R help - Jun 2007 - selecting cut-off in Logistic regression using ROCR package

Hi,

I am using logistic regression to classify a binary psychometric data. using
glm() and then predict.glm() i got the predicted odds ratio of the testing
data. Next i am going to plot ROC curve for the analysis of my study.

Now what i will do:

1. first select a cut-off (say 0.4) and classify the output of predict.glm()
into {0,1} segment and then use it to draw ROC curve using ROCR package 

OR

2. just use the predicted odds ratio in ROCR package to get "error
rate" and
use the minimum error rate (as new cut-off) to draw new ROC curve.

waiting for reply.

with regards and thanks.

Tirtha.
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Tirthadeep wrote:
> 
> Hi,
> 
> I am using logistic regression to classify a binary psychometric data.
using
> glm() and then predict.glm() i got the predicted odds ratio of the testing
> data. Next i am going to plot ROC curve for the analysis of my study.
> 
> Now what i will do:
> 
> 1. first select a cut-off (say 0.4) and classify the output of
predict.glm()
> into {0,1} segment and then use it to draw ROC curve using ROCR package 
> 
> OR
> 
> 2. just use the predicted odds ratio in ROCR package to get "error
rate" and
> use the minimum error rate (as new cut-off) to draw new ROC curve.
> 
> waiting for reply.
> 
> with regards and thanks.
> 
> Tirtha.
It's not clear why any cutoff or ROC curve is needed.  Please give us 
more information about why a continuous variable should be dichotomized, 
and read

@Article{roy06dic,
   author = 		 {Royston, Patrick and Altman, Douglas G. and
Sauerbrei, Willi},
   title = 		 {Dichotomizing continuous predictors in multiple
regression: a bad idea},
   journal = 	 Stat in Med,
   year = 		 2006,
   volume =		 25,
   pages =		 {127-141},
   annote =		 {continuous
covariates;dichotomization;categorization;regression;efficiency;clinical
research;residual confounding;destruction of statistical inference
when cutpoints are chosen using the response variable;varying effect
estimates from change in cutpoints;difficult to interpret effects
when dichotomize;nice plot showing effect of categorization;PBC data}
}

Frank

-- 
Frank E Harrell Jr   Professor and Chair           School of Medicine
                      Department of Biostatistics   Vanderbilt University