Hostname: page-component-cb9f654ff-p5m67 Total loading time: 0 Render date: 2025-08-01T10:45:37.446Z Has data issue: false hasContentIssue false
  • English
  • Français

Estimation of Dose–Response Models for Discrete and Continuous Data in Weed Science

Published online by Cambridge University Press:  20 January 2017

William J. Price ,
Bahman Shafii  and
Steven S. Seefeldt
William J. Price
Affiliation:
Statistical Programs, College of Agricultural and Life Sciences, University of Idaho, Moscow, ID 83844
Bahman Shafii*
Affiliation:
Statistical Programs, College of Agricultural and Life Sciences, University of Idaho, Moscow, ID 83844
Steven S. Seefeldt
Affiliation:
USDA-ARS, Subarctic Agricultural Research Unit, University of Alaska Fairbanks, Fairbanks, AK 99775
*
Corresponding author's E-mail: bshafii@uidaho.edu
Get access Rights & Permissions [Opens in a new window]

Abstract

Dose–response analysis is widely used in biological sciences and has application to a variety of risk assessment, bioassay, and calibration problems. In weed science, dose–response methodologies have typically relied on least squares estimation under the assumptions of normal, homoscedastic, and independent errors. Advances in computational abilities and available software, however, have given researchers more flexibility and choices for data analysis when these assumptions are not appropriate. This article will explore these techniques and demonstrate their use to provide researchers with an up-to-date set of tools necessary for analysis of dose–response problems. Demonstrations of the techniques are provided using a variety of data examples from weed science.

El análisis de respuesta a dosis es ampliamente usado en las ciencias biológicas y tiene aplicación a una variedad de problemas de evaluación de riesgo, bioensayos y calibración. En la ciencia de malezas, las metodologías de respuesta a dosis han dependido típicamente de la estimación de los mínimos cuadrados bajo la premisa de que los error son normales, homocedásticos e independientes. Sin embargo, los avances en las habilidades computacionales y la disponibilidad de software han dado a los investigadores más flexibilidad y opciones para analizar datos cuando estas premisas no son apropiadas. Este artículo explora estas técnicas y demuestra su uso con la intención de brindar a los investigadores un grupo actualizado de herramientas necesarias para el análisis de problemas de respuesta a dosis. Se proveen demostraciones de las técnicas usando una variedad de ejemplos de datos para la ciencia de malezas.

Keywords

Alternative model estimationbioassaymaximum likelihoodnonlinear modelstreatment comparison

Information

Type
Special Topics
Information
Weed Technology , Volume 26 , Issue 3 , September 2012 , pp. 587 - 601
Copyright
Copyright © Weed Science Society of America 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

