Fitting Models of Vulnerability to Toxicity with Generalized Linear Models

Ganiyu Abayomi Dawodu

doi:doi:10.11648/j.ijdsa.20170305.13

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Fitting Models of Vulnerability to Toxicity with Generalized Linear Models

Ganiyu Abayomi Dawodu

Published in International Journal of Data Science and Analysis (Volume 3, Issue 5)

Received: 21 August 2017 Accepted: 15 September 2017 Published: 8 November 2017

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Abstract

People are often exposed to toxic or hazardous (e.g. radioactive radon and lead) elements and rays, without even knowing so. Toxicity often results from an individual’s prolonged exposure to toxic substances. A thorough examination of some individuals’ blood or urine samples for the quantities of hazardous substances or elements, often gives a multivariate data (i.e. matrix of cases against elements) on toxicity. The pertinent response variable is often binary response (or count data) type and hence the Generalized Linear Models (GLM) of it can be fitted using our proposed techniques. This paper purports to identify models in GLM that can be used to study toxicity when it is ‘captured’ as count data or Binary Response Variables (BRV). An illustration of how the techniques work is done by using a sample of data on some artisans.

Published in	International Journal of Data Science and Analysis (Volume 3, Issue 5)
DOI	10.11648/j.ijdsa.20170305.13
Page(s)	46-57
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), 2017. Published by Science Publishing Group

Keywords

GLM, Exploratory Data Analysis (EDA), BRV, Count Data (CD), Toxicity, R

References

[1]	Babalola O O, Okonji R E, Atoyebi J O, Sennuga T F, Raimi M M, Ejim-Eze, E E Adeniran O A, Akinsiku O T, Areola J O, John O O and Odebunmi S O (2010). Distribution of lead in selected organs and tissues of albino rats exposed to acute lead toxicity. Scientific Research and Essay Vol. 5(9), pp. 845-848, Available online at http://www.academicjournals.org/SRE, ISSN 1992-2248 © 2010 Academic Journals
[2]	Calcagno V and Mazancourt C (2010). glmulti: An R Package for easy automated model selection with (generalized) linear models. Journal of statistical software. Volume 34, Issue 12. http://www.jstatsoft.org/
[3]	Chege, M W, Rathore, I V S, Chhabra, S C and Mustapha, A O (2009). The influence of meteorological parameters on indoor radon in selected traditional Kenyan dwellings. J. Radiol. Prot. 29 (2009) 95–103.
[4]	Dawodu G A, Asiribo O E, Adelakun A A, Ozoje M O, Ademuyiwa O and Akinwale T A (2011). On the vulnerability of the Blood of some Artisans to Toxicity. Journal of Environmental Statistics. December, 2011, volume 2, Issue 4. http://www.jenstat.org
[5]	Dawodu G A (2012). The Derivation of some statistical Models for studying the effects of accidental and occupational pollution. Unpublished PhD (statistics) Thesis submitted to the Department of Statistics, College of Natural Sciences, Federal University of Agriculture, Abeokuta (FUNAAB).
[6]	Dawodu, G A, Alatise, O O and Mustapha, A O (2015). Statistical Analysis of Temporal Variations in Indoor Radon Data using an Adapted Response Surface Method. Journal of Natural Science, Engineering and Technology, FUNAAB 14(1):1-12.
[7]	Dawodu, G. A. and Mustapha, A. O. (2015). Hierarchical Modelling of Indoor and Outdoor (Residential) Radon Data (RRD). Journal of Natural Sciences, Engineering and Technology, Volume 14 (formally ASSET: An International Journal (Series B)). Published by FUNAAB. Nigeria. (letter of acceptance dated 6th February, 2015).
[8]	Kleinbaum D G (1990). Logistic Regression: A self-learning Text. Statistics in the Health sciences. Springer verlag, New York Nriagu J, Afeiche M, Linder A, Arowolo T, Ana G, Mynepalli K C S, Oloruntoba E O, Obi E, Ebenebe J C, Orisakwe O E and Adesina A (2008). Lead poisoning associated with malaria in children of urban areas of Nigeria. International Journal of Hygiene and Environmental Health. doi:10.1016/j.ijheh.2008.05.001.
[9]	Ramola, R C, Kandari, M S, Negi, M S and Choubey, V M (2000). A Study of Diurnal Variation of Indoor Radon Concentrations. Journal of Health Physics 35(2); 211-216.
[10]	Seftelis, L, Nicolaou, G and Trassanidis, S, Tsagas, F N (2007). Diurnal Variation of Radon Progeny. Journal of Environmental Radioactivity 97, 116-123.
[11]	Turner H (2008). Introduction to Generalized Linear Models. Lecture note at the ESRC National Centre for Research Methods, UK and Department of statistics, University of Warwick, UK. WU, 2008-04-22-24.

