similar to: Sensitivity and specificity in multi-class problems

library(ROCR) n <- 1000 fitglm <- function(iteration,intercept,sigma,tau,beta){ x <- rnorm(n,0,sigma) ystar <- intercept+beta*x z <- rbinom(n,1,plogis(ystar)) xerr <- x + rnorm(n,0,tau) model<-glm(z ~ xerr, family=binomial(logit)) *int*<-coef(model)[1] *slope*<-coef(model)[2] # when add error you are suppose to get slightly bias slope. However when I change

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 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(

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:

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]]

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

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]]

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 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

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

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

Hello friends of R list, Im a physician and Im not that good in statistics. I have posted similar email in the epi-sig list but no one aswered so far. Im cunducting a systematic review on two diagnostic test for a particular tropical disease. I found a free software that make most of the analysis called MetaDiSc. However there is a paticular analysis that I wuould like to try that it is not

Maybe you want to check out the prevalence package (http://cran.r-project.org/web/packages/prevalence/index.html), and its development version on GitHub (https://github.com/brechtdv/prevalence/). When you have two tests, and neither can be considered to be a 'gold standard' (ie, SE & SP = 100%), Bayesian latent class approaches can help you to estimate true prevalence, sensitivity

Stata users can rely on the very neat Episens package for sensitivity analysis. Briefly, it allows one to specify a diagnostic tool's sensitivity and specificity and take those into account when estimating a risk ratio, for instance. A full description of the package is available at <http://www.stata-journal.com/sjpdf.html?articlenum=st0138>

Dear List, I have encountered an odd problem that I cannot understand. It stems from the calculation of true and false positives based on two input vectors x and y based on different thresholds of x, extracted using the quantile function. I am in certain cases getting different values of true positives for the same threshold value when the threshold was found under different quantiles (e.g. the

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.

Hi everyone, I am trying to construct a glm and am running into a couple of questions. The data set I am using consists of 6 categories for the response and 6 independent predictors representing nutrient concentrations at sample point locations. Ultimately I'd like to use the probabilities for each response category in a simulation model such that these probabilities are used to define a