search for: classagreements

...Kappa test for nominally classified data 91 categories - 22 methods kappa (Siegel) = 0.168593 , Z = 2.50298 , p = 0.00615762 kappa (2*PA-1) = 0.71485 it seems like the second method (type='counts') is the correct way to use a contingency table... but am i correct? Secondly, when using the classAgreements() function I get different numbers: classAgreement(table(A,B)) $diag [1] 0.03296703 $kappa [1] 0.02180419 $rand [1] 0.9874325 $crand [1] 0.7648124 Perhaps I am mis-reading the relevant manual pages. Can anyone shed some light on the proper use, and therfore interpretation of these two method...

I was wondering, whether there is a way to have statistical significance test for cluster agreement. I know that I can use classAgreement() function to get Rand index, which will give me some indication whether the clusters agree or not, but it would be interesting to have a formal test. Thanks.

Is there a function that calculates the inter-rater agreement index (kappa) in R? Thanks ../Murli [[alternative HTML version deleted]]

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

Hola a todos, tengo unos datos clasificados, es decir un factor con etiquetas de 1 a 14 y quiero comprobar las coincidencias con un test (también otro factor). Lo que me interesa obtener más que la matriz de confusión o el indice kappa, es otro factor con las coincidencias entre ambos factores (clasificación y test). Es decir 1 si coinciden las etiquetas y 0 si no coinciden, Supongo que para

Dear WizaRds, I have been trying to compute the adjusted Rand index as by Hubert/ Arabie, and could not correctly approach how to define a partition object as in my last request yesterday. With package fpc I try to work around the problem, using my original data: mat <- matrix( c(6,7,8,2,3,4,12,14,14, 14,15,13,3,1,2,3,4,2, 15,3,10,5,11,7,13,6,1, 15,4,10,6,12,8,12,7,1), ncol=9, byrow=T )

Hi I am working on corpora of automatically recognized utterances, looking for features that predict error in the hypothesis the recognizer is proposing. I am using the glm functions to do logistic regression. I do this type of thing: * logistic.model = glm(formula = similarity ~., family = binomial, data = data) and end up with a model: > summary(logistic.model) Call:

Basically, the `Detail' section of ?tune says it all: Details: As performance measure, the classification error is used for classification, and the mean squared error for regression. ... Andy > From: Tae-Hoon Chung > > Hi, all; > > Basically, the subject contains the all information I need to know. > In e1071 library, there are functions to tune parameters.

I am trying to do some verification across a large dataset, cuData, that has 23 columns. Column 23 (similarity) is the outcome 0 or 1 and the other columns are the features. I do this: verificationglm.model <- glm(formula = similarity ~ ., family=binomial, data=cuData[1:1000,]) and produce the model: > summary(verificationglm.model) Call: glm(formula = similarity ~ ., family =

A new version of e1071 has been released to CRAN which should be much easier to install on a lot of platforms because reading/writing PNM images has been moved to the pixmap package, hence there are no longer dependencies on external libraries and no configure mechanism. For the authors, Fritz Leisch ********************************************************** Changes in Version 1.2-0: o

Hi I am trying to fit an svm to predict speech recognition errors. I am using best.svm like this: svm.model = best.svm(data[1:3000,1:23],data[1:3000,24],tunecontrol = tune.control()) I got this: > print(svm.model) Call: best.svm(x = data[1:3000, 1:23], tunecontrol = tune.control(), data[1:3000, 24]) Parameters: SVM-Type: eps-regression SVM-Kernel: radial cost: 1

Hi guys I have been using R for a few months now and have come across an error that I have been trying to fix for a week or so now.I am trying to build a classifer that will classify the wine dataset using Naive Bayes. My code is as follows library (e1071) wine<- read.csv("C:\\Rproject\\Wine\\wine.csv") split<-sample(nrow(wine), floor(nrow(wine) * 0.5)) wine_training <-

Hi, I searched a lot on the internet but was unable to find the function for calculating the kappa statistics for intra-observer reliabilty. Can anybody help me in the this regards. Thanks, Shreyasee [[alternative HTML version deleted]]

Hi. I am wondering if anybody has studied and/or written code in R to calculate the distance between 2 "trees". For example, if one does a hierarchical agglomerative clustering and say, a hierachical divisive clustering (represented as trees) and wishes to compute a metric on them. I am thinking of something like the symmetric difference as mentioned in Margush and McMorris (1982).

I am trying to use bagging like this: > bag.model <- bagging(as.factor(nextDay) ~ ., data = spi[1:1250,]) > pred = predict(bag.model, spi[1251:13500,-9]) There were 25 warnings (use warnings() to see them) > t = table(pred, spi[1251:13500,9]) > t pred 0 1 0 42 40 1 12 22 > classAgreement(t) but I get the warning. The warnings run like this: >

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 doing this sort of thing: POLY: > > obj = best.tune(svm, similarity ~., data = training, kernel = "polynomial") > summary(obj) Call: best.tune(svm, similarity ~ ., data = training, kernel = "polynomial") Parameters: SVM-Type: eps-regression SVM-Kernel: polynomial cost: 1 degree: 3 gamma: 0.04545455 coef.0: 0

Hi, A. In a nutshell: The training error, obtained as "error (ret)", from the return value of a ksvm () call for a eps-svr model is (likely) being computed wrongly. "nu-svr" and "eps-bsvr" suffer from this as well. I am attaching three files: (1) ksvm.R from the the kernlab package, un-edited, (2) ksvm_eps-svr.txt: (for easier reading) containing only eps-svr

Dear Sir, I got a problem with my program. I would like to classify my data using bagging support vector machine ensemble. I split my data into training data and test data. For a given data sets TR(X), K replicated training data sets are first randomly generated by bootstrapping technique with replacement. Next, Support Vector Mechine (SVM) is applied for each bootstrap data sets. Finally, the

Whether the two variables have the same units does not matter. Moreover, even if there were some way of converting cm to kg the correlation would still be the same because the correlation is invariant under unit conversion as it is invariant under multiplication of its arguments by a constant. As for your second question, the correlation estimator is a continuous function of each of the