Hi,
I need to build ROC curve in R, can you please provide data steps / code or guide me through it. Thanks and Regards Rithesh M Mohan [[alternative HTML version deleted]] ______________________________________________ [hidden email] 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. |
http://search.r-project.org/cgi-bin/namazu.cgi?query=ROC&max=20&result=normal&sort=score&idxname=Rhelp02a&idxname=functions&idxname=docs there is a lot of help try help.search("ROC curve") gave Help files with alias or concept or title matching 'ROC curve' using fuzzy matching: granulo(ade4) Granulometric Curves plot.roc(analogue) Plot ROC curves and associated diagnostics roc(analogue) ROC curve analysis colAUC(caTools) Column-wise Area Under ROC Curve (AUC) DProc(DPpackage) Semiparametric Bayesian ROC curve analysis cv.enet(elasticnet) Computes K-fold cross-validated error curve for elastic net ROC(Epi) Function to compute and draw ROC-curves. lroc(epicalc) ROC curve cv.lars(lars) Computes K-fold cross-validated error curve for lars roc.demo(TeachingDemos) Demonstrate ROC curves by interactively building one HTH see the help and examples those will suffice Type 'help(FOO, package = PKG)' to inspect entry 'FOO(PKG) TITLE'. Regards, Gaurav Yadav +++++++++++ Assistant Manager, CCIL, Mumbai (India) Mob: +919821286118 Email: [hidden email] Bhagavad Gita: Man is made by his Belief, as He believes, so He is "Rithesh M. Mohan" <[hidden email]> Sent by: [hidden email] 07/26/2007 11:26 AM To <[hidden email]> cc Subject [R] ROC curve in R Hi, I need to build ROC curve in R, can you please provide data steps / code or guide me through it. Thanks and Regards Rithesh M Mohan [[alternative HTML version deleted]] ______________________________________________ [hidden email] 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. ============================================================================================ DISCLAIMER AND CONFIDENTIALITY CAUTION:\ \ This message and ...{{dropped}} ______________________________________________ [hidden email] 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. |
In reply to this post by Rithesh M. Mohan
You might also want to try the ROCR package (http://rocr.bioinf.mpi-sb.mpg.de/).
Tutorial slides: http://rocr.bioinf.mpi-sb.mpg.de/ROCR_Talk_Tobias_Sing.ppt Overview paper: http://bioinformatics.oxfordjournals.org/cgi/content/full/21/20/3940 Good luck, Tobias On 7/26/07, Rithesh M. Mohan <[hidden email]> wrote: > Hi, > > > > I need to build ROC curve in R, can you please provide data steps / code > or guide me through it. > > > > Thanks and Regards > > Rithesh M Mohan > > > [[alternative HTML version deleted]] > > ______________________________________________ > [hidden email] 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. > -- Tobias Sing Computational Biology and Applied Algorithmics Max Planck Institute for Informatics Saarbrucken, Germany Phone: +49 681 9325 315 Fax: +49 681 9325 399 http://www.tobiassing.net ______________________________________________ [hidden email] 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. |
In reply to this post by Gaurav Yadav
Note that even though the ROC curve as a whole is an interesting
'statistic' (its area is a linear translation of the Wilcoxon-Mann-Whitney-Somers-Goodman-Kruskal rank correlation statistics), each individual point on it is an improper scoring rule, i.e., a rule that is optimized by fitting an inappropriate model. Using curves to select cutoffs is a low-precision and arbitrary operation, and the cutoffs do not replicate from study to study. Probably the worst problem with drawing an ROC curve is that it tempts analysts to try to find cutoffs where none really exist, and it makes analysts ignore the whole field of decision theory. Frank Harrell [hidden email] wrote: > http://search.r-project.org/cgi-bin/namazu.cgi?query=ROC&max=20&result=normal&sort=score&idxname=Rhelp02a&idxname=functions&idxname=docs > > there is a lot of help try help.search("ROC curve") gave > Help files with alias or concept or title matching 'ROC curve' using fuzzy > matching: > > > > granulo(ade4) Granulometric Curves > plot.roc(analogue) Plot ROC curves and associated > diagnostics > roc(analogue) ROC curve analysis > colAUC(caTools) Column-wise Area Under ROC Curve > (AUC) > DProc(DPpackage) Semiparametric Bayesian ROC > curve analysis > cv.enet(elasticnet) Computes K-fold cross-validated > error curve for elastic net > ROC(Epi) Function to compute and draw > ROC-curves. > lroc(epicalc) ROC curve > cv.lars(lars) Computes K-fold cross-validated > error curve for lars > roc.demo(TeachingDemos) Demonstrate ROC curves by > interactively building one > > HTH > see the help and examples those will suffice > > Type 'help(FOO, package = PKG)' to inspect entry 'FOO(PKG) TITLE'. > > > > Regards, > > Gaurav Yadav > +++++++++++ > Assistant Manager, CCIL, Mumbai (India) > Mob: +919821286118 Email: [hidden email] > Bhagavad Gita: Man is made by his Belief, as He believes, so He is > > > > "Rithesh M. Mohan" <[hidden email]> > Sent by: [hidden email] > 07/26/2007 11:26 AM > > To > <[hidden email]> > cc > > Subject > [R] ROC curve in R > > > > > > > Hi, > > > > I need to build ROC curve in R, can you please provide data steps / code > or guide me through it. > > > > Thanks and Regards > > Rithesh M Mohan > > > [[alternative HTML version deleted]] > Frank E Harrell Jr Professor and Chair School of Medicine Department of Biostatistics Vanderbilt University ______________________________________________ [hidden email] 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.
