similar to: VPN and NetBIOS aliases

Hi, I have come to my wits end again (lately, it's a very short trip). I have been trying to connect to Samba 3.0.21b but Windows keeps throwing up the logon dialog. I'm using "Map network drive" to try to mount this share. It seems the problem is only on the Windows side since I have tested the connection through the Unix account. Both user id and password work fine.

Hi, Problem: Out of the blue Vista on my laptop decides it doesn't want to mount a share over PPTP VPN connection. I can mount the share in the local network but I can't do it over VPN. When trying to access the server, using "net view \\172.16.1.71" it shows the error in the subject. Platform/Versions: OS: Sun Solaris 10 11/06 release Samba version:

I use rsync on an SSD which is slow at writing, but very fast at reading, so it is important to me to avoid writing data. I had hoped rsync would only write changes in files when using --inplace, but it seems not: # Make a 1 GB file $ perl -e 'print "x"x1000000000' > 1g # rsync it (as expected this costs 1 GB writes) $ ./rsync-3.1.0/rsync --progress --inplace -cva 1g 1g.copy

I am using the following code: library(lattice) data<-read.csv("data.csv") attach(data) fig<-xyplot(S_t~month|event, key = list(text=list(lab=c("Time to first CV event - Data", "Survival post first CV event - Model", "Survival post first MIA/CA event - Data",

Hi there, I'm trying to find the KL divergence measure between a prior and it's posterior distributions, and I'm using the KLdiv method in the flexmix package. plese see the example below: require(flexmix) x=seq(-4,4,length=100) d1=dnorm(x,0,1) d2=dunif(x,-3,3) y=cbind(d1,d2) kl=KLdiv(y) but let say, x1=seq(-5,5,length=100) d3=dunif(x1,-3,3) y1=cbind(d1,d3) kl1=KLdiv(y1) Notice

I've been asked about "Canonical Variance Analysis" (CVA). I don't see any reference to it searching the R site. Does it go by other names? Genstat describes it thus: Canonical variates analysis operates on a within-group sums of squares and products matrix, calculated from a set of variates and factor that specifies the grouping of units. It finds linear combinations of the

I am trying to calculate a Kullback-Leibler divergence from two vectors with integers but get NA as a result when trying to calulate the measure. Why? x <- cbind(stuff$X, morestuff$X) x[1:5,] [,1] [,2] [1,] 293 938 [2,] 293 942 [3,] 297 949 [4,] 290 956 [5,] 294 959 KLdiv(x) [,1] [,2] [1,] 0 NA [2,] NA 0 Best, Ralf

I am performing some analysis over a large data frame and would like to conduct repeated analysis over grouped-up subsets. How can I do that? Here some example code for clarification: require("flexmix") # for Kullback-Leibler divergence n <- 23 groups <- c(1,2,3) mydata <- data.frame( sequence=c(1:n), data1=c(rnorm(n)), data2=c(rnorm(n)), group=rep(sample(groups, n,

Hi, This is more of a general stat question. I am looking for a easily computable measure of a distance between two empirical distributions. Say I have two samples x and y drawn from X and Y. I want to compute a statistics rho(x,y) which is zero if X = Y and grows as X and Y become less similar. Kullback-Leibler distance is the most "official" choice, however it needs estimation of

Dear R-Users, I have the CDF of a discrete probability distribution. I now observe a change in this CDF at one point. I would like to find a new CDF such that it has the shortest Kullback-Leibler Distance to the original CDF and respects my new observation. Is there an existing package in R which will let me do this ? Google searches based on entropy revealed nothing. Kind regards, Tolga

Dear R users, I am pleased to announce the release of the new BiplotGUI package on CRAN. Biplots are graphs in which the samples and all the variables of a data matrix are represented simultaneously. They can be very useful for exploring multivariate data. The BiplotGUI package allows users to construct and interact with biplots as they are described in "Biplots" (Gower and Hand,

Dear R users, I am pleased to announce the release of the new BiplotGUI package on CRAN. Biplots are graphs in which the samples and all the variables of a data matrix are represented simultaneously. They can be very useful for exploring multivariate data. The BiplotGUI package allows users to construct and interact with biplots as they are described in "Biplots" (Gower and Hand,

The version 1.1 update to my glmnetUtils package is now available on CRAN. glmnetUtils provides a formula interface for the 'glmnet' package for elasticnet regression, a method for cross-validating the alpha parameter, and other quality-of-life tools. >From the NEWS.md file: - Now allows interaction and expression terms without requiring use.model.frame=TRUE. This works in an

The version 1.1 update to my glmnetUtils package is now available on CRAN. glmnetUtils provides a formula interface for the 'glmnet' package for elasticnet regression, a method for cross-validating the alpha parameter, and other quality-of-life tools. >From the NEWS.md file: - Now allows interaction and expression terms without requiring use.model.frame=TRUE. This works in an

A new version of gss, version 0.8-2, is on CRAN now. Numerous new functionalities have been added since my last r-announce post. An ssanova1 suite has been added since version 0.7-4. It implements low-dimensional approximations of the smoothing spline ANOVA models of the ssanova suite. ssanova1 scales much better than ssanova with large sample sizes. A gssanova1 suite is added for non

A new version of gss, version 0.8-2, is on CRAN now. Numerous new functionalities have been added since my last r-announce post. An ssanova1 suite has been added since version 0.7-4. It implements low-dimensional approximations of the smoothing spline ANOVA models of the ssanova suite. ssanova1 scales much better than ssanova with large sample sizes. A gssanova1 suite is added for non

Dear R users, I used sm.density function in the sm package and kde2d() in the MASS package to estimate the bivariate density. Then I calculated the Kullback leibler divergence meassure between a distribution and the each of the estimated densities, but the asnwers are different. Is there any difference between the kde2d and sm.density estimates? if there is a difference, then which is the best

Dear R-Users, my objective is to measure the overlap/divergence of two probability density functions, p1(x) and p2(x). One could apply the chi-square test or determine the potential mixture components and then compare the respective means and sigmas. But I was rather looking for a simple measure of similarity. Therefore, I used the concept of 'intrinsic discrepancy' which is defined as:

Dear R users, According Brockwell & Davis (1991, Section 9.3, p.304), the penalty term for computing the AIC criteria is "p+q+1" in the context of a zero-mean ARMA(p,q) time series model. They arrived at this criterion (with this particular penalty term) estimating the Kullback-Leibler discrepancy index. In practice, the user usually chooses the model whose estimated index is

Sorry, forgot to send this to the list originally. -----Original Message----- From: Mike Waters [mailto:dr.mike at ntlworld.com] Sent: 06 May 2005 18:40 To: 'Campbell' Subject: RE: [R] distance between distributions -----Original Message----- From: r-help-bounces at stat.math.ethz.ch [mailto:r-help-bounces at stat.math.ethz.ch] On Behalf Of Campbell Sent: 06 May 2005 11:19 To: