similar to: bootstraping sensitivity and specificity

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

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

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(

R Help on 'chisq.test' states that "if 'simulate.p.value' is 'TRUE', the p-value is computed by Monte Carlo simulation with 'B' replicates. In the contingency table case this is done by random sampling from the set of all contingency tables with given marginals, and works only if the marginals are positive... In the

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, 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

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))

>>>>> Gustavo Fernandez Bayon <gbayon at gmail.com> >>>>> on Thu, 24 Aug 2017 16:42:36 +0200 writes: > Hello, > While doing some enrichment tests using chisq.test() with simulated > p-values, I noticed some strange behaviour. The computed p-value was > extremely small, so I decided to dig a little deeper and debug >

> On 25 Aug 2017, at 11:23 , Jari Oksanen <jari.oksanen at oulu.fi> wrote: > > It is not about "really arge total number of observations", but: > > set.seed(4711);tabs <- r2dtable(1e6, c(2, 2), c(2, 2)); A11 <- vapply(tabs, function(x) x[1, 1], numeric(1));table(A11) > > A11 > 0 1 2 > 166483 666853 166664 > > There are

Dear all, I have a dataset with qualitative variables (factors) and I want to test the null hypothesis of independance between two variables for each pair by using appropriate tests on contingency tables. I first applied chisq.test and obtained dependance in almost all cases with extremely small p-values and warning messages. > chisq.test(table(data$ins.f, data$ins.st))$p.val [1]

Suppose I have a binary outcome (disease/no disease and all subjects had the same period of exposure) and 2 or 3 (categorical) predictors. I can obviously build a logistic regression model which describes the data, possibly including interaction terms, on a relative scale: model<-glm(disease~sex*race*prematurity,family=binomial) 1) Is there any way to extract information on the absolute

It is not about "really arge total number of observations", but: set.seed(4711);tabs <- r2dtable(1e6, c(2, 2), c(2, 2)); A11 <- vapply(tabs, function(x) x[1, 1], numeric(1));table(A11) A11 0 1 2 166483 666853 166664 There are three possible matrices, and these come out in proportions 1:4:1, the one with all cells filled with ones being most common. Cheers, Jari

Dear List: Most of the analysis I do involves contingency tables. I am migrating to R from Stata and I have a number of questions about using contingency tables in R. I suspect that most of the things I want to do are very short R scripts that people on this list probably have. I wonder if you would be willing to share them. First, the presentation of tables by table() is not

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