regression models for ordinal responses a review of methods

Regression models for ordinal responses a review of methods

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Unformatted text preview: mptions), as was the case in our data. Tests for model assumptions could also be viewed as goodness-of-fit tests of the link functions; Holtbrugge and Schumacher19 provide a detailed review of such tests. Since the formulation of the logit functions in the proportional odds and the partial-proportional odds model are identical (i.e. 4 ° versus 1° – 3° plus no laceration, 3° – 4° versus 1° – 2° plus no laceration, etc), the overall fit of these models are comparable. The proportional odds model can be viewed as a model ‘nested’ within the unconstrained partial-proportional odds model. The deviance14 (defined as the difference in the likelihood ratios between two nested models) is χ2 = 36.9 (89.8 – 52.9) with 2 d.f. (4 – 2), favouring the unconstrained partial-proportional odds model as a better fit to the data. Applying the same argument, the deviance comparing the likelihood ratios between the unconstrained and the constrained partial proportional odds models is χ2 = 4.2 (89.8 – 85.6) with 2 d.f. (4 ...
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This document was uploaded on 02/25/2014.

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