similar to: roc and lattice

Hello, it is likely that I will have to analyze a rather sizeable dataset: 60000 records, 10 to 15 variables. I will have to make descriptive statistics, and estimate linear models, glm's and maybe Cox proportional hazard model with time varying covariates. In theory, this is possible in R, but I would like to get some feedback on the equipment I should get for this. At this moment, I have a

Hello, I am not very familiar with regular expressions and escaping. I need to replace the %-signs in a character vector with elements as "income 0%-33%# to be replaced by "income 0\%-33\%" (for later use in LaTeX). Using gsub("%","\\%","income 0%-33%") does not give the desired result. However, gsub("%","\\\\%","income

I might be missing something but I thought that AUC was a measure for comparing ROC curves, so there is nothing else needed to "compare" them. The larger AUC is the higher correlation of 2 variables compared. No other measures or calculations are needed. Jarek Tuszynski -----Original Message----- From: r-help-bounces at stat.math.ethz.ch [mailto:r-help-bounces at stat.math.ethz.ch] On

What is the difference between ROC curve and gains/lift chart? how to do them in R? Thanks. -- View this message in context: http://www.nabble.com/ROC-curve-and-gains-lift-chart-tp25083979p25083979.html Sent from the R help mailing list archive at Nabble.com.

Hello R-List: Does anybody have code to optimize a logistic regression using ROC curves? I've seen S+ code that does it but never in R. Thanks. __________________________________________________ You Rock! Your E-mail should, too. Visit Rock.com! [[alternative HTML version deleted]]

Hi list, I know this is not hard to implement based on the returned objects from ROC, ROCR or a couple of other roc-related packages. I'm just wondering if there is already such a function exist. Thanks! ...Tao

hi, I'm looking to compare two area under ROC values for different classifiers on the same data -- is there an r function to do this? Thanks! [[alternative HTML version deleted]]

Dear all, i am newbie to R software and i work with survival functions. Anyone knows a package to build and analyze time-dependent ROC curves? I need some help ;) Best regards Roberto

Hi, I am new here. I would like to compare the performance of the random forest model with support vector machine. Can anybody let me know how to generate a ROC curve for random forest model since there is no need to run the cross-validation. Thank you very much! TL _________________________________________________________________ [[replacing trailing spam]]

Hello, I have a classification tree. I want to obtain a ROC curve for this test. What is the easiest way to obtain one? -Anjali --------------------------------- [[alternative HTML version deleted]]

Is there a statistical test implemented within R to compare the area under two ROC curves ?? Thanks, Berthold -------------- next part -------------- A non-text attachment was scrubbed... Name: berthold.kramm.vcf Type: text/x-vcard Size: 214 bytes Desc: Card for Dr. Berthold Kramm Url : https://stat.ethz.ch/pipermail/r-help/attachments/20011120/dc0f7bf1/berthold.kramm.vcf

Hello Im a beginner on R and I would like how to develop a ROC statistic to evaluate a GAM model with a binomial distribution (Im using mgcv package) Thanks in advance -- David Nogu?s Bravo Functional Ecology and Biodiversity Department Pyrenean Institute of Ecology Spanish Research Council Av. Monta?ana 1005 Zaragoza - CP 50059 976716030 - 976716019 (fax)

I'm using randomSurvivalForest to predict survival from a rather small sample. As it's not enough data to have training and validation set, I rely on the "Estimate of error rate" computed by the randomForest. If I understand the method correctly, it repeatedly partitions the data into varying training/validation sets during the learning steps, which also produces the

Hello list, I am trying to avoid "Rifying" some of my SAS code to generate ROC plots, and the logistic.display() and lroc() functions in the epicalc package do what I want. However, I must generate my logistic model with bigglm because I have 1) limited hardware, 2) ~2.5 million rows, and 4 categorical and 2 continuous independent variables. When I attempt to invoke epicalc's

Hi everyone, I am doing 5 years mortality predictive index score with survival analysis using a Cox proportional hazard model where I have a continous predictive variable and a right censored response which is the mortality, and the individuals were followed a maximum of 7 years. I'd like to asses the discrimination ability of survival analysis Cox model by computing a ROC curve and area

Dear R helpers: I have another question regarding "R" command. It is also measuring Credit Scoring. I need to measure the classifying method for customer's credit score. (ex. regression line by Fisher) If credit card company already classify their customers credit : Good customers / Bad customers [by using regression line; ex) inside of line: Good/ outside: Bad] And I found

Hi, Is there any package or code to compare and display correlated ROC curves in R? Thanks, Reza [[alternative HTML version deleted]]

Hello list, I am trying to perform ROC analysis and count the AUC in order to validate my results. I use package ROCR. I would like to count the AUC not under the cutoff found by "performance" but to use another cutoff that I calculate. How could I change the following command in order to get what I want? perform=performance(pred,measure="auc",x.measure="cutoff"),

Dear R users, pROC is a package to compare, visualize, and smooth receiver operating characteristic (ROC) curves. The package provides the following features: * Partial or full area under the curve (AUC) computation * Comparison of two ROC curves (curves and AUC) * Calculating and plotting confidence intervals * Smoothing of the ROC curve * Coordinates extraction ('coords' function).

Dear R users, pROC is a package to compare, visualize, and smooth receiver operating characteristic (ROC) curves. The package provides the following features: * Partial or full area under the curve (AUC) computation * Comparison of two ROC curves (curves and AUC) * Calculating and plotting confidence intervals * Smoothing of the ROC curve * Coordinates extraction ('coords' function).