期刊
WEED TECHNOLOGY
卷 26, 期 3, 页码 587-601出版社
WEED SCI SOC AMER
DOI: 10.1614/WT-D-11-00101.1
关键词
Alternative model estimation; bioassay; maximum likelihood; nonlinear models; treatment comparison
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.
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