast variance is constant.
#' optTx(x = fitFSM, y = fitFSC, dens = 'nonpar', bmiData)
#'
#' ### The following methods are available for all objects: fitSS, fitFSM,
#' ### fitFSC and fitFSV. We include only one here for illustration.
#'
#' # Coefficients of the outcome regression objects
#' coef(object = fitSS)
#'
#' # Description of method used to obtain object
#' DTRstep(object = fitFSM)
#'
#' # Value object returned by outcome regression method
#' fitObject(object = fitFSC)
#'
#' # Value object returned by outcome regression method
#' outcome(object = fitFSV)
#'
#' # Plots if defined by outcome regression method
#' dev.new()
#' par(mfrow = c(2,4))
#'
#' plot(x = fitSS)
#' plot(x = fitSS, suppress = TRUE)
#'
#' # Show main results of method
#' show(object = fitFSM)
#'
#' # Show summary results of method
#' summary(object = fitFSV)
#'
NULL
#' @export
iqLearnSS <- function(...,
moMain,
moCont,
data,
response,
txName,
iter = 0L,
verbose = TRUE) {
# moMain must be either an object of class modelObj or NULL
if (missing(x = moMain)) moMain <- NULL
if (!is(object = moMain, class2 = "modelObj") && !is.null(x = moMain)) {
stop("moMain must be one of {modelObj, NULL}")
}
# moCont must be either an object of class modelObj or NULL
if (missing(x = moCont)) moCont <- NULL
if (!is(object = moCont, class2 = "modelObj") && !is.null(x = moCont)) {
stop("moCont must be one of {modelObj, NULL}")
}
# at least one of {moMain, moCont} must be an object of class modelObj. If
# either is NULL, iterative algorithm is not appropriate.
if (is.null(x = moMain) && is.null(x = moCont)) {
stop("must provide moMain and/or moCont")
} else if (is.null(x = moMain) || is.null(x = moCont)) {
iter <- NULL
}
# data must be provided as a data.frame object.
data <- .verifyDataFrame(data = data)
# response must a vector
response <- .verifyVectorResponse(response = response)
# verify treatment is appropriately coded.
data <- .checkTxData(txName = txName, data = data)
# treatments must be binary
# Note that NAs are allowed
txVec <- .checkBinaryTx(txName = txName, data = data)
if (!isTRUE(x = all.equal(target = txVec, current = data[,txName]))) {
cat("Treatment variable converted to {-1,1}\n")
data[,txName] <- as.integer(x = round(x = txVec))
}
# iter must be a positive integer or NULL
iter <- .verifyIter(iter = iter)
# verbose must be logical
verbose <- .verifyVerbose(verbose = verbose)
result <- .newIQLearnSS(moMain = moMain,
moCont = moCont,
data = data,
response = response,
txName = txName,
iter = iter,
suppress = !verbose)
result@analysis@call <- match.call()
return(result)
}
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