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
______________________________________________
[email protected] mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
