Fitting probability distributions using R software and its application in medicine

Document Type : Original Paper

Authors

1 Department of Statistics, Hakim Sabzevari University, Sabzevar, Iran

2 Mehr-e-Madar Hospital, Torbat Jam Faculty of Medical Sciences, Torbat Jam, Iran

Abstract

Researchers in different disciplines often face phenomena of random nature. Sometimes it is possible to use probability distributions to describe and predict such phenomena. Each distribution has a number of unknown parameters, whose values are estimated from data. In some problems, there are a few competing distributions for fitting to a data set. In this setup, selecting a suitable model based on some criteria is necessary. This article introduces facilities of R statistical software in performing the above steps. Application of the discussed methods is illustrated using a medical data set.

Keywords

Main Subjects

References

[1] Borlinhas, F., Loução, R., Conceição, R.C., and Ferreira, H.A. (2019). Gamma Distribution Model in the Evaluation of Breast Cancer Through Diffusion-Weighted MRI: A Preliminary Study. Journal of Magnetic Resonance Imaging 50, 230-238.
[2] Cullen, A.C., and Frey, H.C. (1999). Probabilistic Techniques in Exposure Assessment (1st Edition). Plenum Publishing Co.
[3] Delignette-Muller, M.L., and Dutang, C. (2015). fitdistrplus: An R Package for Fitting Distributions. Journal of Statistical Software 64, 1–34.
[4] Neti, P.V.S.V., and Howell, R.W. (2006). Log Normal Distribution of Cellular Uptake of Radioactivity: Implications for Biologic Responses to Radiopharmaceuticals. Journal of Nuclear Medicine 47, 1049-1058.
[5] Parker, K.J., and Poul, S.S. (2020). Burr, Lomax, Pareto, and Logistic Distributions from Ultrasound Speckle. Ultrasonic Imaging 42, 203-212.
[6] Sharma, V.K., Singh, S.K., Singh, U., and Agiwal, V. (2015) The inverse Lindley distribution: a stress-strength reliability model with application to head and neck cancer data. Journal of Industrial and Production Engineering 32, 162-173.
[7] Skriver, M.V., Væth, M., and Støvring, H. (2018). Loss of life expectancy derived from a standardized mortality ratio in Denmark, Finland, Norway and Sweden. Scandinavian Journal of Public Health 46, 767-773.
[8] Tse, Y.K. (2009). Nonlife Actuarial Models: Theory, Methods and Evaluation. International Series on Actuarial Science (1st Edition). Cambridge University Press.
[9] Vose, D. (2010). Quantitative Risk Analysis: A Guide to Monte Carlo Simulation Modelling (1st Edition). John Wiley & Sons.
[10] Wang, R., He, N., Wang, Y., and Lu, K. (2020). Adaptively weighted nonlocal means and TV minimization for speckle reduction in SAR images. Multimedia Tools and Applications 79, 7633–7647.

Files

History

  • Receive Date: 10 September 2022
  • Revise Date: 17 February 2023
  • Accept Date: 20 February 2023

Share

How to cite

Statistics