I'm not implying they should be discarded; however, at the same time I'm not
certain I fully understand why we should check the ordinality assumption if
in the end we're going to include predictors with which the response
variable behaves in a non-ordinal fashion.
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One last thing. At the outset of this discussion I provided the results of a
validation procedure on a model (see below). As discussed previously, the
model overall seems to fair well, with the exception of the slope. With
that in mind, is there a way to correct the coefficients of the model to
Dr. Harrell,
Thanks for your response. The predictor variables I initially included in
the model were based on the x mean plots and whether they exhibited
ordinality and whether they appeared to meet the CR assumptions. Only 7 of
16 potential variables fit that designation and those are the
I guess I must be misunderstanding the point of checking the ordinality
assumptions prior to fitting a model. Are you saying that a response
variable that does not behave in an ordinal fashion can still be included in
the initial and final model?
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Does your point about proportionality also hold for ordinality? In other
words, if I have several X variables that do not behave in an ordinal
fashion with Y, should I still include them in the full model? My
understanding or perhaps misunderstanding of the ordinality assumption was
that all X
Hello,
I've recently started using the rms package to fit some continuation ratio
models using cr.setup. The package runs beautifully and I'm getting good
fits with my data, however, I'm having trouble getting plots of the
predicted mean values of y in relation to predictor variables with
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