[R] logistic regression model with non-integer weights
Frank E Harrell Jr
f.harrell at vanderbilt.edu
Sun Apr 16 21:51:35 CEST 2006
Ramón Casero Cañas wrote:
> Michael Dewey wrote:
>
>>At 17:12 09/04/06, RamÃ³n Casero CaÃ±as wrote:
>>
>>I am not sure what the problem you really want to solve is but it seems
>>that
>>a) abnormality is rare
>>b) the logistic regression predicts it to be rare.
>>If you want a prediction system why not try different cut-offs (other
>>than 0.5 on the probability scale) and perhaps plot sensitivity and
>>specificity to help to choose a cut-off?
>
>
> Thanks for your suggestions, Michael. It took me some time to figure out
> how to do this in R (as trivial as it may be for others). Some comments
> about what I've done follow, in case anyone is interested.
>
> The problem is a) abnormality is rare (Prevalence=14%) and b) there is
> not much difference in the independent variable between abnormal and
> normal. So the logistic regression model predicts that P(abnormal) <=
> 0.4. I got confused with this, as I expected a cut-off point of P=0.5 to
> decide between normal/abnormal. But you are right, in that another
> cut-off point can be chosen.
>
> For a cut-off of e.g. P(abnormal)=0.15, Sensitivity=65% and
> Specificity=52%. They are pretty bad, although for clinical purposes I
> would say that Positive/Negative Predictive Values are more interesting.
> But then PPV=19% and NPV=90%, which isn't great. As an overall test of
> how good the model is for classification I have computed the area under
> the ROC, from your suggestion of using Sensitivity and Specificity.
>
> I couldn't find how to do this directly with R, so I implemented it
> myself (it's not difficult but I'm new here). I tried with package ROCR,
> but apparently it doesn't cover binary outcomes.
>
> The area under the ROC is 0.64, so I would say that even though the
> model seems to fit the data, it just doesn't allow acceptable
> discrimination, not matter what the cut-off point.
>
>
> I have also studied the effect of low prevalence. For this, I used
> option ran.gen in the boot function (package boot) to define a function
> that resamples the data so that it balances abnormal and normal cases.
>
> A logistic regression model is fitted to each replicate, to a parametric
> bootstrap, and thus compute the bias of the estimates of the model
> coefficients, beta0 and beta1. This shows very small bias for beta1, but
> a rather large bias for beta0.
>
> So I would say that prevalence has an effect on beta0, but not beta1.
> This is good, because a common measure like the odds ratio depends only
> on beta1.
>
> Cheers,
>
This makes me think you are trying to go against maximum likelihood to
optimize an improper criterion. Forcing a single cutpoint to be chosen
seems to be at the heart of your problem. There's nothing wrong with
using probabilities and letting the utility possessor make the final
decision.
--
Frank E Harrell Jr Professor and Chair School of Medicine
Department of Biostatistics Vanderbilt University
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