Bootstrapping Pseudo - R<sup>2</sup> Measures for Binary Response Variable Model

Zakariya Yahya Algamal; Haithem Taha Mohammad Ali

doi:doi:10.11648/j.bsi.20170203.13

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Bootstrapping Pseudo - R² Measures for Binary Response Variable Model

Zakariya Yahya Algamal, Haithem Taha Mohammad Ali

Published in Biomedical Statistics and Informatics (Volume 2, Issue 3)

Received: 7 March 2017 Accepted: 16 March 2017 Published: 31 March 2017

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Abstract

Statistical inference is based generally on some estimates that are functions of the data. Bootstrapping procedure offers strategies to estimate or approximate the sampling distribution of a statistic. Logistics regression model with binary response is commonly used. This paper focuses on the behavior of bootstrapping pseudo - R² measures in logistic regression model. Simulation and real data results also presented. We conclude and suggest to use either R²_M or R²_D, since they have convergence in there values.

Published in	Biomedical Statistics and Informatics (Volume 2, Issue 3)
DOI	10.11648/j.bsi.20170203.13
Page(s)	107-110
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Logistic Regression, Pseudo - R², Bootstrap, Logit, Propit

References

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[8]	Harrell, F., E., (2001) "Regression Modeling Strategies: with Applications to Linear Models, Logistic Regression and Survival Analysis", Springer-Velag, Inc., USA
[9]	Horowitz, J., L., and Savin, N., E. (2001), "Binary Response Models: Logits, Probits and Semiparametrics", Journal of Economic Perspectives, Vol. 15, No. 4, pp. 43-56.
[10]	Hosmer, D., W. and Lemeshow, S., (2000), "Applied Logistic Regression" 2^nd ed., John Wiley & Sons, Inc., New York.
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[12]	Long, S., J. (1997) "Regression Models for Categorical and Limited Dependent Variables", SAGE Publication, USA.
[13]	McFadden, D. (1974), "Conditional Logit Analysis of Qualitative Choice Behavior, in P. Zarembka (ed.), Frontiers in Econometrics, New York: Academic Press.
[14]	Mittlebick, M., and Heinzl, H., (2003), "Pseudo- Measures for Generalized Linear Models", 1^st European Workshop on the Assessment of Diagnostic Performance, pp. 71-80.
[15]	Algamal, Z. Y. and Lee, M. H., (2015),” Penalized logistic regression with the adaptive LASSO for gene selection in high-dimensional cancer classification”, Expert Systems with Applications, Vol. 42, pp. 9326–9332.
[16]	Algamal, Z., Y. and Lee, M., H., (2015), “Regularized logistic regression with adjusted adaptive elastic net for gene selection in high dimensional cancer classification”, Computers in Biology and Medicine, Vol. 67, pp. 136–145.
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APA Style

Zakariya Yahya Algamal, Haithem Taha Mohammad Ali. (2017). Bootstrapping Pseudo - R2 Measures for Binary Response Variable Model. Biomedical Statistics and Informatics, 2(3), 107-110. https://doi.org/10.11648/j.bsi.20170203.13

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ACS Style

Zakariya Yahya Algamal; Haithem Taha Mohammad Ali. Bootstrapping Pseudo - R2 Measures for Binary Response Variable Model. Biomed. Stat. Inform. 2017, 2(3), 107-110. doi: 10.11648/j.bsi.20170203.13

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AMA Style

Zakariya Yahya Algamal, Haithem Taha Mohammad Ali. Bootstrapping Pseudo - R2 Measures for Binary Response Variable Model. Biomed Stat Inform. 2017;2(3):107-110. doi: 10.11648/j.bsi.20170203.13

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@article{10.11648/j.bsi.20170203.13,
  author = {Zakariya Yahya Algamal and Haithem Taha Mohammad Ali},
  title = {Bootstrapping Pseudo - R2 Measures for Binary Response Variable Model},
  journal = {Biomedical Statistics and Informatics},
  volume = {2},
  number = {3},
  pages = {107-110},
  doi = {10.11648/j.bsi.20170203.13},
  url = {https://doi.org/10.11648/j.bsi.20170203.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.bsi.20170203.13},
  abstract = {Statistical inference is based generally on some estimates that are functions of the data. Bootstrapping procedure offers strategies to estimate or approximate the sampling distribution of a statistic. Logistics regression model with binary response is commonly used. This paper focuses on the behavior of bootstrapping pseudo - R2 measures in logistic regression model. Simulation and real data results also presented. We conclude and suggest to use either R2M or R2D, since they have convergence in there values.},
 year = {2017}
}

