similar to: cluster.stats

Hi list, is there any direct way to obtain confidence intervals for the regression slope from lm, predict.lm or the like? (If not, is there any reason? This is also missing in some other statistics softwares, and I thought this would be quite a standard application.) I know that it's easy to implement but it's for explanation to people who faint if they have to do their own programming...

Hello all, I am using the clustering functions in R in order to work with large masses of binary time series data, however the clustering functions do not seem able to fit this size of practical problem. Library 'hclust' is good (though it may be sub par for this size of problem, thus doubly poor for this application) in that I do not want to make assumptions about the number of

Hi everybody, I'd like to know if there's an easy way for extracting outliers record from a dataset, in order to perform further analysis on them. Thanks Alessandro

Hello all. I wish to run k-means with "manhattan" distance. Since this is not supported by the function "kmeans", I turned to the "pam" function in the "fpc" package. Yet, when I tried to have the algorithm run with different starting points, I found that pam ignores and keep on starting the algorithm from the same starting-points (medoids). For my

Hi, I wanted the DICE coefficient (similarity measure for binary variables) to be calculated in R and found that the "igraph" package has the option of "similarity.dice" to do this. But, for this command, the input object should be an igraph object. But, I have a dataframe of columns containing 1's and 0's. Can I convert this dataframe into an igraph object, so that

Hi there, is there any R-function for k-nearest neighbour agglomerative hierarchical clustering? By this I mean standard agglomerative hierarchical clustering as in hclust or agnes, but with the k-nearest neighbour distance between clusters used on the higher levels where there are at least k>1 distances between two clusters (single linkage is 1-nearest neighbour clustering)? Best regards,

Dear list, R CMD check on my updated package now generated the following error: "LaTeX errors when creating DVI version. This typically indicates Rd problems." But the Rd files (and everything else) were checked as "OK" (I removed the problem about which I asked the list some hours ago, but answers are still appreciated because I rather created a rough workaround than

Hello: Does anyone know if there exists a package that handles methods for [ for dist objects? I would like to access a dist object using matrix notation e.g. dMat = dist(x) dMat[i,j] Thanks in advance to anyone who can point me in the right direction. [[alternative HTML version deleted]]

Dear list, I came across the following error for three of my newly written Rd-files: non-ASCII input and no declared encoding I can't make sense of this. Below I copied in one of the three files. Can anybody please tell me what's wrong with it? Thank you, Christian \name{tetragonula} \alias{tetragonula} \alias{tetragonula.coord} \docType{data} % \non_function{} \title{Microsatellite

Hi, I have a dataset which has around 138 variables and 30,000 cases. I am trying to calculate a mahalanobis distance matrix for them and my procedure is like this: Suppose my data is stored in mymatrix > S<-cov(mymatrix) # this is fine > D<-sapply(1:nrow(mymatrix), function(i) mahalanobis(mymatrix, mymatrix[i,], S)) Error in solve.default(cov, ...) : system is computationally

Dear R users, I am interested in clustering in R. In SAS we have some criteria for determining the number of clusters using the PROC CLUSTER procedure, which are "CCC" cubic clustering criterion (Sarl 1981), Psuedo F (PSF), and Psuedo T square (PST). My question is do thsese criterion exists in R, I tried to search and got one hit (BIC) in Mclust, which I am aware of, any input is

Is it possible to implement my own distance and mean for k-means clustering for any clustering package in R? Just looking for simple way, without creating a new package. karsar

Hi everyone? Does anybody know how can I estimate a Latent class multinomial (or conditional) logit using R? I have tried flexmix, poLCA, and they do not seem to support this model. thanks in advance adan -- View this message in context: http://r.789695.n4.nabble.com/Latent-class-multinomial-or-conditional-logit-using-R-tp4230083p4230083.html Sent from the R help mailing list archive at

Dear R helpers, I have a large data set with 36 variables and about 50.000 cases. The variabels represent labour market status during 36 months, there are 8 different variable values (e.g. Full-time Employment, Student,...) Only cases with at least one change in labour market status is included in the data set. To analyse sub sets of the data, I have used daisy in the cluster-package to create

Hi there, I just tried to install the package prabclus on a computer running Ubuntu Linux 9.04 using install.packages from within R. This gave me a message: Warning message: In install.packages("prabclus") : package ?prabclus? is not available I tried to do this selecting two different CRAN mirrors (same result) and with other packages (installing them works fine). Looking up the

Hello, I'm playing around with cluster analysis, and am looking for methods to select the number of clusters. I am aware of methods based on a 'pseudo F' or a 'pseudo T^2'. Are there packages in R that will generate these statistics, and/or other statistics to aid in cluster number selection? Thanks, John. --

hi r-help, i am doing kmeans clustering in stats. i tried for five clusters clustering using: kcl1 <- kmeans(as1[,c("contlife","somlife","agglife","sexlife",                         "rellife","hordlife","doutlife","symtlife","washlife",                       

Hi there, it seems that if I update R, it doesn't find previously installed packages anymore and is also not found by ESS. Actually the update has been done by our system administrator who assumed that there would be no problems with these things (I don't have root access to this system) and will perhaps not be too keen on installing everything else again. Is there any simple way how

I use kmeans to classify spectral events in high and low 1/3 octave bands: #Do cluster analysis CyclA<-data.frame(LlowA,LhghA) CntrA<-matrix(c(0.9,0.8,0.8,0.75,0.65,0.65), nrow = 3, ncol=2, byrow=TRUE) ClstA<-kmeans(CyclA,centers=CntrA,nstart=50,algorithm="MacQueen") This works well when the actual data shows 1,2 or 3 groups that are not "too close" in a cross plot.

Hello R users It's some time I am playing with a dataset to do some cluster analysis. The data set consists of 14 columns being geographical coordinates and monthly temperatures in annual files latitutde - longitude - temperature 1 -..... - temperature 12 I have some missing values in some cases, maybe there are 8 monthly valid values at some points with four non valid. I don't want to