Frank Harrell
Department of Biostatistics, Vanderbilt University |
On Thursday 26 July 2007 06:01, Frank E Harrell Jr wrote:
> Note that even though the ROC curve as a whole is an interesting > 'statistic' (its area is a linear translation of the > Wilcoxon-Mann-Whitney-Somers-Goodman-Kruskal rank correlation > statistics), each individual point on it is an improper scoring rule, > i.e., a rule that is optimized by fitting an inappropriate model. Using > curves to select cutoffs is a low-precision and arbitrary operation, and > the cutoffs do not replicate from study to study. Probably the worst > problem with drawing an ROC curve is that it tempts analysts to try to > find cutoffs where none really exist, and it makes analysts ignore the > whole field of decision theory. > > Frank Harrell Frank, This thread has caught may attention for a couple reasons, possibly related to my novice-level experience. 1. in a logistic regression study, where i am predicting the probability of the response being 1 (for example) - there exists a continuum of probability values - and a finite number of {1,0} realities when i either look within the original data set, or with a new 'verification' data set. I understand that drawing a line through the probabilities returned from the logistic regression is a loss of information, but there are times when a 'hard' decision requiring prediction of {1,0} is required. I have found that the ROCR package (not necessarily the ROC Curve) can be useful in identifying the probability cutoff where accuracy is maximized. Is this an unreasonable way of using logistic regression as a predictor? 2. The ROC curve can be a helpful way of communicating false positives / false negatives to other users who are less familiar with the output and interpretation of logistic regression. 3. I have been using the area under the ROC Curve, kendall's tau, and cohen's kappa to evaluate the accuracy of a logistic regression based prediction, the last two statistics based on a some probability cutoff identified before hand. How does the topic of decision theory relate to some of the circumstances described above? Is there a better way to do some of these things? Cheers, Dylan > > [hidden email] wrote: > > http://search.r-project.org/cgi-bin/namazu.cgi?query=ROC&max=20&result=no > >rmal&sort=score&idxname=Rhelp02a&idxname=functions&idxname=docs > > > > there is a lot of help try help.search("ROC curve") gave > > Help files with alias or concept or title matching 'ROC curve' using > > fuzzy matching: > > > > > > > > granulo(ade4) Granulometric Curves > > plot.roc(analogue) Plot ROC curves and associated > > diagnostics > > roc(analogue) ROC curve analysis > > colAUC(caTools) Column-wise Area Under ROC > > Curve (AUC) > > DProc(DPpackage) Semiparametric Bayesian ROC > > curve analysis > > cv.enet(elasticnet) Computes K-fold cross-validated > > error curve for elastic net > > ROC(Epi) Function to compute and draw > > ROC-curves. > > lroc(epicalc) ROC curve > > cv.lars(lars) Computes K-fold cross-validated > > error curve for lars > > roc.demo(TeachingDemos) Demonstrate ROC curves by > > interactively building one > > > > HTH > > see the help and examples those will suffice > > > > Type 'help(FOO, package = PKG)' to inspect entry 'FOO(PKG) TITLE'. > > > > > > > > Regards, > > > > Gaurav Yadav > > +++++++++++ > > Assistant Manager, CCIL, Mumbai (India) > > Mob: +919821286118 Email: [hidden email] > > Bhagavad Gita: Man is made by his Belief, as He believes, so He is > > > > > > > > "Rithesh M. Mohan" <[hidden email]> > > Sent by: [hidden email] > > 07/26/2007 11:26 AM > > > > To > > <[hidden email]> > > cc > > > > Subject > > [R] ROC curve in R > > > > > > > > > > > > > > Hi, > > > > > > > > I need to build ROC curve in R, can you please provide data steps / code > > or guide me through it. > > > > > > > > Thanks and Regards > > > > Rithesh M Mohan > > > > > > [[alternative HTML version deleted]] > > - > Frank E Harrell Jr Professor and Chair School of Medicine > Department of Biostatistics Vanderbilt University > > ______________________________________________ > [hidden email] 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. ______________________________________________ [hidden email] 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. |
Dylan Beaudette wrote:
> On Thursday 26 July 2007 06:01, Frank E Harrell Jr wrote: >> Note that even though the ROC curve as a whole is an interesting >> 'statistic' (its area is a linear translation of the >> Wilcoxon-Mann-Whitney-Somers-Goodman-Kruskal rank correlation >> statistics), each individual point on it is an improper scoring rule, >> i.e., a rule that is optimized by fitting an inappropriate model. Using >> curves to select cutoffs is a low-precision and arbitrary operation, and >> the cutoffs do not replicate from study to study. Probably the worst >> problem with drawing an ROC curve is that it tempts analysts to try to >> find cutoffs where none really exist, and it makes analysts ignore the >> whole field of decision theory. >> >> Frank Harrell > > Frank, > > This thread has caught may attention for a couple reasons, possibly related to > my novice-level experience. > > 1. in a logistic regression study, where i am predicting the probability of > the response being 1 (for example) - there exists a continuum of probability > values - and a finite number of {1,0} realities when i either look within the > original data set, or with a new 'verification' data set. I understand that > drawing a line through the probabilities returned from the logistic > regression is a loss of information, but there are times when a 'hard' > decision requiring prediction of {1,0} is required. I have found that the > ROCR package (not necessarily the ROC Curve) can be useful in identifying the > probability cutoff where accuracy is maximized. Is this an unreasonable way > of using logistic regression as a predictor? Logistic regression (with suitable attention to not assuming linearity and to avoiding overfitting) is a great way to estimate P[Y=1]. Given good predicted P[Y=1] and utilities (losses, costs) for incorrect positive and negative decisions, an optimal decision is one that optimizes expected utility. The ROC curve does not play a direct role in this regard. If per-subject utilities are not available, the analyst may make various assumptions about utilities (including the unreasonable but often used assumption that utilities do not vary over subjects) to find a cutoff on P[Y=1]. A very nice feature of P[Y=1] is that error probabilities are self-contained. For example if P[Y=1] = .02 for a single subject and you predict Y=0, the probability of an error is .02 by definition. One doesn't need to compute an overall error probability over the whole distribution of subjects' risks. If the cost