[R] Checking the proportional odds assumption holds in an ordinal logistic regression using polr function
rune.haubo at gmail.com
Wed Nov 26 18:56:32 CET 2014
On 26 November 2014 at 17:55, Charlotte Whitham
<charlotte.whitham at gmail.com> wrote:
> Dear Rune,
> Thank you for your prompt reply and it looks like the ordinal package could be the answer I was looking for!
> If you don't mind, I'd also like to know please what to do if the tests show the proportional odds assumption is NOT met. (Unfortunately I notice effects from almost all variables that breach the proportional odds assumption in my dataset)
That depends almost entirely on the purpose of the analysis and is not
a topic fit for email - consulting a local statistician is probably
sound advice... Yet: With enough data these tests can be sensitive
beyond practical significance; if the 'proportional' part of the
effect explains the majority of the deviance, perhaps the proportional
odds model provides a reasonably good description of the main
structures in the data anyway. On the other hand, if the magnitude
(not significance!) of the non-proportional effects are large, perhaps
a cumulative link model is not the right kind of model structure and
you should be looking at alternative approaches in your analysis.
> Would you recommend a multinomial logistic model? Or re-scaling of the data?
> Thank you for your time,
> Best wishes,
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