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#' Simple Transformation Functions
#'
#' A set of objects are contained here to easily facilitate the
#' use of outcome transformations for modeling. For example, if
#' there is a large amount of variability in the resampling results
#' for the Kappa statistics, which lies between -1 and 1, assuming
#' normality may produce posterior estimates outside of the natural
#' bound. One way to solve this is to use a link function or assume
#' a prior that is appropriately bounded. Another approach is to
#' transform the outcome values prior to modeling using a Gaussian
#' prior and reverse-transforming the posterior estimates prior to
#' visualization and summarization. These object can help
#' facilitate this last approach.
#'
#' @details The `logit_trans` object is useful for model
#' performance statistics bounds in zero and one, such as accuracy
#' or the area under the ROC curve.
#'
#' `ln_trans` and `inv_trans` can be useful when the statistics
#' are right-skewed and strictly positive.
#'
#' `Fisher_trans` was originally used for correlation statistics
#' but can be used here for an metrics falling between -1 and 1,
#' such as Kappa.
#'
#' @examples
#' logit_trans$func(.5)
#' logit_trans$inv(0)
#' @rdname transformations
#' @export
no_trans <- list(
func = function(x) x,
inv = function(x) x
)
#' @rdname transformations
#' @export
logit_trans <- list(
func = binomial()$linkfun,
inv = binomial()$linkinv
)
#' @rdname transformations
#' @export
Fisher_trans <- list(func = atanh, inv = tanh)
#' @rdname transformations
#' @export
ln_trans <- list(func = log, inv = exp)
#' @rdname transformations
#' @export
inv_trans <- list(
func = function(x) 1 / x,
inv = function(x) 1 / x
)
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