of a false negative is C, the expected cost is .02*C in this example. > > 2. The ROC curve can be a helpful way of communicating false positives / false > negatives to other users who are less familiar with the output and > interpretation of logistic regression. What is more useful than that is a rigorous calibration curve estimate to demonstrate the faithfulness of predicted P[Y=1] and a histogram showing the distribution of predicted P[Y=1]. Models that put a lot of predictions near 0 or 1 are the most discriminating. Calibration curves and risk distributions are easier to explain than ROC curves. Too often a statistician will solve for a cutoff on P[Y=1], imposing her own utility function without querying any subjects. > > > 3. I have been using the area under the ROC Curve, kendall's tau, and cohen's > kappa to evaluate the accuracy of a logistic regression based prediction, the > last two statistics based on a some probability cutoff identified before > hand. ROC area (equiv. to Wilcoxon-Mann-Whitney and Somers' Dxy rank correlation between pred. P[Y=1] and Y) is a measure of pure discrimination, not a measure of accuracy per se. Rank correlation (concordance) measures do not require the use of cutoffs. > > > How does the topic of decision theory relate to some of the circumstances > described above? Is there a better way to do some of these things? See above re: expected loses/utilities. Good questions. Frank > > Cheers, > > Dylan > > > >> [hidden email] wrote: >>> http://search.r-project.org/cgi-bin/namazu.cgi?query=ROC&max=20&result=no >>> rmal&sort=score&idxname=Rhelp02a&idxname=functions&idxname=docs >>> >>> there is a lot of help try help.search("ROC curve") gave >>> Help files with alias or concept or title matching 'ROC curve' using >>> fuzzy matching: >>> >>> >>> >>> granulo(ade4) Granulometric Curves >>> plot.roc(analogue) Plot ROC curves and associated >>> diagnostics >>> roc(analogue) ROC curve analysis >>> colAUC(caTools) Column-wise Area Under ROC >>> Curve (AUC) >>> DProc(DPpackage) Semiparametric Bayesian ROC >>> curve analysis >>> cv.enet(elasticnet) Computes K-fold cross-validated >>> error curve for elastic net >>> ROC(Epi) Function to compute and draw >>> ROC-curves. >>> lroc(epicalc) ROC curve >>> cv.lars(lars) Computes K-fold cross-validated >>> error curve for lars >>> roc.demo(TeachingDemos) Demonstrate ROC curves by >>> interactively building one >>> >>> HTH >>> see the help and examples those will suffice >>> >>> Type 'help(FOO, package = PKG)' to inspect entry 'FOO(PKG) TITLE'. >>> >>> >>> >>> Regards, >>> >>> Gaurav Yadav >>> +++++++++++ >>> Assistant Manager, CCIL, Mumbai (India) >>> Mob: +919821286118 Email: [hidden email] >>> Bhagavad Gita: Man is made by his Belief, as He believes, so He is >>> >>> >>> >>> "Rithesh M. Mohan" <[hidden email]> >>> Sent by: [hidden email] >>> 07/26/2007 11:26 AM >>> >>> To >>> <[hidden email]> >>> cc >>> >>> Subject >>> [R] ROC curve in R >>> >>> >>> >>> >>> >>> >>> Hi, >>> >>> >>> >>> I need to build ROC curve in R, can you please provide data steps / code >>> or guide me through it. >>> >>> >>> >>> Thanks and Regards >>> >>> Rithesh M Mohan >>> >>> >>> [[alternative HTML version deleted]] >> - >> Frank E Harrell Jr Professor and Chair School of Medicine >> Department of Biostatistics Vanderbilt University >> ______________________________________________ [hidden email] 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.
Frank Harrell
Department of Biostatistics, Vanderbilt University |
In reply to this post by Rithesh M. Mohan
Hi Ritesh ***please note Ritesh always mark a copy to the R-help mailing list :) *** Please visit this link to get help in R http://rocr.bioinf.mpi-sb.mpg.de/ROCR_Talk_Tobias_Sing.ppt#384,8,Examples (2/8): Precision/recall curves futher :) what do you mean by PSA and cohort :) after some googling i got this coÂ·hort(khÃ´rt) n. 1. A group or band of people. 2. A companion or associate. 3. A generational group as defined in demographics, statistics, or market research: "The cohort of people aged 30 to 39 . . . were more conservative" American Demographics. 4. a. One of the 10 divisions of a Roman legion, consisting of 300 to 600 men. b. A group of soldiers. and for PSA i got Prostate-specific antigen. A substance produced by the prostate that may be found in an increased amount in the blood of men who have prostate cancer, benign prostatic hyperplasia, or infection or inflammation of the prostate. Now please clarify what you want to model :) please dont take it otherwise i am not from biology field. Please clarify :) Regards, Gaurav Yadav +++++++++++ Assistant Manager, CCIL, Mumbai (India) Mob: +919821286118 Email: [hidden email] Bhagavad Gita: Man is made by his Belief, as He believes, so He is "Rithesh M. Mohan" <[hidden email]> 07/30/2007 01:30 PM To <[hidden email]> cc Subject Re: [R] ROC curve in R Hi Gaurav, Need your help, Iâm relatively new to R or even stats, so can you please give me step by step details to get ROC curve in R. Requirement. To build ROC curve using only PSA(variable) alone of the original cohort against the ROC of the Model of the original cohort. It would be really great if you could help me with this. Thanks and Regards Rithesh ============================================================================================ DISCLAIMER AND CONFIDENTIALITY CAUTION: This message and any attachments with it (the "message") are confidential and intended solely for the addressees. Unauthorized reading, copying, dissemination, distribution or disclosure either whole or partial, is prohibited. If you receive this message in error, please delete it and immediately notify the sender. Communicating through email is not secure and capable of interception, corruption and delays. Anyone communicating with The Clearing Corporation of India Limited (CCIL) by email accepts the risks involved and their consequences. The internet can not guarantee the integrity of this message. CCIL shall (will) not therefore be liable for the message if modified. The recipient should check this email and any attachments for the presence of viruses. CCIL accepts no liability for any damage caused by any virus transmitted by this email. [[alternative HTML version deleted]] ______________________________________________ [hidden email] 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. |
In reply to this post by Rithesh M. Mohan
Sorry Gaurav,
