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# October 30, 2018
#' @export
iqLearnFSV <- function(...,
object,
moMain,
moCont,
data,
iter = 0L,
verbose = TRUE) {
if (!is(object = object, class2 = 'IQLearnFS_C')) {
stop("object must be an object returned by iqLearnFSC()")
}
# 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)
# treatments must be binary
# Note that NAs are allowed
txName <- .getTxName(object = object@analysis)
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 <- .newIQLearnFS_VHet(object = object,
moMain = moMain,
moCont = moCont,
data = data,
iter = iter,
suppress = !verbose)
result@analysis@call <- match.call()
return( result )
}
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