search for: lroc

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 "lroc" function on the big...

...lm(p ~ Time + Temperature + Depth + Time*Temperature + Time*Depth + Temperature*Depth, family = binomial(link = logit), data=gmelt.df, weight=Total) summary(gmelt.glm) #------------------------------------------------ # ROC #------------------------------------------------ library(epicalc) lroc(gmelt.glm, graph = TRUE, line.col = "red") The error message: > lroc(gmelt.glm, graph = TRUE, line.col = "red") Error in dimnames(x) <- dn : length of 'dimnames' [2] not equal to array extent Have I overlooked something? Many thanks to anyone who might ha...

...? ? ? ? ? ? ? ? ?+ Time*Temperature + Time*Depth + Temperature*Depth, ? ? ? ? ? ? ? ? ? ? ? ?family = binomial(link = logit), data=gmelt.df, weight=Total) summary(gmelt.glm) #------------------------------------------------ # ?ROC #------------------------------------------------ library(epicalc) lroc(gmelt.glm, graph = TRUE, line.col = "red") The error message: > lroc(gmelt.glm, graph = TRUE, line.col = "red") Error in dimnames(x) <- dn : ?length of 'dimnames' [2] not equal to array extent Have I overlooked something? Many thanks to anyone who might hav...

I know how to compute the ROC curve and the empirical AUC from the logistic regression after fitting the model. But here is my question, how can I compute the standard error for the AUC estimator resulting form logistic regression? The variance should be more complicated than AUC based on known test results. Does anybody know a reference on this problem? [[alternative HTML version deleted]]

Hi, Is there a way when doing logistic regression for the output to spit out accuracy, sensitivity, and specificity? I would like to know these basic measures for my model. Thanks! [[alternative HTML version deleted]]

Hello, Are there any packages/functions available for testing the assumptions underlying assumptions for a good GLM model? Like linktest in STATA and smilar. If not, could somebody please describe their work process when they check the validity of a logit/probit model? Regards, Jay

Hi, I'm trying to set up a prediction software, now i testing the performance of my method, so i need to calculate a ROC curve, specially auc, cut-off, sens and spec, i just looking at ROCH package, but it's a mass for me, i'm not a math guy and I'm getting lost Could any of you recommend me an easy-to-use package to do this task? i just have a list of positive/negative samples

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]]