[R] Interpreting the example given by Frank Harrell in the predict.lrm {Design} help

[John Haart]

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    y=best
> 1 0.3199710 0.3560355 0.3239935
> 2 0.4153257 0.3437086 0.2409657
> 
> predict mean(y) using codes 1,2,3
> 
> 
>>  predict(f, d, type='mean', codes=TRUE)
> 
>    1        2 
> 2.004022 1.825640 

How do i use this information  to assign x1 and x2 into a category on the response scale (good,better,best?)

Thanks

John




On 1 Oct 2010, at 12:14, Frank Harrell wrote:


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 classify an observation, and unless the category's
probability is high, this may be dangerous.  You might do well to consider a
more smooth approach such as using the generalized roc area (C-index) or its
related rank correlation measure Dxy.  Also there are odds ratios.

Frank

















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