search for: classagr

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 <- matrix(un...

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 )

...31 3.341 6.387 > I can get the model to give some sort of prediction by doing this: > pred = predict(logistic.model, data) > pred[pred <= 1.5] = 0 > pred[pred > 1.5] = 1 > t = table(pred, data[,24]) > t pred 0 1 0 102 253 1 255 3701 > > classAgreement(t) $diag [1] 0.8821619 $kappa [1] 0.2222949 $rand [1] 0.7920472 $crand [1] 0.1913888 > but as you can see I am using a break point well outside the range 0 to 1 and the kappa is rather low (I think). I am a bit of a novice in this, and the results worry me. Can anyone comme...

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.

...8819 -1.298372615 1.185368711 -1.148974059 0.358234433 0.671495255 1043 1044 1045 1046 1047 1048 1049 0.683771224 0.663767266 2.009012643 0.196591464 2.063417812 0.823472345 0.696638161 [runs on to 2000] However, I then want to check for classAgreement (an e1701 package function). First I want a table. I do this: > t = table(pred,cuData[1001:2000,24]) > t pred 0 1 -8.90070098980106 0 1 -8.0484071844879 0 1 -7.79298548775523 1 0 -7.18338330609013 1 0 [runs on] when I ex...

...featuring: - Multi-Class Classification - weighting of classes for C-classification (for asymmetric sample sizes) - nu-regression - Formula Interface - k-fold cross-validation In addition, an introductory article is provided in directory `docs/' (svmdoc.pdf). o classAgreement now features an option to match factor levels o updated API design for the fuzzy clustering functions (cmeans, cshell, scaclust). Documentation updates and function name changes (cmeanscl -> cmeans, validity.measures -> fclustIndex) -- ------------------------------------...

...gamma: 0.04347826 epsilon: 0.1 Number of Support Vectors: 970 But when I applied it: > pred = predict(svm.model, data[3001:4000,1:23]) > pred[pred > .5] = 1 > pred[pred <= .5] = 0 > t = table(pred,data[3001:4000,24]) > t pred 0 1 1 65 935 > classAgreement(t) $diag [1] 0.065 $kappa [1] 0 $rand [1] 0.8783283 $crand [1] 0 It didn’t produce really good results. Will best.svm get me the best svm? Have I given it the wrong parameters? Any help most welcome. Stephen -- No virus found in this outgoing message. Checked by AVG Anti-Viru...

...iris[-split, ] naive_bayes <-naiveBayes(class~.,data=wine_training) x_testing <- subset(wine_testing, select = -class) y_testing <- wine_testing$class # just grab Species variable of iris_training pred <- predict(naive_bayes, x_testing) tab<-table(pred, y_testing) ca <- classAgreement(tab) print(tab) print(ca) when I enter this code in I get the error Error in -class : invalid argument to unary operator If anybody could give me anysort of advice this would be most welcome,Thanks -- View this message in context: http://www.nabble.com/Error-in--class-%3A-invalid-argu...

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).

...ng 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: > warnings() Warning messages: 1: number of rows of result is not a multiple of vector length (arg 2) in: cbind(1, 1:N, predict(object$mtrees[[i]], newdata, type = "class")) 2: number of rows of result...

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

...el = FALSE, 975 class.weights = NULL, 976 cross = 0, 977 fit = TRUE, 978 cache = 40, 979 tol = 0.001, 980 shrinking = TRUE, 981 ...) 982 { ... 1723 }) 1724 1725 1726 1727 .classAgreement <- function (tab) { ... 1737 } 1738 1739 ## List Interface 1740 1741 1742 setMethod("ksvm",signature(x="list"), 1743 function (x, 1744 y = NULL, 1745 type = NULL, 1746 kernel = "stringdot",...

...red = prediction2,testset$class) tab3 <- table(pred = prediction3,testset$class) tab4 <- table(pred = prediction4,testset$class) ecr1<-(tab1[2,1]+tab1[1,2])/sum(tab1) ecr2<-(tab2[2,1]+tab2[1,2])/sum(tab2) ecr3<-(tab3[2,1]+tab3[1,2])/sum(tab3) ecr4<-(tab4[2,1]+tab4[1,2])/sum(tab4) classAgreement(tab) #Bagging EnSVM library(colorspace) library(rpart) library(e1071) library(MASS) dataset <- read.csv("E:/thesis/SVM/hepatitis data csv.csv",header=T,sep=";") attach(dataset) m<-10 k<-10 index <- 1:nrow(dataset) testindex <- sample(index, trunc(lengt...

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