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.

Usage

no_trans

logit_trans

Fisher_trans

ln_trans

inv_trans

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$func(.5)
#> [1] 0
logit_trans$inv(0)
#> [1] 0.5