Effects under Linear, Logistic and Poisson Regression Models with Transformed Variables

Computation of effects under linear, logistic and Poisson regression models with transformed variables. Logarithm and power transformations are allowed. Effects can be displayed both numerically and graphically in both the original and the transformed space of the variables. The methods are described in Barrera-Gomez and Basagana (2015) .

tlm

Variables in regression models are frequently transformed to achieve homogeneity of variance, normality of errors, linearization of associations, or a more homogeneous distribution of predictors. This package is a tool to fit linear, logistic, and Poisson regression models with logarithmic or power transformations. The package also show how to report and interpret effects in the original scale of the variables.

Getting started

• The last version released on CRAN can be installed within an R session by executing:

install.packages("tlm")

• The package tlm is available on the Comprehensive R Archive Network (CRAN), with info at the related web page https://CRAN.R-project.org/package=tlm.

• Once the package has been installed, a summary of the main functions is available by executing:

help(package = "tlm")

• A comprehensive tutorial, including a number of detailed examples, is available by executing:

vignette("tlm")

References

The methodology used in the package is described in

• Barrera-Gómez J, Basagaña X. Models with transformed variables: Interpretation and software. Epidemiology. 2015;26(2):e16-17. DOI: 10.10.1097/EDE.0000000000000247. URL: https://journals.lww.com/epidem/Fulltext/2015/03000/Models_with_Transformed_Variables__Interpretation.27.aspx