similar to: statistical test for comparison of two classifications (nominal)

Hi, im little bit confused about Cohen's Kappa and i should be look into the Kappa function code. Is the easy formula really wrong? kappa=agreement-chance/(1-chance) many thanks christian ############################################################################### true-negativ:7445 false-positive:3410 false-negativ:347 true-positiv:772 classification-aggrement:68,6%

Hello, I?m looking for a way to measure the goodness of fit of my model with Cohen?s Kappa (scaling between 0 and 1). The kappa function does not give the results I?m looking for. Heres the code: z<-glm(x~y,binomial) kappa(z, exact = T) Does anyone know more? many thanks Christian -- _______________________________________________ Search for businesses by name, location, or phone number.

Hi, I am wondering if anyone can suggest how to test the equality of 2 proportions. The caveat here is that the 2 proportions were calculated from the same number of samples using 2 different tests. So essentially we are comparing 2 accuracy rates from same, say 100, samples. I think this is like a paired test, but don't know if really we need to consider the "paired" nature of the

Hi, I am a new user of R. I have 2 series of nominal data (2 series of answers for the same question) and I want to calculate the correlation between these 2 series. I've tried to use the correlation function (corr, ...) but all are for numeric data... Does anyone know what function should I use for the nominal data? Thanks a lot, Bianca

Hello, I am using logistic discriminant analysis to check whether a known classification Yobs can be predicted by few continuous variables X. What I do is to predict class probabilities with multinom() in nnet(), obtaining a predicted classification Ypred and then compute the percentage P(obs) of objects classified the same in Yobs and Ypred. My problem now is to figure out whether P(obs) is

Dear all, I have a question on Cohen's kappa: Assume I have two datasets, one has 500 objects, 10 methods and the other, 1000 different objects, 20 different methods. Could I compare between the two datasets to conclude the 10 methods are more "concordant" than the 20 ones by looking at some output, for example, cohen.kappa{concord} ? One more, could anyone explain in brief,

Hi, I've got two vectors with ratings from two coders, like this: x<-c("red", "yellow", "blue", "red") #coder number 1 y<-c("red", "blue", "blue", "red") #coder number 2 I want to find Cohen's Kappa using the wkappa function in the psych package. The only example in the docs is using a matrix, which

Dear R-user, Did anybody know how to calculate the kappa statistics of two vectors for the agreement? for example: a<-c(0,1,0,0,1,0) b<-c(0,1,1,0,0,1) I know the percent of agreement is 3/6, but how to get the kappa? [[alternative HTML version deleted]]

Dear list, I calculated the kappa-accuracy for two differnt classifications. How can I test now the two kappa-value for significance? thanks, Mark ..................................................................... Markus Schwarz Wissenschaftliche Mitarbeiterin Eidg. Forschungsanstalt WSL Forschungsprogramm Musterland Z??rcherstrasse 111 CH-8903 Birmensdorf Telefon +41-44-739 22 87 Fax

Greetings. I've been experimenting with some algorithms for document classification (specifically, a Naive Bayes classifier and a kNN classifier) and I would now like to calculate some inter-rater reliability scores. I have the data in a PostgreSQL database, such that for each document, each measure (there are 9) has three variables: ap_(measure), nb_(measure), and knn_(measure). ap is me

Hi, I am having problems getting cohen's kappa to work. I have been using the function: ><-ckappa(x,y) from the psy package. I am trying to test for inter-observer reliability, I have 2 observers and 26 categories, however, the two observers might not necessarily have the same range of categories (I have unequal score ranges). However, I thought R could cope with this. Each time I

Hello, I'm new to R but wan't to use it to compute the statistics of my medical study. The study includes several parameters for a number of patients. Each parameter was assessed by a number of readers, once with a special condition, once without. Now I have a data.frame with colums like: PatientID, ReaderID, SpecialCond(yes/no), Parameter1, Parameter2..... the rows are not sorted, and

Greetings, I am a bit confused by the results returned by the functions: cohen.kappa {concord} classAgreement {e1071} when using a two-way table. for example, if I have an matrix A, and a similar matrix B (same dimensions), then: matrix A and B can be found: http://casoilresource.lawr.ucdavis.edu/drupal/files/a_40.txt http://casoilresource.lawr.ucdavis.edu/drupal/files/b_40.txt A <-

Dear R list, I have a function specifying my log-likelihood, and now I need to set the constraint that *alpha > kappa*, could anyone help me with setting this in my function? the function is defined as follows: mll <- function(param){ n <- length(x) psi <- numeric(n) psi[1] <- 1.0 a0 <- exp(param[1]); a1 <-exp(param[2]); b1 <- exp(param[3]); *alpha *<-

Hi all, I have a list of 20000 data, and the list look like below. I wonder what is the simplest way to extract 'kappa' value (or 'xi' or 'alpha' for the matter) from each of the data. How can I simply code it without having to change the list to a dataframe first? Many thanks! $X19997 xi alpha kappa 784.7718640 165.4065141 -0.2709599 $X19998

I have started using the circular package but it is not recognizing the function arrows.circular. I attempted to use the example provided in the circular manual. Here is the example code using the circular package: plot(rvonmises(10, circular(0), kappa=1)) arrows.circular(rvonmises(10, circular(0), kappa=1)) arrows.circular(rvonmises(10, circular(0), kappa=1), y=runif(10), col=2)

Hello, When evaluating different learning methods for a categorization problem with the (really useful!) caret package, I'm getting confusing results from the Kappa computation. The data is about 20,000 rows and a few dozen columns, and the categories are quite asymmetrical, 4.1% in one category and 95.9% in the other. When I train a ctree model as: model <- train(dat.dts,

Hi All, I am using fleiss kappa for inter rater agreement. Are there any know issues with Fleiss kappa calculation in R? Even when I supply mock data with total agreement among the raters I do not get a kappa value of 1. instead I am getting negative values. I am using the irr package version 0.70 Any help is much appreciated. Thanks and Regards M [[alternative HTML version deleted]]

Hello, Consider MCMC sampling with metropolis / metropolis hastings proposals and a density function with a given valid parameter space. How are MCMC proposals performed if the parameter could be located at the very extreme of the parameter space, or even 'beyond that' ? Example to express it and my very nontechnical 'beyond that': The von Mises distribution is a circular

Dear list, I'm migrating a project from Matlab to R, and I'm facing a relatively complicated problem for nls. My objective function is below: >> objFun <- function(yEx,xEx,tEx,gamma,theta,kappa){ yTh <- pdfDY(xEx,tEx,gamma,theta,kappa) sum(log(yEx/yTh)^2) } The equation is yTh=P(xEx,tEx) + noise. I collect my data in: >> data <-