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European Journal of Plant Pathology

, Volume 102, Issue 9, pp 847–854 | Cite as

Calibration and verification of risk algorithms using logistic regression

  • Jonathan Yuen
  • Eva Twengström
  • Roland Sigvald
Research Articles

Abstract

The use of logistic regression is proposed as a method of verifying and calibrating disease risk algorithms. The logistic regression model calculates the log of the odds of a binary outcome as a function of a linear combination of predictors. The resulting model assumes a multiplicative (relative) relationship between the different risk factors. Computer programs for performing logistic regression produce both estimates and standard errors, thus permitting the evaluation of the importance of different predictive variables. The use of receiver operating characteristic (ROC) curves is also proposed as a means of comparing different algorithms. An example is presented using data on Sclerotinia stem rot in oil seed rape, caused bySclerotinia sclerotiorum.

Keywords

Standard Error Regression Model Linear Combination Receiver Operating Characteristic Logistic Regression Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Kluwer Academic Publishers 1996

Authors and Affiliations

  • Jonathan Yuen
    • 1
    • 2
  • Eva Twengström
    • 3
  • Roland Sigvald
    • 3
  1. 1.Department of Cancer EpidemiologyUppsala University, University HospitalUppsalaSweden
  2. 2.Department of Plant PathologySwedish University of Agricultural SciencesUppsalaSweden
  3. 3.Research Information CentreSwedish University of Agricultural SciencesUppsalaSweden

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