search for: colauc

...ORE) + } > f(data)  [1] 706 633 443 843 756 743 730 843 706 730 606 743 768 768 743 763 608 730 743 743 530 813 813 831 793 900 793 693 900 738 706 831 [33] 818 758 718 831 768 638 770 738 > repl<-list() > auc<-list() > for(i in 1:1000) + { + repl[[i]]<-f(data) + auc[[i]]<-colAUC(repl[[i]],rep(c("BAD","GOOD")),plotROC=FALSE,alg="ROC") + } Error in colAUC(repl[[i]], rep(c("BAD", "GOOD")), plotROC = FALSE, alg = "ROC") :   colAUC: length(y) and nrow(X) must be the same Thanks alotTaby [[alternative...

Hi, I was lately debugging parts of my 'colAUC' function in caTools package, and in a process looked into other packages for calculating Areas Under ROC Curves (AUC). To my surprise I found at least 6 other functions: * wilcox.test * AUC from ROC package, * performance from ROCR package, * auROC from limma package, * ROC from...

...substitute(myformula)) logistic_reg <- glm(myformula , data=df, family=binomial(link="logit")) print("model build OK") #score up scores <- predict(logistic_reg, type="response", df) print("model scored OK") #calc AUC Y = df$mytarget auc <- colAUC(scores,Y) print("auc calculated OK") } logistictest(df=trainset,mytarget=Target) [1] "model build OK" [1] "model scored OK" Error in as.vector(x, mode) : invalid 'mode' argument -- View this message in context: http://www.nabble.com/passing-field-na...

...eparse(substitute(myformula)) logistic_reg <- glm(myformula , data=df, family=binomial(link="logit")) print("model build OK") #score up scores <- predict(logistic_reg, type="response", df) print("model scored OK") #calc AUC Y = df$mytarget auc <- colAUC(scores,Y) print("auc calculated OK") } logistictest(df=trainset,mytarget=Target) [1] "model build OK" [1] "model scored OK" Error in as.vector(x, mode) : invalid 'mode' argument -- View this message in context: http://www.nabble.com/field-names-as-functio...

I am doing a simulation with a relatively large data set (20,000 observations) for which I want to calculate the area under the Receiver Operator Curve (AUC) for many parameter combinations. I am using the ROC library and the following commands to generate each AUC: rocobj=rocdemo.sca(truth = ymis, data = model$fitted.values, rule = dxrule.sca) #generation of observed ROC object

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]]