transformations: Simple Transformation Functions

Description Usage Format Details Examples

Description

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

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Format

An object of class list of length 2.

An object of class list of length 2.

An object of class list of length 2.

An object of class list of length 2.

An object of class list of length 2.

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

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logit_trans$func(.5)
logit_trans$inv(0)

topepo/tidyposterior documentation built on July 5, 2020, 11:11 p.m.