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TY  - JOUR
T1  - Bootstrapping Pseudo - R2 Measures for Binary Response Variable Model
AU  - Zakariya Yahya Algamal
AU  - Haithem Taha Mohammad Ali
Y1  - 2017/03/31
PY  - 2017
N1  - https://doi.org/10.11648/j.bsi.20170203.13
DO  - 10.11648/j.bsi.20170203.13
T2  - Biomedical Statistics and Informatics
JF  - Biomedical Statistics and Informatics
JO  - Biomedical Statistics and Informatics
SP  - 107
EP  - 110
PB  - Science Publishing Group
SN  - 2578-8728
UR  - https://doi.org/10.11648/j.bsi.20170203.13
AB  - Statistical inference is based generally on some estimates that are functions of the data. Bootstrapping procedure offers strategies to estimate or approximate the sampling distribution of a statistic. Logistics regression model with binary response is commonly used. This paper focuses on the behavior of bootstrapping pseudo - R2 measures in logistic regression model. Simulation and real data results also presented. We conclude and suggest to use either R2M or R2D, since they have convergence in there values.
VL  - 2
IS  - 3
ER  -

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Author Information

Zakariya Yahya Algamal

Department of Statistics and Informatics, University of Mosul, Mosul, Iraq
Haithem Taha Mohammad Ali

College of Computers and Information Technology, Nawroz University, Kurdistan Region, Iraq

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APA Style

Zakariya Yahya Algamal, Haithem Taha Mohammad Ali. (2017). Bootstrapping Pseudo - R2 Measures for Binary Response Variable Model. Biomedical Statistics and Informatics, 2(3), 107-110. https://doi.org/10.11648/j.bsi.20170203.13

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ACS Style

Zakariya Yahya Algamal; Haithem Taha Mohammad Ali. Bootstrapping Pseudo - R2 Measures for Binary Response Variable Model. Biomed. Stat. Inform. 2017, 2(3), 107-110. doi: 10.11648/j.bsi.20170203.13

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AMA Style

Zakariya Yahya Algamal, Haithem Taha Mohammad Ali. Bootstrapping Pseudo - R2 Measures for Binary Response Variable Model. Biomed Stat Inform. 2017;2(3):107-110. doi: 10.11648/j.bsi.20170203.13

Copy | Download

@article{10.11648/j.bsi.20170203.13,
  author = {Zakariya Yahya Algamal and Haithem Taha Mohammad Ali},
  title = {Bootstrapping Pseudo - R2 Measures for Binary Response Variable Model},
  journal = {Biomedical Statistics and Informatics},
  volume = {2},
  number = {3},
  pages = {107-110},
  doi = {10.11648/j.bsi.20170203.13},
  url = {https://doi.org/10.11648/j.bsi.20170203.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.bsi.20170203.13},
  abstract = {Statistical inference is based generally on some estimates that are functions of the data. Bootstrapping procedure offers strategies to estimate or approximate the sampling distribution of a statistic. Logistics regression model with binary response is commonly used. This paper focuses on the behavior of bootstrapping pseudo - R2 measures in logistic regression model. Simulation and real data results also presented. We conclude and suggest to use either R2M or R2D, since they have convergence in there values.},
 year = {2017}
}

Copy | Download

TY  - JOUR
T1  - Bootstrapping Pseudo - R2 Measures for Binary Response Variable Model
AU  - Zakariya Yahya Algamal
AU  - Haithem Taha Mohammad Ali
Y1  - 2017/03/31
PY  - 2017
N1  - https://doi.org/10.11648/j.bsi.20170203.13
DO  - 10.11648/j.bsi.20170203.13
T2  - Biomedical Statistics and Informatics
JF  - Biomedical Statistics and Informatics
JO  - Biomedical Statistics and Informatics
SP  - 107
EP  - 110
PB  - Science Publishing Group
SN  - 2578-8728
UR  - https://doi.org/10.11648/j.bsi.20170203.13
AB  - Statistical inference is based generally on some estimates that are functions of the data. Bootstrapping procedure offers strategies to estimate or approximate the sampling distribution of a statistic. Logistics regression model with binary response is commonly used. This paper focuses on the behavior of bootstrapping pseudo - R2 measures in logistic regression model. Simulation and real data results also presented. We conclude and suggest to use either R2M or R2D, since they have convergence in there values.
VL  - 2
IS  - 3
ER  -

Copy | Download