Dear List and Frank,
I have calculated the log-odds for my models but maybe i am not getting
something but i am not understanding how for a categorical factor this helps?
On all the examples i have see it relates to continuous factors where moving
from one number to another shows either a
I may be missing a point, but the proportional odds model easily gives you
odds ratios for Y=j (independent of j by PO assumption). Other options
include examining a rank correlation between the linear predictor and Y, or
(if Y is numeric and spacings between categories are meaningful) you can
Thanks Frank and Greg,
This makes alot more sense to me now. I appreciate you are both very busy, but
i was wondering if i could trouble you for one last piece of advice. As my data
is a little complicated for a first effort at R let alone modelling!
The response is on a range from 1-6, which
You still seem to be hung up on making arbitrary classifications. Instead,
look at tendencies using odds ratios or rank correlation measures. My book
Regression Modeling Strategies covers this.
Frank
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Frank Harrell
Department of Biostatistics, Vanderbilt University
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Dear list,
I am relatively new to ordinal models and have been working through the example
given by Frank Harrell in the predict.lrm {Design} help
All of this makes sense to me, except for the responses, i,e how do i interpret
them? i would be extremely grateful if someone could explain the
John,
Don't conclude that one category is the most probable when its probability
of being equaled or exceeded is a maximum. The first category would always
be the winner if that were the case.
When you say y=best remember that you are dealing with a probability model.
Nothing is forcing you
Frank,
Thats great thanks for the advice, i appreciate that brier score, AUC etc are a
better method of validation and discrimination but when it comes to
predictions of new data
d - data.frame(x1=c(.1,.5),x2=c(.5,.15))
predict(f, d, type=fitted.ind)
y=good y=bettery=best
Why assign them at all? Is this a forced choice at gunpoint problem?
Remember what probabilities mean.
Frank
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Frank Harrell
Department of Biostatistics, Vanderbilt University
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The reason I am trying to assign them is because I have a data set where i have
arrived at the most likely model that describes the data and now I have
another dataset where I know the factors but not the response.
Therefore, surely I need to assign the predicted values to a response in order
I have this discussion fairly often with doctors that I work with. The issue
is that you can certainly predict from a model, but you can predict on
different scales. Let's consider the simpler case of just 2 outcomes (disease
yes/no):
Let's say you have 4 patients that you want to predict
Well put Greg. The job of the statistician is to produce good estimates
(probabilities in this case). Those cannot be translated into action
without subject-specific utility functions. Classification during the
analysis or publication stage is not necessary.
Frank
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Frank Harrell
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