similar to: How to add accuracy, sensitivity, specificity to logistic regression output?

Hi, Assume I have a variable Y having two discrete values and two predictor variables x1 and x2. I then do a logistic regression model fit as: fit<-glm(Y~x1+x2,family=binomial). Are there functions in R than calculate the Sensitivity, Specificity , and Agreement of the model "fit"? Thanks Meir ******************************************** Meir Preiszler - Research Engineer I t

Hi, My questions concerns the ROCR package and I hope somebody here on the list can help - or point me to some better place. When evaluating a model's performane, like this: pred1 <- predict(model, ..., type="response") pred2 <- prediction(pred1, binary_classifier_vector) perf <- performance(pred, "sens", "spec") (Where "prediction" and

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 list, I'm new to R and I'm currently using ROCR package. Data in input look like this: DIAGNOSIS SCORE 1 0.387945 1 0.50405 1 0.435667 1 0.358057 1 0.583512 1 0.387945 1 0.531795 1 0.527148 0 0.526397 0 0.372935 1 0.861097 And I run the following simple code: d <- read.table("inputFile", header=TRUE); pred <- prediction(d$SCORE, d$DIAGNOSIS); perf <- performance(

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 all, I'm looking for a program that will take the predicted probabilities from a logistic regression using glm{stats}, dichotomize them according to a threshold that I can control, and then use them to form sensitivity, specificity, false pos and false neg rates. Thanks in advance. David -- ======================================================================= David Kaplan, Ph.D.

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]]

Dear Guru's, Is there a package (R of course) with programs for diagnostics - roc, sens , spec, kappa etc? Best wishes Fredrik L

Dear R-help, I am resending as I believe I screwed up the e-mail address to R-help earlier. Sorry for my lack of attention to detail, and for any inconvenience. I have also sent the question to the package maintainer, as suggested in the posting guide. Regards, Cliff ---------- Forwarded message ---------- From: Clifford Long <gnolffilc at gmail.com> Date: Sun, Jul 26, 2009 at 8:46

Hi, When I use logistic regression, each variable has a p value associated with it. Do I only include the variables that have a statistically significant p value (<0.05), or are there situations when I should include variables when their p values are high? I had heard that if a variable has a high p value but it's not the terminal variable, keep it; otherwise, take it out. Not sure if

Hi, I have two sets of sensitivity, specificity, positive predictive value, and negative predictive value, and accuracy from two tests on the same subjects. Is there an R package that does such paired comparisons? Thanks, Gang Chen

If we use the ROCR package to find the accuracy of a classifier pred <- prediction(svm.pred, testset[,2]) perf.acc <- performance(pred,"acc") Do we?find the maximum accuracy?as follows?(is there a simplier way?): > max(perf.acc at x.values[[1]]) Then to find the cutoff point that maximizes the accuracy?do we do the following?(is there a simpler way): > cutoff.list <-

Dear List,   I am new to ROC analysis and the package ROCR. I want to compute the confidence intervals of sensitivity and specificity for a given cutoff value. I have used the following to calculate sensitivity and specificity:   data(ROCR.simple) pred <- prediction(ROCR.simple$predictions, ROCR.simple$labels)   se.sp <- function (cutoff, performance) {     sens <-

Dear R-help list, I'm attempting to use the ROC routine from the epicalc package after performing a logistic regression analysis. My code is included after the sessionInfo() result. The datafile (GasketMelt1.csv) is attached. I updated both R and the epicalc packages and tried again before sending this request. sessionInfo result: R version 2.9.1 (2009-06-26) i386-pc-mingw32 locale:

Hola! Tengo que hacer una curva ROC com unos datos que obtuve de hacer una macro de excel y aplicar unas reglas, y basicamente tengo que a partir de la variacion del tiempo y la velocidad del barco obtengo diferentes porcentajes de true positives (TP) y false positives (FP) y con eso deberia de construir una curva ROC. Dada mi ignorância en este tema, no se muy bien por donde empezar , estuve

Hi Hi I am working on credit scoring model using logistic regression. I havd main sample of 42500 clentes and based on their status as regards to defaulted / non - defaulted, I have genereted the probability of default. I have a hold out sample of 5000 clients. I have calculated (1) No of correctly classified goods Gg, (2) No of correcly classified Bads Bg and also (3) number of wrongly

Hola, Marta, Por lo que he podido ver tus datos ya tienen calculados las tasas de verdaderos y falsos positivos (TPR y FPR). También parece que los tienes ordenados por la variable FishSpeed y supongo que también por las que parecen marcas de tiempo. No necesitas ROCR porque con un simple plot te sale algo parecido a una curva. Eso sí, veo que son medidas repetidas en el tiempo para cada valor de

Hello all, I am very new to R and i am facing two problems. First i didn't succeed changing the konsole language in english even after trying the line command set language='en'. I would like to plot ROC curves. I have a serie of 10 threshold tests that i do for 10 patients. The prediction for the patients is always the same but the status can change given to the considered threshold.

*Hi all, I know this is probably basic, but I have proven to be a slow learner in any programming language. Anyhow, how can I calculate the SD for each person in my table? I have two patients in this R data.frame, 7200 and 23955. I extracted this from a relational database, but am I better off attempting to compute SD in SQL, or is this easily accomplished in R? * SUBJECT_ID HR 1

Hi, I am using logistic regression to classify a binary psychometric data. using glm() and then predict.glm() i got the predicted odds ratio of the testing data. Next i am going to plot ROC curve for the analysis of my study. Now what i will do: 1. first select a cut-off (say 0.4) and classify the output of predict.glm() into {0,1} segment and then use it to draw ROC curve using ROCR package