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Ganiyu Abayomi Dawodu. (2017). Fitting Models of Vulnerability to Toxicity with Generalized Linear Models. International Journal of Data Science and Analysis, 3(5), 46-57. https://doi.org/10.11648/j.ijdsa.20170305.13

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ACS Style

Ganiyu Abayomi Dawodu. Fitting Models of Vulnerability to Toxicity with Generalized Linear Models. Int. J. Data Sci. Anal. 2017, 3(5), 46-57. doi: 10.11648/j.ijdsa.20170305.13

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AMA Style

Ganiyu Abayomi Dawodu. Fitting Models of Vulnerability to Toxicity with Generalized Linear Models. Int J Data Sci Anal. 2017;3(5):46-57. doi: 10.11648/j.ijdsa.20170305.13

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@article{10.11648/j.ijdsa.20170305.13,
  author = {Ganiyu Abayomi Dawodu},
  title = {Fitting Models of Vulnerability to Toxicity with Generalized Linear Models},
  journal = {International Journal of Data Science and Analysis},
  volume = {3},
  number = {5},
  pages = {46-57},
  doi = {10.11648/j.ijdsa.20170305.13},
  url = {https://doi.org/10.11648/j.ijdsa.20170305.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdsa.20170305.13},
  abstract = {People are often exposed to toxic or hazardous (e.g. radioactive radon and lead) elements and rays, without even knowing so. Toxicity often results from an individual’s prolonged exposure to toxic substances. A thorough examination of some individuals’ blood or urine samples for the quantities of hazardous substances or elements, often gives a multivariate data (i.e. matrix of cases against elements) on toxicity. The pertinent response variable is often binary response (or count data) type and hence the Generalized Linear Models (GLM) of it can be fitted using our proposed techniques. This paper purports to identify models in GLM that can be used to study toxicity when it is ‘captured’ as count data or Binary Response Variables (BRV). An illustration of how the techniques work is done by using a sample of data on some artisans.},
 year = {2017}
}

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TY  - JOUR
T1  - Fitting Models of Vulnerability to Toxicity with Generalized Linear Models
AU  - Ganiyu Abayomi Dawodu
Y1  - 2017/11/08
PY  - 2017
N1  - https://doi.org/10.11648/j.ijdsa.20170305.13
DO  - 10.11648/j.ijdsa.20170305.13
T2  - International Journal of Data Science and Analysis
JF  - International Journal of Data Science and Analysis
JO  - International Journal of Data Science and Analysis
SP  - 46
EP  - 57
PB  - Science Publishing Group
SN  - 2575-1891
UR  - https://doi.org/10.11648/j.ijdsa.20170305.13
AB  - People are often exposed to toxic or hazardous (e.g. radioactive radon and lead) elements and rays, without even knowing so. Toxicity often results from an individual’s prolonged exposure to toxic substances. A thorough examination of some individuals’ blood or urine samples for the quantities of hazardous substances or elements, often gives a multivariate data (i.e. matrix of cases against elements) on toxicity. The pertinent response variable is often binary response (or count data) type and hence the Generalized Linear Models (GLM) of it can be fitted using our proposed techniques. This paper purports to identify models in GLM that can be used to study toxicity when it is ‘captured’ as count data or Binary Response Variables (BRV). An illustration of how the techniques work is done by using a sample of data on some artisans.
VL  - 3
IS  - 5
ER  -