I'll make sure I mark a copy to r-help also. As I have told, I'm new to R and even to statistics, so it will take some time for me to learn it. Just help me get a simple ROC curve, please give an example of your own and explain the steps, no mater if its biology or any other field, I just need to get the logic behind it. Thanks & Regards Rithesh M Mohan ________________________________ From: [hidden email] [mailto:[hidden email]] Sent: Monday, July 30, 2007 4:28 PM To: Rithesh M. Mohan Cc: [hidden email] Subject: Re: [R] ROC curve in R Hi Ritesh ***please note Ritesh always mark a copy to the R-help mailing list :) *** Please visit this link to get help in R http://rocr.bioinf.mpi-sb.mpg.de/ROCR_Talk_Tobias_Sing.ppt#384,8,Examples (2/8): Precision/recall curves futher :) what do you mean by PSA and cohort :) after some googling i got this co·hort(khôrt) n. 1. A group or band of people. 2. A companion or associate. 3. A generational group as defined in demographics, statistics, or market research: "The cohort of people aged 30 to 39 . . . were more conservative" American Demographics. 4. a. One of the 10 divisions of a Roman legion, consisting of 300 to 600 men. b. A group of soldiers. and for PSA i got Prostate-specific antigen. A substance produced by the prostate that may be found in an increased amount in the blood of men who have prostate cancer, benign prostatic hyperplasia, or infection or inflammation of the prostate. Now please clarify what you want to model :) please dont take it otherwise i am not from biology field. Please clarify :) Regards, Gaurav Yadav +++++++++++ Assistant Manager, CCIL, Mumbai (India) Mob: +919821286118 Email: [hidden email] Bhagavad Gita: Man is made by his Belief, as He believes, so He is "Rithesh M. Mohan" <[hidden email]> 07/30/2007 01:30 PM To <[hidden email]> cc Subject Re: [R] ROC curve in R Hi Gaurav, Need your help, I'm relatively new to R or even stats, so can you please give me step by step details to get ROC curve in R. Requirement. To build ROC curve using only PSA(variable) alone of the original cohort against the ROC of the Model of the original cohort. It would be really great if you could help me with this. Thanks and Regards Rithesh ============================================================================================ DISCLAIMER AND CONFIDENTIALITY CAUTION:\ \ This message and ...{{dropped}} ______________________________________________ [hidden email] 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. |
In reply to this post by Rithesh M. Mohan
Hi Ritesh, what i understad of ROC analysis will be coming in other mail :) excellent introduction can be found at http://www.csee.usf.edu/~candamo/site/papers/ROCintro.pdf http://rocr.bioinf.mpi-sb.mpg.de/ take this zip file :) http://rocr.bioinf.mpi-sb.mpg.de/ROCR_1.0-2.zip also ROCR and analogue R manual :) they are having good examples :) please read it in english with the papers given above then it would be really easy to interpret ROC curve. Just try to grasp a simple thing that what is on x axis and what is on y axis, further whether the values are in ascending or descending order. accordingly try to visualize how the ROC space has be analogly divided to give digital classification :) ########code starts here and taken from manual of nanalogue#################### library(analogue) ## continue the example from roc() example(roc) ## draw the ROC curve plot(swap.roc, 1) ## draw the four default diagnostic plots opar <- par(mfrow = c(2,2)) plot(swap.roc) par(opar) #################end of code snippet########################### ############R software working session################## > > ## draw the ROC curve > plot(swap.roc, 1) > > ## draw the four default diagnostic plots > opar <- par(mfrow = c(2,2)) > plot(swap.roc) > par(opar) > ## continue the example from roc() > example(roc) roc> example(join) join> ## load the example data join> data(swapdiat) join> data(swappH) join> data(rlgh) join> ## process so common set of columns for training and test join> ## number of training set samples join> n.train <- nrow(swapdiat) join> ## merge training and test set on columns join> dat <- join(swapdiat, rlgh, verbose = TRUE) Summary: Rows Cols Data set 1: 167 277 Data set 2: 101 139 Merged: 268 277 join> ## convert to proportions join> dat <- dat / 100 join> ## subset data back into training and test sets join> swapdiat <- dat[1:n.train, ] join> rlgh <- dat[(n.train+1):nrow(dat), ] roc> ## fit the MAT model using the squared chord distance measure roc> swap.mat <- mat(swapdiat, swappH, method = "SQchord") roc> ## fit the ROC curve to the SWAP diatom data using the MAT results roc> ## Generate a grouping for the SWAP lakes roc> clust <- hclust(as.dist(swap.mat$Dij), method = "ward") roc> grps <- cutree(clust, 12) roc> ## fit the ROC curve roc> swap.roc <- roc(swap.mat, groups = grps) roc> swap.roc ROC curve of dissimilarities Optimal Dissimilarity = 0.894 AUC = 0.889, p-value: < 2.22e-16 No. within: 1214 No. outside: 12647 > > ## draw the ROC curve > plot(swap.roc, 1) > > ## draw the four default diagnostic plots > opar <- par(mfrow = c(2,2)) > plot(swap.roc) > par(opar) > ##############end of demonstration session######################### Sorry Gaurav, Iâll make sure I mark a copy to r-help also. As I have told, Iâm new to R and even to statistics, so it will take some time for me to learn it. Just help me get a simple ROC curve, please give an example of your own and explain the steps, no mater if its biology or any other field, I just need to get the logic behind it. Thanks & Regards Rithesh M Mohan From: [hidden email] [mailto:[hidden email]] Sent: Monday, July 30, 2007 4:28 PM To: Rithesh M. Mohan Cc: [hidden email] Subject: Re: [R] ROC curve in R Hi Ritesh ***please note Ritesh always mark a copy to the R-help mailing list :) *** Please visit this link to get help in R http://rocr.bioinf.mpi-sb.mpg.de/ROCR_Talk_Tobias_Sing.ppt#384,8,Examples (2/8): Precision/recall curves futher :) what do you mean by PSA and cohort :) after some googling i got this coÂ·hort(khÃ´rt) n. 1. A group or band of people. 2. A companion or associate. 3. A generational group as defined in demographics, statistics, or market research: "The cohort of people aged 30 to 39 . . . were more conservative" American Demographics. 