PanJen: An R package for ranking transformations in a linear regression

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PanJen : An R package for ranking transformations in a linear regression. / Jensen, Cathrine Ulla; Panduro, Toke Emil.

I: The R Journal, Bind 10, Nr. 1, 2018, s. 109-121.

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt

Harvard

Jensen, CU & Panduro, TE 2018, 'PanJen: An R package for ranking transformations in a linear regression', The R Journal, bind 10, nr. 1, s. 109-121. <https://journal.r-project.org/archive/2018/RJ-2018-018/index.html>

APA

Jensen, C. U., & Panduro, T. E. (2018). PanJen: An R package for ranking transformations in a linear regression. The R Journal, 10(1), 109-121. https://journal.r-project.org/archive/2018/RJ-2018-018/index.html

Vancouver

Jensen CU, Panduro TE. PanJen: An R package for ranking transformations in a linear regression. The R Journal. 2018;10(1):109-121.

Author

Jensen, Cathrine Ulla ; Panduro, Toke Emil. / PanJen : An R package for ranking transformations in a linear regression. I: The R Journal. 2018 ; Bind 10, Nr. 1. s. 109-121.

Bibtex

@article{f47858ac3da34aacb1e95b3d6af580d5,
title = "PanJen: An R package for ranking transformations in a linear regression",
abstract = "PanJen is an R-package for ranking transformations in linear regressions. It provides users with the ability to explore the relationship between a dependent variable and its independent variables. The package offers an easy and data-driven way to choose a functional form in multiple linear regression models by comparing a range of parametric transformations. The parametric functional forms are benchmarked against each other and a non-parametric transformation. The package allows users to generate plots that show the relation between a covariate and the dependent variable. Furthermore, PanJen will enable users to specify specific functional transformations, driven by a priori and theory-based hypotheses. The package supplies both model fits and plots that allow users to make informed choices on the functional forms in their regression. We show that the ranking in PanJen outperforms the Box-Tidwell transformation, especially in the presence of inefficiency, heteroscedasticity or endogeneity.",
author = "Jensen, {Cathrine Ulla} and Panduro, {Toke Emil}",
year = "2018",
language = "English",
volume = "10",
pages = "109--121",
journal = "The R Journal",
issn = "2073-4859",
publisher = "R Foundation for Statistical Computing",
number = "1",

}

RIS

TY - JOUR

T1 - PanJen

T2 - An R package for ranking transformations in a linear regression

AU - Jensen, Cathrine Ulla

AU - Panduro, Toke Emil

PY - 2018

Y1 - 2018

N2 - PanJen is an R-package for ranking transformations in linear regressions. It provides users with the ability to explore the relationship between a dependent variable and its independent variables. The package offers an easy and data-driven way to choose a functional form in multiple linear regression models by comparing a range of parametric transformations. The parametric functional forms are benchmarked against each other and a non-parametric transformation. The package allows users to generate plots that show the relation between a covariate and the dependent variable. Furthermore, PanJen will enable users to specify specific functional transformations, driven by a priori and theory-based hypotheses. The package supplies both model fits and plots that allow users to make informed choices on the functional forms in their regression. We show that the ranking in PanJen outperforms the Box-Tidwell transformation, especially in the presence of inefficiency, heteroscedasticity or endogeneity.

AB - PanJen is an R-package for ranking transformations in linear regressions. It provides users with the ability to explore the relationship between a dependent variable and its independent variables. The package offers an easy and data-driven way to choose a functional form in multiple linear regression models by comparing a range of parametric transformations. The parametric functional forms are benchmarked against each other and a non-parametric transformation. The package allows users to generate plots that show the relation between a covariate and the dependent variable. Furthermore, PanJen will enable users to specify specific functional transformations, driven by a priori and theory-based hypotheses. The package supplies both model fits and plots that allow users to make informed choices on the functional forms in their regression. We show that the ranking in PanJen outperforms the Box-Tidwell transformation, especially in the presence of inefficiency, heteroscedasticity or endogeneity.

M3 - Journal article

VL - 10

SP - 109

EP - 121

JO - The R Journal

JF - The R Journal

SN - 2073-4859

IS - 1

ER -

ID: 202947838