[R] interpreting bootstrap corrected slope [rms package]

[Frank Harrell]

That's not reasonable for 2 reasons.  First, selecting variables based on
apparent assumption satisfaction is an unexplored technique.  Second, you
failed to account for variable selection during resampling validation.  You
will need to give the model all CANDIDATE variables and use the bw=TRUE
option for validate() and calibrate() to get the right answer.  You'll have
to specify the stopping rule too.

If there is a wide range of predicted probabilities then an Emax of 0.05 is
less stressful.  But the Emax is meaningless if you didn't repeat all
modeling steps that used Y for each resampling iteration.

Frank

apeer wrote:
> 
> 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 variables
> I initially included.  I then used backward variable selection, which
> selected 3 significant terms.  Does that seem reasonable?  
> 
> Also, are you saying that if the exceedence probabilites for the middle Y
> category have a wide range then keeping the model as is would be fine for
> future predictions?
> 
> Thanks for your time,
> Adam
> 


-----
Frank Harrell
Department of Biostatistics, Vanderbilt University
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