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Author Information

Ganiyu Abayomi Dawodu

Statistics Department, College of Physical Sciences, Federal University of Agriculture, Abeokuta (FUNAAB), Nigeria

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APA Style

Ganiyu Abayomi Dawodu. (2017). Fitting Models of Vulnerability to Toxicity with Generalized Linear Models. International Journal of Data Science and Analysis, 3(5), 46-57. https://doi.org/10.11648/j.ijdsa.20170305.13

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ACS Style

Ganiyu Abayomi Dawodu. Fitting Models of Vulnerability to Toxicity with Generalized Linear Models. Int. J. Data Sci. Anal. 2017, 3(5), 46-57. doi: 10.11648/j.ijdsa.20170305.13

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AMA Style

Ganiyu Abayomi Dawodu. Fitting Models of Vulnerability to Toxicity with Generalized Linear Models. Int J Data Sci Anal. 2017;3(5):46-57. doi: 10.11648/j.ijdsa.20170305.13

Copy | Download

@article{10.11648/j.ijdsa.20170305.13,
  author = {Ganiyu Abayomi Dawodu},
  title = {Fitting Models of Vulnerability to Toxicity with Generalized Linear Models},
  journal = {International Journal of Data Science and Analysis},
  volume = {3},
  number = {5},
  pages = {46-57},
  doi = {10.11648/j.ijdsa.20170305.13},
  url = {https://doi.org/10.11648/j.ijdsa.20170305.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdsa.20170305.13},
  abstract = {People are often exposed to toxic or hazardous (e.g. radioactive radon and lead) elements and rays, without even knowing so. Toxicity often results from an individual’s prolonged exposure to toxic substances. A thorough examination of some individuals’ blood or urine samples for the quantities of hazardous substances or elements, often gives a multivariate data (i.e. matrix of cases against elements) on toxicity. The pertinent response variable is often binary response (or count data) type and hence the Generalized Linear Models (GLM) of it can be fitted using our proposed techniques. This paper purports to identify models in GLM that can be used to study toxicity when it is ‘captured’ as count data or Binary Response Variables (BRV). An illustration of how the techniques work is done by using a sample of data on some artisans.},
 year = {2017}
}

Copy | Download

TY  - JOUR
T1  - Fitting Models of Vulnerability to Toxicity with Generalized Linear Models
AU  - Ganiyu Abayomi Dawodu
Y1  - 2017/11/08
PY  - 2017
N1  - https://doi.org/10.11648/j.ijdsa.20170305.13
DO  - 10.11648/j.ijdsa.20170305.13
T2  - International Journal of Data Science and Analysis
JF  - International Journal of Data Science and Analysis
JO  - International Journal of Data Science and Analysis
SP  - 46
EP  - 57
PB  - Science Publishing Group
SN  - 2575-1891
UR  - https://doi.org/10.11648/j.ijdsa.20170305.13
AB  - People are often exposed to toxic or hazardous (e.g. radioactive radon and lead) elements and rays, without even knowing so. Toxicity often results from an individual’s prolonged exposure to toxic substances. A thorough examination of some individuals’ blood or urine samples for the quantities of hazardous substances or elements, often gives a multivariate data (i.e. matrix of cases against elements) on toxicity. The pertinent response variable is often binary response (or count data) type and hence the Generalized Linear Models (GLM) of it can be fitted using our proposed techniques. This paper purports to identify models in GLM that can be used to study toxicity when it is ‘captured’ as count data or Binary Response Variables (BRV). An illustration of how the techniques work is done by using a sample of data on some artisans.
VL  - 3
IS  - 5
ER  -

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