[R] hierarchical clustering

hi there! i am searching for a possibility to plot the cluster fusion number against the distance in hierarchical clustering. i used the following code: distances = dist(my data[, c(1:11)], method=euclidean) eward = hclust(distances, method=ward) plot(eward, labels=my data[, 12],

[R] attribute evaluation

Hey there! I am searching for an attribute evaluation algorithm (such as based on Info gain, Gain ratio or Chi squared statistics). Is something like that available in R? Thanks for your reply. Best regards, Julia -- __ R-help@r-project.org mailing

Re: [R] NAIVE BAYES with 10-fold cross validation

Datum: Tue, 30 Oct 2007 17:03:49 -0400 Von: Kuhn, Max [EMAIL PROTECTED] An: Julia Kröpfl [EMAIL PROTECTED], r-help@r-project.org Betreff: RE: [R] NAIVE BAYES with 10-fold cross validation am trying to implement the code of the e1071 package for naive bayes, but it doens't really work, any

[R] NAIVE BAYES with 10-fold cross validation

hi there!! i am trying to implement the code of the e1071 package for naive bayes, but it doens't really work, any ideas?? i am very glad about any help!! i need a naive bayes with 10-fold cross validation: code: library(e1071) model - naiveBayes(code ~ ., mydata) tune.control -

[R] Q-type factor analysis

Hallo! Is there a package in R that does Q-type factor analysis? I know how to do principal component analysis, but haven't found any application of Q-type factor analysis. Thx, Julia -- Pt! Schon vom neuen GMX MultiMessenger gehört? Der kanns mit allen:

[R] pseudo code

Hey there! I got a pseudo code and don't know how to apply it to R, maybe someone can help me: Input: A dataset X, kmax: maximum number of clusters, num_subsamples: number of subsamples. Output: S(i; k) - a distribution of similarities between partitions into k clusters of a reference

[R] 10- fold cross validation for naive bayes(e1071)

Hallo! I would need a code for 10-fold cross validation for the classifiers Naive Bayes and svm (e1071) package. Has there already been done something like that? I tried to do it myself by applying the tune function first: library(e1071) tune.control - tune.control(random =F, nrepeat=1,