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# October 25, 2018
#' Class \code{TypedFit_fSet}
#'
#' Class \code{TypedFit_fSet} is a \code{TypedFit} when subsets are identified
#' but not modeled independently.
#'
#' @name TypedFit_fSet-class
#'
#' @include C_TypedFit.R
#'
#' @keywords internal
setClass(Class = "TypedFit_fSet",
contains = c("TxObj","TypedFit"))
##########
## METHODS
##########
#' Methods Available for Objects of Class \code{TypedFit_fSet}
#'
#' Methods call equivalently named methods defined for \code{TypedFit}
#' objects.
#'
#' @name TypedFit_fSet-methods
#'
#' @keywords internal
NULL
#' @rdname newTypedFit
setMethod(f = ".newTypedFit",
signature = c(modelObj = "modelObj",
txObj = "TxInfoWithSubsets"),
definition = function(modelObj,
txObj,
data,
response,
type,
suppress) {
# this combination of modelObj and TxInfoWithSubsets
# is only used when singletons are not included in
# models. When singletons are included in models,
# ModelObjSubset must be used.
singles <- .getSingleton(object = txObj)
if (all(singles) ) stop("no data provided")
if (!suppress) {
cat(sum(!singles), "included in analysis\n")
}
txNew <- .newTxObj(fSet = NULL,
txName = .getTxName(object = txObj),
data = data[!singles,],
suppress = TRUE,
verify = FALSE)
fitObj <- .newTypedFit(modelObj = modelObj,
txObj = txNew,
data = data[!singles,],
response = response[!singles],
type = type,
suppress = suppress)
result <- new(Class = "TypedFit_fSet", txObj, fitObj)
return( result )
})
#' \code{coef(object)}
#' retrieves the estimated coefficients.
#'
#' @rdname TypedFit_fSet-methods
setMethod(f = "coef",
signature = c(object = "TypedFit_fSet"),
definition = function(object, ...) {
return( coef(object = as(object = object, Class = "TypedFit")) )
})
#' \code{fitObject(object)}
#' retrieves the regression objects.
#'
#' @rdname TypedFit_fSet-methods
setMethod(f = "fitObject",
signature = c(object = "TypedFit_fSet"),
definition = function(object, ...) {
return( fitObject(object = as(object = object,
Class = "TypedFit")) )
})
#' \code{plot(x, ...)}
#' calls plot method(s) for a regression object.
#'
#' @rdname TypedFit_fSet-methods
setMethod(f = "plot",
signature = c(x = "TypedFit_fSet"),
definition = function(x, suppress=FALSE, ...) {
plot(x = as(object = x, Class = "TypedFit"), suppress, ...)
})
#' \code{predict(object, ...)}
#' calls predict method for the regression object. Patients with only 1
#' tx option are NA.
#'
#' \code{predict(object, ...)}
#' Patients with only 1 tx option are NA.
#'
#' @rdname TypedFit_fSet-methods
setMethod(f = "predict",
signature = c(object = "TypedFit_fSet"),
definition = function(object, newdata, ...) {
if (!missing(newdata)) {
txNew <- .newTxObj(fSet = .getSubsetRule(object = object@txInfo),
txName = .getTxName(object = object@txInfo),
data = newdata,
suppress = TRUE,
verify = FALSE)
# this combination of modelObj and TxInfoWithSubsets
# is only used when singletons are not included in
# models; and thus they should not be sent to prediction methods
singles <- .getSingleton(object = txNew)
pred <- predict(object = as(object = object, Class = "TypedFit"),
newdata = newdata[!singles,,drop=FALSE], ...)
} else {
# this combination of modelObj and TxInfoWithSubsets
# is only used when singletons are not included in
# models; they were not included in the original fit so do
# do not need to be excluded from predict
singles <- .getSingleton(object = object@txInfo)
pred <- predict(object = as(object = object, Class = "TypedFit"))
}
if (is.null(x = ncol(x = pred))) {
pred <- matrix(data = pred, ncol = 1L)
}
vals <- matrix(data = NA,
nrow = length(x = singles),
ncol = ncol(x = pred))
vals[!singles,] <- pred
if (ncol(x = vals) == 1L) vals <- drop(x = vals)
return( vals )
})
#' \code{print(x)}
#'
#' @rdname TypedFit_fSet-methods
setMethod(f = "print",
signature = c(x = "TypedFit_fSet"),
definition = function(x, ...) {
print(x = as(object = x, Class = "TypedFit"))
})
#' \code{show(object)}
#'
#' @rdname TypedFit_fSet-methods
setMethod(f = "show",
signature = c(object = "TypedFit_fSet"),
definition = function(object) {
show(object = as(object = object, Class = "TypedFit"))
})
#' \code{summary(object)}
#' calls summary method(s) for regression object.
#'
#' @rdname TypedFit_fSet-methods
setMethod(f = "summary",
signature = c(object = "TypedFit_fSet"),
definition = function(object, ...) {
return( summary(object = as(object = object,Class = "TypedFit")) )
})
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