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Utilizing R Software Package for Dose-Response Studies: The Concept and Data Analysis

Published online by Cambridge University Press:  20 January 2017

Stevan Z. Knezevic ,
Jens C. Streibig  and
Christian Ritz
Stevan Z. Knezevic*
Affiliation:
Haskell Agricultural Laboratory, University of Nebraska, 57905 866 Rd., Concord, NE 68728–2828
Jens C. Streibig
Affiliation:
Department of Agricultural Sciences and Department of Natural Sciences, The Royal Veterinary and Agricultural University, Thorvaldsensvej 40, DK-1871 Frederiksberg C, Denmark
Christian Ritz
Affiliation:
Department of Agricultural Sciences and Department of Natural Sciences, The Royal Veterinary and Agricultural University, Thorvaldsensvej 40, DK-1871 Frederiksberg C, Denmark
*
Corresponding author's E-mail: sknezevic2@unl.edu
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Abstract

Advances in statistical software allow statistical methods for nonlinear regression analysis of dose-response curves to be carried out conveniently by non-statisticians. One such statistical software is the program R with the drc extension package. The drc package can: (1) simultaneously fit multiple dose-response curves; (2) compare curve parameters for significant differences; (3) calculate any point along the curve at the response level of interest, commonly known as an effective dose (e.g., ED30, ED50, ED90), and determine its significance; and (4) generate graphs for publications or presentations. We believe that the drc package has advantages that include: the ability to relatively simply and quickly compare multiple curves and select ED-levels easily along the curve with relevant statistics; the package is free of charge and does not require licensing fees, and the size of the package is only 70 MB. Therefore, our objectives are to: (1) provide a review of a few common issues in dose-response-curve fitting, and (2) facilitate the use of up-to-date statistical techniques for analysis of dose-response curves with this software. The methods described can be utilized to evaluate chemical and non-chemical weed control options. Benefits to the practitioners and academics are also presented.

Keywords

nonlinear regressionexperimental designweed dry matter

Information

Type
Teaching/Education
Information
Weed Technology , Volume 21 , Issue 3 , September 2007 , pp. 840 - 848
Copyright
Copyright © Weed Science Society of America 

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