Literature Cited

Bates, D. M. and Watts, D. G. 2007. Nonlinear regression analysis and its applications. New York John Wiley. 392 p.Google Scholar
Bliss, C. I. 1934. The method of probits. Science. 79:38–39.Google Scholar
Box, G. E. P. and Cox, D. R. 1964. An analysis of transformations. J. R. Stat. Soc. Ser. B Stat. Method 26:211–252.Google Scholar
Brain, P. and Cousens, R. 1989. An equation to describe dose responses where there is stimulation of growth at low doses. Weed Res. 29:93–96.Google Scholar
Cameron, A. C. and Trivedi, P. K. 1998. Regression Analysis for Count Data. Cambridge, UK Cambridge University Press. 436 p.Google Scholar
Consul, P. C. 1974. A simple run model dependent upon predetermined strategy. Sankhya Indian J. Stat. Ser. B. 36:391–399.Google Scholar
Davidian, M. and Giltinan, D. M. 1998. Nonlinear Models for Repeated Measurement Data. London CRC. 360 p. (Monographs on Statistics and Applied Probability; vol. 62.).Google Scholar
Finney, D. J. 1971. Probit Analysis. 3rd ed. London Charles Griffin. 333 p.Google Scholar
Fisher, R. A. 1935. The case of zero survivors. Appendix to Bliss, C. L. 1935. The calculation of the dosage–mortality curve. Ann. Appl. Biol. 22:164–165.Google Scholar
Huet, S., Bouvier, A., Poursat, M-A., and Jolivet, E. 2004. Statistical tools for nonlinear regression: a practical guide with S-Plus. New York Springer-Verlag. 246 p.Google Scholar
Knezevic, S. Z., Streibig, J. C., and Ritz, C. 2007. Utilizing R software package for dose–response studies: the concept and data analysis. Weed Technol. 21:840–848.Google Scholar
Littell, R. C., Milliken, G. A., Stroup, W. W., Wolfinger, R. D., Schabenberber, O., and Mukhopadhyay, S. 2006. SAS for Mixed Models. 2nd ed. Cary, NC SAS. 840 p.Google Scholar
Mukhopadhyay, S. 2000. Bayesian nonparametric inference on the dose level with specified response rate. Biometrics. 56:220–226.Google Scholar
Nielson, O. K., Ritz, C., and Streibig, J. C. 2004. Nonlinear mixed-model regression to analyze herbicide dose–response relationships. Weed Technol. 18:30–37.Google Scholar
Pinheiro, J. C. and Bates, D. M. 1995. Approximations to the log-likelihood function in the nonlinear mixed-effects model. J. Comput. Graph. Statist. 4:12–35.Google Scholar
Pinheiro, J. C., Bates, D. M., DebRoy, S., and Sarkar, D. 2011. nlme: linear and nonlinear mixed effects models. Wien, Austria R Development Core Team R, package version 3.1–102.Google Scholar
Price, W. J. and Shafii, B. 2005. Bayesian analysis of dose–response calibration curves. Pages 65–75 in Proceedings of the 17th Annual Kansas State University Conference on Applied Statistics in Agriculture [CDROM], April 25–27, 2005. Manhattan, KS KSU.Google Scholar
Rainbolt, C. R., Thill, C. C., Zemetra, R. S., and Shaner, D. L. 2005. Imidazolinone-resistant wheat acetolactate synthase in vivo response to imazamox. Weed Technol. 19:539–548.Google Scholar
Ranft, R. D., Seefeldt, S. S., Zhang, M., and Barnes, D. L. 2010. Development of a soil bioassay for triclopyr residues and comparison with a laboratory extraction. Weed Technol. 24:538–543.Google Scholar
R Development Core Team. 2011. R: A Language and Environment for Statistical Computing. Vienna, Austria R Foundation for Statistical Computing.Google Scholar
Reed, L. J. and Berkson, J. 1929. The application of the logistic function to experimental data. J. Phys. Chem. 33:760–779.Google Scholar
Ritz, C. 2010. Toward a unified approach to dose–response modeling in ecotoxicology. Environmental Toxicology and Chem. 29:220–229.Google Scholar
Ritz, C., Cedergreen, N., Jensen, J. E., and Streibig, J. C. 2006. Relative potency in nonsimilar dose–response curves. Weed Sci. 54:407–412.Google Scholar
Ritz, C. and Streibig, J. C. 2005. Bioassay analysis using R. J. Stat. Softw. 12:1–22.Google Scholar
Ritz, C. and Streibig, J. C. 2008. Nonlinear Regression with R. New York Springer-Verlag. 148 p.Google Scholar
SAS Institute. 2009. SAS OnlineDoc, Version 9.2. Cary, NC SAS.Google Scholar
Seber, G. A. F. and Wild, C. J. 2003. Nonlinear Regression. New York John Wiley. 792 p.Google Scholar
Seefeldt, S. S., Jensen, J. E., and Fuerst, P. 1995. Log-logistic analysis of herbicide dose–response relationships. Weed Technol. 9:218–227.Google Scholar
Shafii, B. and Price, W. J. 2001. Estimation of cardinal temperatures in germination data analysis. J. Agric. Biol. Environ. Stat. 6:356–366.Google Scholar
Shafii, B. and Price, W. J. 2006. Bayesian approaches to dose–response calibration models. In Proceedings of the XXIII International Biometrics Conference [CDROM], July 16–21, 2006, Montreal, Canada. Washington, DC International Biometric Society. [Abstract].Google Scholar
Streibig, J. C., Rudemo, M., and Jensen, J. E. 1993. Dose–response curves and statistical models. Pages 29–55 in Streibig, J. C. and Kudsk, P. eds. Herbicide Bioassays. Boca Raton, FL CRC. 288 p.Google Scholar