similar to: Cluster validation

Does any package available in R contain the V-fold cross-validation method for determining the right number of clusters? Any help greatly appreciated Kind regards Dr Graham Leask Economics and Strategy Group Aston Business School Aston University Aston Triangle Birmingham B4 7ET Tel: Direct line 0121 204 3150 email g.leask@aston.ac.uk [[alternative HTML version deleted]]

Is there a function available in R that implements Mojena's Upper Tail Rule or that draws a Mojena plot? I would also like to find a function that implements Duda and Hart's stopping rule. Finally with function cophenet how can I achieve a straightforward Cophenet correlation coefficient (i.e. one number for example 0.876) that gives the agreement between the structure of the

Has anyone implemented Tibrishani's gap statistic in R or S plus? If so I would greatly appreciate the relevant script file. Any help will be much appreciated Kind regards Dr Graham Leask Economics and Strategy Group Aston Business School Aston University Aston Triangle Birmingham B4 7ET Tel: Direct line 0121 204 3150 email g.leask@aston.ac.uk [[alternative HTML version deleted]]

Dear R users, I know cross-validation does not work in rpart with user defined split functions. As Terry Therneau suggested, one can use the xpred.rpart function and then summarize the matrix of the predicted values into a single "goodness" value. I need only a confirmation: set for example xval=10, if I correctly understood a single column of the matrix obatined by xpred.rpart gives

Hi, I ran two svm models in R e1071 package: the first without cross-validation and the second with 10-fold cross-validation. I used the following syntax: #Model 1: Without cross-validation: > svm.model <- svm(Response ~ ., data=data.df, type="C-classification", > kernel="linear", cost=1) > predict <- fitted(svm.model) > cm <- table(predict,

Hi, I was trying to get the optimal 'k' for the knn. To do this I was using the following function : knn.cvk <- function(datmat, cl, k = 2:9) { datmatT <- (datmat) cv.err <- cl.pred <- c() for (i in k) { newpre <- as.vector(knn.cv(datmatT, cl, k = i)) cl.pred <- cbind(cl.pred, newpre) cv.err <- c(cv.err, sum(cl != newpre)) }

Hi, I ran two svm models in R e1071 package: the first without cross-validation and the second with 10-fold cross-validation. I used the following syntax: #Model 1: Without cross-validation: > svm.model <- svm(Response ~ ., data=data.df, type="C-classification", > kernel="linear", cost=1) > predict <- fitted(svm.model) > cm <- table(predict,

Hi there, I'm trying clustering methods on flow cytometry data. We want to evaluate the clustering results and compare the validation methods. So far the cluster validation functions I found in R are: cluster.stats{fpc} cl_agreement{clue} Are there other validation functions in R? Thank you! [[alternative HTML version deleted]]

Dear R users I'd like to hear from someone if there is a function to do a repeated k-fold cross-validation for a lm object and get the predicted values for every observation. The situation is as follows: I had a data set composed by 174 observations from which I sampled randomly a subset composed by 150 observations. With the subset (n = 150) I fitted the model: y = a + bx. The model

Hi, according to ?rvm the relevance vector machine function as implemented in the kernlab-package has an argument 'cross' with which you can perform k-fold cross validation. However, when I try to add a 10-fold cross validation I get the following error message: Error in match.arg(type, c("C-svc", "nu-svc", "kbb-svc", "spoc-svc",

Seems crossval from library(bootstrap) can only be used for leave-one-out and k-fold cross validation? Here is a dumb question, suppose n=80, how to do exactly leave-50-out cross validation? K-fold cross validation is not eligible for this case since n/ngroup is not an integer. Thanks!

Hi R folks, I am using R version 2.2.1 for Unix. I am exploring the rpart function, in particular the rpart.control parameter. I have tried using different values for xval (0, 1, 10, 20) leaving other parameters constant but I receive the same tree after each run. Is the10 fold cross-validation default still running every time? I would expect the trees to change at least a little when I

Hi all, Has anyone tried to do k-fold cross validation for flexible discriminant analysis ( mda library), for example, using crossval() in bootstrap? The problem is that the function crossval() requires a separate matrix for predictors and another for responses, whereas the function fda(), using the formula argument only. Is there another way of doing k-fold cross validation for functions which

Hi, I am using the Random Forest package to classify observations into one of two classes. My data is unbalanced with the minority class accounting for 7% of total data set. I have heard the 10-Fold Cross validation can help me with improving classification. But being new at most of this it's not something I can do from scratch on my own. So I have spent all this morning trying to find a

Hi, this is to announce a new version (1.1-2) of my package fpc. Apart from the stuff already present in the older version (methods for fixed point clustering and clusterwise regression, somewhat bug-cleaned and with faster examples) there is now a function cluster.stats, which computes some distance-based statistics often used for cluster validation, description and decision about the number of

Hi, this is to announce a new version (1.1-2) of my package fpc. Apart from the stuff already present in the older version (methods for fixed point clustering and clusterwise regression, somewhat bug-cleaned and with faster examples) there is now a function cluster.stats, which computes some distance-based statistics often used for cluster validation, description and decision about the number of

Hello, I did a cross-validation using cvlm from DAAG package but wasn't sure how to assess the result. Does this result means my model is a good model? I understand that the overall ms is the mean of sum of squares. But is 0.0987 a good number? The response (i.e. gailRel5yr) has min,1st Quantile, median, mean and 3rd Quantile, and max as follows: (0.462, 0.628, 0.806, 0.896, 1.000, 2.400) ?

Hello list, I'm trying to generate classifiers for a certain task using several methods, one of them being decision trees. The doubts come when I want to estimate the cross-validation error of the generated tree: tree <- rpart(y~., data=data.frame(xsel, y), cp=0.00001) ptree <- prune(tree, cp=tree$cptable[which.min(tree$cptable[,"xerror"]),"CP"]) ptree$cptable

Hello list, I'm having a problem with custom functions in rpart, and before I tear my hair out trying to fix it, I want to make sure it's actually a problem. It seems that, when you write custom functions for rpart (init, split and eval) then rpart no longer cross-validates the resulting tree to return errors. A simple test is to use the usersplits.R function to get a simple, custom

Hi All, Except the "Rand Index", "Dunn Index" and "Silhouette width", are there other cluster validation methods in R? Could you please also specify the function? Thanks! [[alternative HTML version deleted]]