4. a. One of the 10 divisions of a Roman legion, consisting of 300 to 600 men. b. A group of soldiers. and for PSA i got Prostate-specific antigen. A substance produced by the prostate that may be found in an increased amount in the blood of men who have prostate cancer, benign prostatic hyperplasia, or infection or inflammation of the prostate. Now please clarify what you want to model :) please dont take it otherwise i am not from biology field. Please clarify :) Regards, Gaurav Yadav +++++++++++ Assistant Manager, CCIL, Mumbai (India) Mob: +919821286118 Email: [hidden email] Bhagavad Gita: Man is made by his Belief, as He believes, so He is "Rithesh M. Mohan" <[hidden email]> 07/30/2007 01:30 PM To <[hidden email]> cc Subject Re: [R] ROC curve in R Hi Gaurav, Need your help, Iâm relatively new to R or even stats, so can you please give me step by step details to get ROC curve in R. Requirement. To build ROC curve using only PSA(variable) alone of the original cohort against the ROC of the Model of the original cohort. It would be really great if you could help me with this. Thanks and Regards Rithesh ============================================================================================ DISCLAIMER AND CONFIDENTIALITY CAUTION: This message and any attachments with it (the "message") are confidential and intended solely for the addressees. Unauthorized reading, copying, dissemination, distribution or disclosure either whole or partial, is prohibited. If you receive this message in error, please delete it and immediately notify the sender. Communicating through email is not secure and capable of interception, corruption and delays. Anyone communicating with The Clearing Corporation of India Limited (CCIL) by email accepts the risks involved and their consequences. The internet can not guarantee the integrity of this message. CCIL shall (will) not therefore be liable for the message if modified. The recipient should check this email and any attachments for the presence of viruses. CCIL accepts no liability for any damage caused by any virus transmitted by this email. ============================================================================================ DISCLAIMER AND CONFIDENTIALITY CAUTION: This message and any attachments with it (the "message") are confidential and intended solely for the addressees. Unauthorized reading, copying, dissemination, distribution or disclosure either whole or partial, is prohibited. If you receive this message in error, please delete it and immediately notify the sender. Communicating through email is not secure and capable of interception, corruption and delays. Anyone communicating with The Clearing Corporation of India Limited (CCIL) by email accepts the risks involved and their consequences. The internet can not guarantee the integrity of this message. CCIL shall (will) not therefore be liable for the message if modified. The recipient should check this email and any attachments for the presence of viruses. CCIL accepts no liability for any damage caused by any virus transmitted by this email. [[alternative HTML version deleted]] ______________________________________________ [hidden email] 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. |
In reply to this post by Rithesh M. Mohan
Thanks Gaurav,
I'll try this and get back to you. Rithesh M Mohan ________________________________ From: [hidden email] [mailto:[hidden email]] Sent: Monday, July 30, 2007 6:01 PM To: Rithesh M. Mohan Cc: [hidden email] Subject: RE: [R] ROC curve in R Hi Ritesh, what i understad of ROC analysis will be coming in other mail :) excellent introduction can be found at http://www.csee.usf.edu/~candamo/site/papers/ROCintro.pdf http://rocr.bioinf.mpi-sb.mpg.de/ take this zip file :) http://rocr.bioinf.mpi-sb.mpg.de/ROCR_1.0-2.zip also ROCR and analogue R manual :) they are having good examples :) please read it in english with the papers given above then it would be really easy to interpret ROC curve. Just try to grasp a simple thing that what is on x axis and what is on y axis, further whether the values are in ascending or descending order. accordingly try to visualize how the ROC space has be analogly divided to give digital classification :) ########code starts here and taken from manual of nanalogue#################### library(analogue) ## continue the example from roc() example(roc) ## draw the ROC curve plot(swap.roc, 1) ## draw the four default diagnostic plots opar <- par(mfrow = c(2,2)) plot(swap.roc) par(opar) #################end of code snippet########################### ############R software working session################## > > ## draw the ROC curve > plot(swap.roc, 1) > > ## draw the four default diagnostic plots > opar <- par(mfrow = c(2,2)) > plot(swap.roc) > par(opar) > ## continue the example from roc() > example(roc) roc> example(join) join> ## load the example data join> data(swapdiat) join> data(swappH) join> data(rlgh) join> ## process so common set of columns for training and test join> ## number of training set samples join> n.train <- nrow(swapdiat) join> ## merge training and test set on columns join> dat <- join(swapdiat, rlgh, verbose = TRUE) Summary: Rows Cols Data set 1: 167 277 Data set 2: 101 139 Merged: 268 277 join> ## convert to proportions join> dat <- dat / 100 join> ## subset data back into training and test sets join> swapdiat <- dat[1:n.train, ] join> rlgh <- dat[(n.train+1):nrow(dat), ] roc> ## fit the MAT model using the squared chord distance measure roc> swap.mat <- mat(swapdiat, swappH, method = "SQchord") roc> ## fit the ROC curve to the SWAP diatom data using the MAT results roc> ## Generate a grouping for the SWAP lakes roc> clust <- hclust(as.dist(swap.mat$Dij), method = "ward") roc> grps <- cutree(clust, 12) roc> ## fit the ROC curve roc> swap.roc <- roc(swap.mat, groups = grps) roc> swap.roc ROC curve of dissimilarities Optimal Dissimilarity = 0.894 AUC = 0.889, p-value: < 2.22e-16 No. within: 1214 No. outside: 12647 > > ## draw the ROC curve > plot(swap.roc, 1) > > ## draw the four default diagnostic plots > opar <- par(mfrow = c(2,2)) > plot(swap.roc) > par(opar) > ##############end of demonstration session######################### Sorry Gaurav, I'll make sure I mark a copy to r-help also. As I have told, I'm new to R and even to statistics, so it will take some time for me to learn it. Just help me get a simple ROC curve, please give an example of your own and explain the steps, no mater if its biology or any other field, I just need to get the logic behind it. Thanks & Regards Rithesh M Mohan ________________________________ From: [hidden email] [mailto:[hidden email]] Sent: Monday, July 30, 2007 4:28 PM To: Rithesh M. Mohan Cc: [hidden email] Subject: Re: [R] ROC curve in R Hi Ritesh ***please note Ritesh always mark a copy to the R-help mailing list :) *** Please visit this link to get help in R http://rocr.bioinf.mpi-sb.mpg.de/ROCR_Talk_Tobias_Sing.ppt#384,8,Examples (2/8): Precision/recall curves futher :) what do you mean by PSA and cohort :) after some googling i got this co·hort(khôrt) n. 1. A group or band of people. 2. A companion or associate. 3. A generational group as defined in demographics, statistics, or market research: "The cohort of people aged 30 to 39 . . . were more conservative" American Demographics. 