logitXform: Logit/Log-Odds Transformation

In Rita: Automated Transformations, Normality Testing, and Reporting

Logit/Log-Odds Transformation

Description

This function transforms data via the logit/log-odds transformation.

Usage

logitXform(sample, divisor = 2)

Arguments

sample	The input data (vector, matrix, or dataframe)
divisor	Number used to modify epsilon enroute to the empirical logit, in cases of output consisting of a single distinct value (scalar)

Details

Initially, features of the input data are extracted and used to determine an initial transformation to perform.

All forms of data representing an underlying discrete scale are converted to proportions of the total sample size, if needed. In these cases, values should be stored such that elements are in absolute frequency, relative frequency, or percentage form.

For non-count data, variables are shifted and bounded at [0,1] in a manner analogous to the potential transformations of the scale performed by arcsineXform() prior to the arcine, although transformed values are not expected to outperform more suitable transformations.

Then, the empirical logit transformation is applied to avoid zeroes or ones, and the data are transformed by taking the log-odds/logit of each value.

Value

The logit-transformed data (vector)

References

Stevens, S., Valderas, J. M., Doran, T., Perera, R., & Kontopantelis, E. (2016). Analysing indicators of performance, satisfaction, or safety using empirical logit transformation. bmj, 352.

Osborne, J. W. (2002). Notes on the use of data transformations. Practical Assessment, Research and Evaluation, 9(1), 42-50.

Osborne, J. W. (2002). The Effects of Minimum Values on Data Transformations. Retrieved from https://files.eric.ed.gov/fulltext/ED463313.pdf

Warton, D. I., & Hui, F. K. (2011). The arcsine is asinine: the analysis of proportions in ecology. Ecology, 92(1), 3-10.

Examples

values <- rnorm(100)
x <- logitXform(values)

Rita documentation built on March 18, 2022, 6:36 p.m.