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 incre
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 g
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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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
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
Departme
--Original Message-
> From: r-help-boun...@r-project.org [mailto:r-help-boun...@r-
> project.org] On Behalf Of peterfran...@me.com
> Sent: Friday, October 01, 2010 8:23 AM
> To: Frank Harrell
> Cc: r-help@r-project.org
> Subject: Re: [R] Interpreting the example given by Frank
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
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
--
View this message in context:
http://r.789695.n4.nabble.com/Interpreting-the-example-given-by-Frank-Harrell
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=better
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 to
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 re
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