4. a. One of the 10 divisions of a Roman legion, consisting of 300 to 600 men. b. A group of soldiers. and for PSA i got Prostate-specific antigen. A substance produced by the prostate that may be found in an increased amount in the blood of men who have prostate cancer, benign prostatic hyperplasia, or infection or inflammation of the prostate. Now please clarify what you want to model :) please dont take it otherwise i am not from biology field. Please clarify :) Regards, Gaurav Yadav +++++++++++ Assistant Manager, CCIL, Mumbai (India) Mob: +919821286118 Email: [hidden email] Bhagavad Gita: Man is made by his Belief, as He believes, so He is "Rithesh M. Mohan" <[hidden email]> 07/30/2007 01:30 PM To <[hidden email]> cc Subject Re: [R] ROC curve in R Hi Gaurav, Need your help, I'm relatively new to R or even stats, so can you please give me step by step details to get ROC curve in R. Requirement. To build ROC curve using only PSA(variable) alone of the original cohort against the ROC of the Model of the original cohort. It would be really great if you could help me with this. Thanks and Regards Rithesh ============================================================================================ DISCLAIMER AND CONFIDENTIALITY CAUTION: This message and any attachments with it (the "message") are confidential and intended solely for the addressees. Unauthorized reading, copying, dissemination, distribution or disclosure either whole or partial, is prohibited. If you receive this message in error, please delete it and immediately notify the sender. Communicating through email is not secure and capable of interception, corruption and delays. Anyone communicating with The Clearing Corporation of India Limited (CCIL) by email accepts the risks involved and their consequences. The internet can not guarantee the integrity of this message. CCIL shall (will) not therefore be liable for the message if modified. The recipient should check this email and any attachments for the presence of viruses. CCIL accepts no liability for any damage caused by any virus transmitted by this email. ============================================================================================ DISCLAIMER AND CONFIDENTIALITY CAUTION: This message and any attachments with it (the "message") are confidential and intended solely for the addressees. Unauthorized reading, copying, dissemination, distribution or disclosure either whole or partial, is prohibited. If you receive this message in error, please delete it and immediately notify the sender. Communicating through email is not secure and capable of interception, corruption and delays. Anyone communicating with The Clearing Corporation of India Limited (CCIL) by email accepts the risks involved and their consequences. The internet can not guarantee the integrity of this message. CCIL shall (will) not therefore be liable for the message if modified. The recipient should check this email and any attachments for the presence of viruses. CCIL accepts no liability for any damage caused by any virus transmitted by this email. [[alternative HTML version deleted]] ______________________________________________ [hidden email] 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. |
In reply to this post by Frank Harrell
On Thursday 26 July 2007 10:45, Frank E Harrell Jr wrote:
> Dylan Beaudette wrote: > > On Thursday 26 July 2007 06:01, Frank E Harrell Jr wrote: > >> Note that even though the ROC curve as a whole is an interesting > >> 'statistic' (its area is a linear translation of the > >> Wilcoxon-Mann-Whitney-Somers-Goodman-Kruskal rank correlation > >> statistics), each individual point on it is an improper scoring rule, > >> i.e., a rule that is optimized by fitting an inappropriate model. Using > >> curves to select cutoffs is a low-precision and arbitrary operation, and > >> the cutoffs do not replicate from study to study. Probably the worst > >> problem with drawing an ROC curve is that it tempts analysts to try to > >> find cutoffs where none really exist, and it makes analysts ignore the > >> whole field of decision theory. > >> > >> Frank Harrell > > > > Frank, > > > > This thread has caught may attention for a couple reasons, possibly > > related to my novice-level experience. > > > > 1. in a logistic regression study, where i am predicting the probability > > of the response being 1 (for example) - there exists a continuum of > > probability values - and a finite number of {1,0} realities when i either > > look within the original data set, or with a new 'verification' data set. > > I understand that drawing a line through the probabilities returned from > > the logistic regression is a loss of information, but there are times > > when a 'hard' decision requiring prediction of {1,0} is required. I have > > found that the ROCR package (not necessarily the ROC Curve) can be useful > > in identifying the probability cutoff where accuracy is maximized. Is > > this an unreasonable way of using logistic regression as a predictor? Thanks for the detailed response Frank. My follow-up questions are below: > Logistic regression (with suitable attention to not assuming linearity > and to avoiding overfitting) is a great way to estimate P[Y=1]. Given > good predicted P[Y=1] and utilities (losses, costs) for incorrect > positive and negative decisions, an optimal decision is one that > optimizes expected utility. The ROC curve does not play a direct role > in this regard. Ok. > If per-subject utilities are not available, the analyst > may make various assumptions about utilities (including the unreasonable > but often used assumption that utilities do not vary over subjects) to > find a cutoff on P[Y=1]. Can you elaborate on what exactly a "per-subject utility" is? In my case, I am trying to predict the occurance of specific soil features based on two predictor variables: 1 continuous, the other categorical. Thus far my evaluation of how well this method works is based on how often I can correctly predict (a categorical) quality. > A very nice feature of P[Y=1] is that error > probabilities are self-contained. For example if P[Y=1] = .02 for a > single subject and you predict Y=0, the probability of an error is .02 > by definition. One doesn't need to compute an overall error probability > over the whole distribution of subjects' risks. If the cost of a false > negative is C, the expected cost is .02*C in this example. Interesting. The hang-up that I am having is that I need to predict from {O,1}, as the direct users of this information are not currently interested in in raw probabilities. As far as I know, in order to predict a class from a probability I need use a cutoff... How else can I accomplish this without imposing a cutoff on the entire dataset? One thought, identify a cutoff for each level of the categorical predictor term in the model... (?) > > 2. The ROC curve can be a helpful way of communicating false positives / > > false negatives to other users who are less familiar with the output and > > interpretation of logistic regression. > > What is more useful than that is a rigorous calibration curve estimate > to demonstrate the faithfulness of predicted P[Y=1] and a histogram > showing the distribution of predicted P[Y=1] Ok. I can make that