similar to: Package for comparing sensitivity, specificity, PPV, NPV, and accuracy?

Dear all Seems that puzzles always come in packs. I was asked to help with some statistics in blood analysis. (You can not refuse your wife's asks :-). She has contingency table for values IgVH mutation and ZAP expression. I can do chi-square test (in R) and get a results, and with some literature I can try explain them. However she found an article in which they use SPSS and use

All, Appreciate any leads on the following: In a recent blind-validation study of a depression screening instrument we used a two-stage sampling design. In stage 1, we used a broad paper-and-pencil screen to identify likely positives (say 30% of entire sample). In stage 2 we conducted in-depth interviews with the 30% of likely positives plus another 20% of the negatives as controls.

Dear Mr Peter Dalgaard and Mr Dieter Menne, I sincerely thank you for helping me out with my problem. The thing is taht I already have calculated SENS = Gg / (Gg + Bg) = 89.97% and SPEC = Bb / (Bb + Gb) = 74.38%. Now I have values of SENS and SPEC, which are absolute in nature. My question was how do I interpret these absolue values. How does these values help me to find out wheher my model is

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]]

Dear R-colleagues, this is a question from a R-newbie medical doctor: I am evaluating data on inter-observer-reliability in endoscopy. 20 medical doctors judged 42 videos filling out a multiple choice survey for each video. The overall-data is organized in a classical way: observations (items from the multiple choice survey) as columns, each case (identified by the two columns "number of

Hello, As obtained from a table svyglm clasificaion, sensitivity and specificity. The funtion ConfusionMatrix () of the library (caret) gives these results but not how to apply it to svyglm. thanks [[alternative HTML version deleted]]

Dear R Users, Do you know of an existing function that allows the production of sensitivity and specificity forest plots? See the following for an example:

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(

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, 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

I am running my code in a loop and it does not work but when I run it outside the loop I get the values I want. n <- 1000; # Sample size fitglm <- function(sigma,tau){ x <- rnorm(n,0,sigma) intercept <- 0 beta <- 0 ystar <- intercept+beta*x z <- rbinom(n,1,plogis(ystar)) xerr <- x + rnorm(n,0,tau) model<-glm(z ~ xerr, family=binomial(logit))

Dear all, I have a standard method and two alternatives to perform a test, called method A and method B. I have calculated the sensitivity and specificity for standard method vs. method A and standard method vs. method B. Hence, I have two sensitivity values an two specificity values. To be clear, sensitivity and specificity was calculated from: Disease(A) No Disease(Ac) Total Positive

Hi, I am a graduate student applying published R scripts to compare the classification accuracy of 2 predictive models, one built using discriminant function analysis and one using random forests (webpage link for these scripts is provided below). The purpose of these models is to predict the biotic integrity of streams. Specifically, I am trying to compare the classification accuracy (i.e.,

Hi, I'm relative new to R. I'm looking for a convenient way to calculate sensitivity and specificity in machine-learning models (SVM, ...). In a two-class case it's quite simple by calculating the TP, TN, FP, FN rates once but in the multi-class case I have to do it as often as there exist different classes (for each class). Of course I could do it manually in a for-loop or so. But is

I am trying to replace a spreadsheet model of a project justification with an R script. I can't seem to track down R functions to calculate Internal Rate of Return and NPV? Am I missing something? NPV doesn't seem so difficult to calculate (at least for a regular series) but I am struggling to identify how to solve for IRR in R. It would be sufficient if it worked for a regular

Hi, I am new to R, and as a first exercise, I decided to try to implement an XIRR function using the secant method. I did a quick search and saw another posting that used the Bisection method but wanted to see if it was possible using the secant method. I would input a Cash Flow and Date vector as well as an initial guess. I hardcoded today's initial date so I could do checks in Excel.

Hello, could You please help: I am looking for a way to formulate test accuracy measures such as test sensitivity, specificity, predictive values, and correct classification rate using p/d/qnorm. The tests' primary values follow a bimodal distribution, which is modelled by a mixture of two normal distributions: p * dnorm ((x - u1) / s1) / s1 + (1 - p) * dnorm ((x - u2) / s2) / s2)

Hi all, I would like to calculate the area under the ROC curve for my predictive model. I have managed to plot points giving me the ROC curve. However, I do not know how to get the value of the area under. Does anybody know of a function that would give the result I want using an array of specificity and an array of sensitivity as input? Thanks, Olivier -- View this message in context:

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

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