Dear all, Some functions like 'ROC(Epi)' can be used to perform ROC analyssi, but it needs us to specify the fitting model in the argument. Now i have got the predicted p-values (0,1) for the 0/1 response variable using some other approach, see the following example dataset: id mark predict.pvalue 1 1 0.927 2 0 0.928 3 1 0.928 .................. *mark* is the true classes, *predict.pvalue* is the predicted p-values, which was used to determine the predicted classes. So i need to specify some cut points for *predict.pvalue*, and then compare it with *mark*class, generate the 2*2 tables, and then calculate some sensitivity, specifity....statistcs, and ROC curve. I have searched some functions, such as roc(analogue),'ROC(Epi),etc. They may need to specify the fitting model in the codes or group varibles, and may be not appropriate for my condition. I think that it should have been performed in some package for ROC analysis. Anybody can tell me which function is for this case? Thanks very much. -- With Kind Regards, oooO::::::::: (..)::::::::: :\.(:::Oooo:: ::\_)::(..):: :::::::)./::: ::::::(_/:::: ::::::::::::: [***********************************************************************] Zhi Jie,Zhang ,PHD Tel:+86-21-54237149 Dept. of Epidemiology,School of Public Health,Fudan University Address:No. 138 Yi Xue Yuan Road,Shanghai,China Postcode:200032 Email:epistat@gmail.com Website: www.statABC.com [***********************************************************************] oooO::::::::: (..)::::::::: :\.(:::Oooo:: ::\_)::(..):: :::::::)./::: ::::::(_/:::: ::::::::::::: [[alternative HTML version deleted]]
Hi, ZJ, In verification package, there is a function that takes observed response and predicted probability and then calculate the ROC. I am not sure if it is what you are after. On Dec 31, 2007 10:27 AM, zhijie zhang <epistat at gmail.com> wrote:> Dear all, > Some functions like 'ROC(Epi)' can be used to perform ROC analyssi, but it > needs us to specify the fitting model in the argument. Now i have got the > predicted p-values (0,1) for the 0/1 response variable using some other > approach, see the following example dataset: > > id mark predict.pvalue > > 1 1 0.927 > > 2 0 0.928 > > 3 1 0.928 > > .................. > > *mark* is the true classes, *predict.pvalue* is the predicted p-values, > which was used to determine the predicted classes. So i need to specify some > cut points for *predict.pvalue*, and then compare it with *mark*class, > generate the 2*2 tables, and then calculate some sensitivity, > specifity....statistcs, and ROC curve. > I have searched some functions, such as roc(analogue),'ROC(Epi),etc. They > may need to specify the fitting model in the codes or group varibles, > and may be not appropriate for my condition. I think that it should > have been performed in some package for ROC analysis. > Anybody can tell me which function is for this case? > Thanks very much. > -- > With Kind Regards, > > oooO::::::::: > (..)::::::::: > :\.(:::Oooo:: > ::\_)::(..):: > :::::::)./::: > ::::::(_/:::: > ::::::::::::: > [***********************************************************************] > Zhi Jie,Zhang ,PHD > Tel:+86-21-54237149 > Dept. of Epidemiology,School of Public Health,Fudan University > Address:No. 138 Yi Xue Yuan Road,Shanghai,China > Postcode:200032 > Email:epistat at gmail.com > Website: www.statABC.com > [***********************************************************************] > oooO::::::::: > (..)::::::::: > :\.(:::Oooo:: > ::\_)::(..):: > :::::::)./::: > ::::::(_/:::: > ::::::::::::: > > [[alternative HTML version deleted]] > > ______________________________________________ > R-help at r-project.org 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. >-- ==============================WenSui Liu Statistical Project Manager ChoicePoint Precision Marketing (http://spaces.msn.com/statcompute/blog)
zhijie zhang wrote:> Dear all, > Some functions like 'ROC(Epi)' can be used to perform ROC analyssi, but it > needs us to specify the fitting model in the argument. Now i have got the > predicted p-values (0,1) for the 0/1 response variable using some other > approach, see the following example dataset: > > id mark predict.pvalue > > 1 1 0.927 > > 2 0 0.928 > > 3 1 0.928 > > .................. > > *mark* is the true classes, *predict.pvalue* is the predicted p-values, > which was used to determine the predicted classes. So i need to specify some > cut points for *predict.pvalue*, and then compare it with *mark*class, > generate the 2*2 tables, and then calculate some sensitivity, > specifity....statistcs, and ROC curve. > I have searched some functions, such as roc(analogue),'ROC(Epi),etc. They > may need to specify the fitting model in the codes or group varibles, > and may be not appropriate for my condition. I think that it should > have been performed in some package for ROC analysis. > Anybody can tell me which function is for this case? > Thanks very much.Forming the ROC curve can lead to bad statistical practice, e.g., use of non-pre-specified cutpoints and use of cutpoints in general. The area under the ROC curve is a valid measure of predictive discrimination though (even though it cannot be used to compare 2 models as it is not sensitive enough). To get the ROC area you can use the simple somers2 function in the Hmisc package. Frank -- Frank E Harrell Jr Professor and Chair School of Medicine Department of Biostatistics Vanderbilt University