Re: [R] selecting cut-off in Logistic regression using ROCR package

From: Frank E Harrell Jr <f.harrell_at_vanderbilt.edu>
Date: Sat, 16 Jun 2007 09:03:32 -0500

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

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Received on Sat 16 Jun 2007 - 14:09:52 GMT

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