similar to: kmeans() compared to PROC FASTCLUS

Hi, I was wondering what the best equivalent to SAS's FASTCLUS and PROC CLUSTER would be. I need to be able to test the significance of the clusters by comparing the probability of obtaining an equal or greater pseudo F to the Bonferroni-corrected level. I will also need to plot r squared against the number of clusters. Thanks so much, Taisa [[alternative HTML version deleted]]

Hi I'm trying two combine two vectors that have different lengths. This without recursive the shorter one. E.g., a <- seq(1:3) b <- seq(1:6) Thanks in advance Serdar [[alternative HTML version deleted]]

Buenas tardes. Quisiera saber como puedo aplicar las reglas de un kmeans a otra base, para hacer un proceso de validaciòn de la segmentaciòn. En SAS, se cuenta con el argumento "OUTSEED", ¿què debo tener en cuenta en R? Mil gracias. -- Luis Alberto López González [[alternative HTML version deleted]]

Hi, I'm currently trying desperately to get the nnet function for training a neural network (with one hidden layer) to perform a regression task. So I run it like the following: trainednet <- nnet(x=traindata, y=trainresponse, size = 30, linout = TRUE, maxit=1000) (where x is a matrix and y a numerical vector consisting of the target values for one variable) To see whether the network

Hi all, I am trying to develop a neural network with single target variable and 5 input variables to predict the importance of input variables using R. I used the packages nnet and RSNNS. But unfortunately I could not interpret the out put properly and the documentation of that packages also not giving proper direction. Please help me to find a good package with a proper documentation for neural

Dear all, I am looking for a clustering method usefull to classify the countries in some clusters taking account of: a) the geographical distance (in km) between countries and b) of some macroeconomic indicators (gdp, life expectancy...). Are there some packages in R usefull for this? Thanks a lot for your help, Dorina

Dear all, I am currently looking for a book about support vector machines for regression and classification and am a bit lost since they are plenty of books dealing with this subject. I am not totally new to the field and would like to get more information on that subject for later use with the e1071 <http://cran.r-project.org/web/packages/e1071/index.html> package for instance. Does

Hi all, I need some help with R. I am looking for a function that puts R back into a vanilla state (exactly the same when I just started it). Specifically I want all objects in the workspace removed and all non-base packages detached and unloaded; all base packages that are loaded on startup should remain loaded (and preferably a .Rprofile executed as well). It would also be good if all the

Hi, Am working on neural network. Below is the coding and the output > library (nnet) > uplift.nn<-nnet (PVU~ConsumerValue+Duration+PromoVolShare,y,size=3) # weights: 16 initial value 4068.052704 final value 3434.194253 converged > summary (uplift.nn) a 3-3-1 network with 16 weights options were - b->h1 i1->h1 i2->h1 i3->h1 16.64 6.62 149.93

I have a data set with about 30,000 training cases and 103 variable. I've trained an SVM (using the e1071 package) for a binary classifier {0,1}. The accuracy isn't great. I used a grid search over the C and G parameters with an RBF kernel to find the best settings. I remember that for least squares, R has a nice stepwise function that will try combining subsets of variables to find

Hi, I'd like to download some data files from a remote server, the problem here is that some of the files actually don't exist, which I don't know before try. Just wondering if a function in R could tell me if a file exists on a remote server? I searched this mailing list and after read severals mails, still clueless. Any help will be highly appreciated. B.C.

Hi there, this is more a comment and a solution rather than a question, but I thought I'd post it since it cost some time to dig down to the issue and maybe someone else could run into this. I'm using the nnet function for a regression task. I'm inputting the following data frame: > 'data.frame': 4970 obs. of 11 variables: $ EC25 : num 67.5 67.6 68 69 69.5 ... $

Hello to everyone! I use .Call to call a C function without arguments wich calls a fortran optimization package. My C function uses others C and Fortran functions and it works fine when I call it from a main() in a C program. But when I call it from R with .Call("name_of_the_c_function"), R gives me some weird output. This weird output is a worng answer to my optimization problem

I had a look at the online documentation, and didn't see from that what is my problem. If I should have, pardon me. Here is my session. As I understand the documentation, this should work with only an hclust object. I get a similar error when in include a FUN argument. I am using V2.4.0. > hc Call: hclust(d = dist(mtx2, method = "manh"), method =

Hello: What I want to do is quite simple, but I can't find a way. I have a data frame with several points (x and y coords). I want to add another column with cluster membership. For example aggregate all the points that stand within a distance of 40 from each other. I've tried using "nncluster" from the package nnclust, but the results are not correct, for some