histogram - how would one go about making the 'rigorous calibration curve' ? Note that I have a training set, from which the model is built, and a smaller testing set for evaluation. > . Models that put a lot of > predictions near 0 or 1 are the most discriminating. Calibration curves > and risk distributions are easier to explain than ROC curves. By 'risk discrimination' do you mean said histogram ? > Too often > a statistician will solve for a cutoff on P[Y=1], imposing her own > utility function without querying any subjects. in this case I have picked a cutoff that resulted in the smallest number of incorrectly classified observations , or highest kappa / tau statistics -- the results were very close. > > 3. I have been using the area under the ROC Curve, kendall's tau, and > > cohen's kappa to evaluate the accuracy of a logistic regression based > > prediction, the last two statistics based on a some probability cutoff > > identified before hand. > > ROC area (equiv. to Wilcoxon-Mann-Whitney and Somers' Dxy rank > correlation between pred. P[Y=1] and Y) is a measure of pure > discrimination, not a measure of accuracy per se. Rank correlation > (concordance) measures do not require the use of cutoffs. Ok. Hopefully I am not abusing the kappa and tau statistics too badly by using them to evaluate a probability cutoff... (?) > > How does the topic of decision theory relate to some of the circumstances > > described above? Is there a better way to do some of these things? > > See above re: expected loses/utilities. > > Good questions. > > Frank Thanks for the feedback. Cheers, Dylan > > Cheers, > > > > Dylan > > > >> [hidden email] wrote: > >>> http://search.r-project.org/cgi-bin/namazu.cgi?query=ROC&max=20&result= > >>>no rmal&sort=score&idxname=Rhelp02a&idxname=functions&idxname=docs > >>> > >>> there is a lot of help try help.search("ROC curve") gave > >>> Help files with alias or concept or title matching 'ROC curve' using > >>> fuzzy matching: > >>> > >>> > >>> > >>> granulo(ade4) Granulometric Curves > >>> plot.roc(analogue) Plot ROC curves and > >>> associated diagnostics > >>> roc(analogue) ROC curve analysis > >>> colAUC(caTools) Column-wise Area Under ROC > >>> Curve (AUC) > >>> DProc(DPpackage) Semiparametric Bayesian ROC > >>> curve analysis > >>> cv.enet(elasticnet) Computes K-fold > >>> cross-validated error curve for elastic net > >>> ROC(Epi) Function to compute and draw > >>> ROC-curves. > >>> lroc(epicalc) ROC curve > >>> cv.lars(lars) Computes K-fold > >>> cross-validated error curve for lars > >>> roc.demo(TeachingDemos) Demonstrate ROC curves by > >>> interactively building one > >>> > >>> HTH > >>> see the help and examples those will suffice > >>> > >>> Type 'help(FOO, package = PKG)' to inspect entry 'FOO(PKG) TITLE'. > >>> > >>> > >>> > >>> Regards, > >>> > >>> Gaurav Yadav > >>> +++++++++++ > >>> Assistant Manager, CCIL, Mumbai (India) > >>> Mob: +919821286118 Email: [hidden email] > >>> Bhagavad Gita: Man is made by his Belief, as He believes, so He is > >>> > >>> > >>> > >>> "Rithesh M. Mohan" <[hidden email]> > >>> Sent by: [hidden email] > >>> 07/26/2007 11:26 AM > >>> > >>> To > >>> <[hidden email]> > >>> cc > >>> > >>> Subject > >>> [R] ROC curve in R > >>> > >>> > >>> > >>> > >>> > >>> > >>> Hi, > >>> > >>> > >>> > >>> I need to build ROC curve in R, can you please provide data steps / > >>> code or guide me through it. > >>> > >>> > >>> > >>> Thanks and Regards > >>> > >>> Rithesh M Mohan > >>> > >>> > >>> [[alternative HTML version deleted]] > >> > >> - > >> Frank E Harrell Jr Professor and Chair School of Medicine > >> Department of Biostatistics Vanderbilt > >> University ______________________________________________ [hidden email] 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. |
Dylan Beaudette wrote:
> On Thursday 26 July 2007 10:45, Frank E Harrell Jr wrote: >> Dylan Beaudette wrote: >>> On Thursday 26 July 2007 06:01, Frank E Harrell Jr wrote: >>>> Note that even though the ROC curve as a whole is an interesting >>>> 'statistic' (its area is a linear translation of the >>>> Wilcoxon-Mann-Whitney-Somers-Goodman-Kruskal rank correlation >>>> statistics), each individual point on it is an improper scoring rule, >>>> i.e., a rule that is optimized by fitting an inappropriate model. Using >>>> curves to select cutoffs is a low-precision and arbitrary operation, and >>>> the cutoffs do not replicate from study to study. Probably the worst >>>> problem with drawing an ROC curve is that it tempts analysts to try to >>>> find cutoffs where none really exist, and it makes analysts ignore the >>>> whole field of decision theory. >>>> >>>> Frank Harrell >>> Frank, >>> >>> This thread has caught may attention for a couple reasons, possibly >>> related to my novice-level experience. >>> >>> 1. in a logistic regression study, where i am predicting the probability >>> of the response being 1 (for example) - there exists a continuum of >>> probability values - and a finite number of {1,0} realities when i either >>> look within the original data set, or with a new 'verification' data set. >>> I understand that drawing a line through the probabilities returned from >>> the logistic regression is a loss of information, but there are times >>> when a 'hard' decision requiring prediction of {1,0} is required. I have >>> found that the ROCR package (not necessarily the ROC Curve) can be useful >>> in identifying the probability cutoff where accuracy is maximized. Is >>> this an unreasonable way of using logistic regression as a predictor? > > Thanks for the detailed response Frank. My follow-up questions are below: > >> Logistic regression (with suitable attention to not assuming linearity >> and to avoiding overfitting) is a great way to estimate P[Y=1]. Given >> good predicted P[Y=1] and utilities (losses, costs) for incorrect >> positive and negative decisions, an optimal decision is one that >> optimizes expected utility. The ROC curve does not play a direct role >> in this regard. > > Ok. > >> If per-subject utilities are not available, the analyst >> may make various assumptions about utilities (including the unreasonable >> but often used assumption that utilities do not vary over subjects) to >> find a cutoff on P[Y=1]. > > Can you elaborate on what exactly a "per-subject utility" is? In my case, I am > trying to predict the occurance of specific soil features based on two > predictor variables: 1 continuous, the other categorical. Thus far my > evaluation of how well this method works is based on how often I can > correctly predict (a categorical) quality. This could be called a per-unit utility in your case. It is the consequence of decisions at the point in which you decide Y=0 or Y=1. If consequences are the same over all units, you just have to deal with the single ratio of cost of false positive to cost of false negative. One way to limit bad consequences is to not make any decision when the predicted probability is in the middle, i.e., the decision is 'obtain more data'. That is a real advantage of having a continuous risk estimate. > > >> A very nice feature of P[Y=1] is that error >> probabilities are self-contained. For example if P[Y=1] = .02 for a >> single subject and you predict Y=0, the probability of an error is .02 >> by definition. One doesn't need to compute an overall error probability >> over the whole distribution of subjects' risks. If the cost of a false >> negative is C, the expected cost is .02*C in this example. > > Interesting. The hang-up that I am having is that I need to predict from > {O,1}, as the direct users of this information are not currently interested > in in raw probabilities. As far as I know, in order to predict a class from a > probability I need use a cutoff... How else can I accomplish this without > imposing a cutoff on the entire dataset? One thought, identify a cutoff for > each level of the categorical predictor term in the model... (?) You're right you have to ultimately use a cutoff (or better still, educate the users about the meaning of probabilities and let them make the decision without exposing the cutoff). And see the comment regarding gray zones above. > >>> 2. The ROC curve can be a helpful way of communicating false positives / >>> false negatives to other users who are less familiar with the output and >>> interpretation of logistic regression. >> What is more useful than that is a rigorous calibration curve estimate >> to demonstrate the faithfulness of predicted P[Y=1] and a histogram >> showing the distribution of predicted P[Y=1] > > Ok. I can make that histogram - how would one go about making the 'rigorous > calibration curve' ? Note that I have a training set, from which the model is > built, and a smaller testing set for evaluation. See the val.prob function in the Design package. This assumes your test samples and training samples are both large and are independent. Otherwise data splitting is too noisy a method and you might consider calibrate.lrm in Design, fitting all the data. > > >> . Models that put a lot of >> predictions near 0 or 1 are the most discriminating. Calibration curves >> and risk distributions are easier to explain than ROC curves. > > By 'risk discrimination' do you mean said histogram ? yes > >> Too often >> a statistician will solve for a cutoff on P[Y=1], imposing her own >> utility function without querying any subjects. > > in this case I have picked a cutoff that resulted in the smallest number of > incorrectly classified observations , or highest kappa / tau statistics -- > the results were very close. Proportion of incorrect classifications is an improper scoring rule that tells you about the average performance of the method over all of the units. It is not that helpful for an individual unit, as all units may have different predicted probabilities. Because it's improper, you will find examples where a powerful variable is added to a model and the percent classified correctly decreases. > > >>> 3. I have been using the area under the ROC Curve, kendall's tau, and >>> cohen's kappa to evaluate the accuracy of a logistic regression based >>> prediction, the last two statistics based on a some probability cutoff >>> identified before hand. >> ROC area (equiv. to Wilcoxon-Mann-Whitney and Somers' Dxy rank >> correlation between pred. P[Y=1] and Y) is a measure of pure >> discrimination, not a measure of accuracy per se. Rank correlation >> (concordance) measures do not require the use of cutoffs. > > Ok. Hopefully I am not abusing the kappa and tau statistics too badly by using > them to evaluate a probability cutoff... (?) Kappa, tau, Dxy, gamma, ROC area are all functions of the continuous predicted risks and the observed Y=0,1. They don't deal with cutoffs. > >>> How does the topic of decision theory relate to some of the circumstances >>> described above? Is there a better way to do some of these things? >> See above re: expected loses/utilities. Decision theory helps you translate maximum current information (often summarized in a predicted risk) and utilities/losses/costs to decisions. I'm looking for a great background article on this; some useful stuff is in the Encyclopedia of Statistical Sciences but other people may find some great references for us. Frank >> >> Good questions. >> >> Frank > > Thanks for the feedback. > > Cheers, > > Dylan > > >>> Cheers, >>> >>> Dylan >>> >>>> [hidden email] wrote: >>>>> http://search.r-project.org/cgi-bin/namazu.cgi?query=ROC&max=20&result= >>>>> no rmal&sort=score&idxname=Rhelp02a&idxname=functions&idxname=docs >>>>> >>>>> there is a lot of help try help.search("ROC curve") gave >>>>> Help files with alias or concept or title matching 'ROC curve' using >>>>> fuzzy matching: >>>>> >>>>> >>>>> >>>>> granulo(ade4) Granulometric Curves >>>>> plot.roc(analogue) Plot ROC curves and >>>>> associated diagnostics >>>>> roc(analogue) ROC curve analysis >>>>> colAUC(caTools) Column-wise Area Under ROC >>>>> Curve (AUC) >>>>> DProc(DPpackage) Semiparametric Bayesian ROC >>>>> curve analysis >>>>> cv.enet(elasticnet) Computes K-fold >>>>> cross-validated error curve for elastic net >>>>> ROC(Epi) Function to compute and draw >>>>> ROC-curves. >>>>> lroc(epicalc) ROC curve >>>>> cv.lars(lars) Computes K-fold >>>>> cross-validated error curve for lars >>>>> roc.demo(TeachingDemos) Demonstrate ROC curves by >>>>> interactively building one >>>>> >>>>> HTH >>>>> see the help and examples those will suffice >>>>> >>>>> Type 'help(FOO, package = PKG)' to inspect entry 'FOO(PKG) TITLE'. >>>>> >>>>> >>>>> >>>>> Regards, >>>>> >>>>> Gaurav Yadav >>>>> +++++++++++ >>>>> Assistant Manager, CCIL, Mumbai (India) >>>>> Mob: +919821286118 Email: [hidden email] >>>>> Bhagavad Gita: Man is made by his Belief, as He believes, so He is >>>>> >>>>> >>>>> >>>>> "Rithesh M. Mohan" <[hidden email]> >>>>> Sent by: [hidden email] >>>>> 07/26/2007 11:26 AM >>>>> >>>>> To >>>>> <[hidden email]> >>>>> cc >>>>> >>>>> Subject >>>>> [R] ROC curve in R >>>>> >>>>> >>>>> >>>>> >>>>> >>>>> >>>>> Hi, >>>>> >>>>> >>>>> >>>>> I need to build ROC curve in R, can you please provide data steps / >>>>> code or guide me through it. >>>>> >>>>> >>>>> >>>>> Thanks and Regards >>>>> >>>>> Rithesh M Mohan >>>>> >>>>> >>>>> [[alternative HTML version deleted]] >>>> - >>>> Frank E Harrell Jr Professor and Chair School of Medicine >>>> Department of Biostatistics Vanderbilt >>>> University ______________________________________________ [hidden email] 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.
Frank Harrell
Department of Biostatistics, Vanderbilt University |
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