similar to: Kullback Leibler Divergence

Hi all, x <- cbind(rnorm(500),rnorm(500)) KLdiv(x, eps=1e-4) KLdiv(x, eps=1e-5) KLdiv(x, eps=1e-6) KLdiv(x, eps=1e-7) KLdiv(x, eps=1e-8) KLdiv(x, eps=1e-9) KLdiv(x, eps=1e-10) ... KLdiv(x, eps=1e-100) ... KLdiv(x, eps=1e-1000) When calling flexmix::KLdiv using the given code I get results with increasing value the smaller I pick the accuracy parameter 'eps' until finally reaching

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,

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

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

Hi, I have a vector or random variables and I'm estimating the density using "bkde" function in the KernSmooth package. The out put contains two vectors (x and y), and the R documentation calls y as the density estimates, but my y-values are not exact density etstimates (since these are numbers larger than 1)! what is y here? Is it possible to get the true estimated density at each

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

Hi, I like to generate two normal densities such that the overlapping area between them is 0.35. Is there any code/package available in R to do that?? Regards, Lavan -- View this message in context: http://www.nabble.com/densities-with-overlapping-area-of-0.35-tp19384741p19384741.html Sent from the R help mailing list archive at Nabble.com.

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, 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:

Hi, Let say I have a normal density X~n(0,1) and I have a line y=0.01x+0.07. the following code generate the plots. x=seq(-10,10,length=100) plot(x,p1,type='n',ylab="Density",main="Overlap Measure",xaxt="n",yaxt="n") pi=dnorm(x,0,1) points(x,p1,type='l') abline(0.07,0.01) you can see that the curves intersects at 3 points. My question is

I am receiving the following error while executing KLdiv method: kl<-KLdiv(y) Error in function (classes, fdef, mtable) : unable to find an inherited method for function "KLdiv", for signature "data.frame" the code is as follows: require(flexmix) KLdiv(y) the object y (snippet, actual object contains 188 entries) is as follows: s1p5 s2p5 1 0.6 0.8 2 0.8

Hi all, when running KL on a small data set, everything is fine: require("flexmix") n <- 20 a <- rnorm(n) b <- rnorm(n) mydata <- cbind(a,b) KLdiv(mydata) however, when this dataset increases require("flexmix") n <- 10000000 a <- rnorm(n) b <- rnorm(n) mydata <- cbind(a,b) KLdiv(mydata) KL seems to be not defined. Can somebody explain what is going

Hi all, I wonder why KLdiv does not work with data.frames: n <- 50 mydata <- data.frame( sequence=c(1:n), data1=c(rnorm(n)), data2=c(rnorm(n)) ) # does NOT work KLdiv(mydata) # works fine dataOnly <- cbind(mydata$data1, mydata$data2, mydata$group) KLdiv(dataOnly) Any ideas? Is there a better implementation that can deal with data.frame or is there a simpler way of converting? Ralf

Hi,I'm wondering if anyone has run into this but I found out that I can't use Window's VPN connection and logon to Samba using Aliases. I can log into the server using the server's main NetBIOS name but I can't use any of the aliases. Whilst inside the network the aliases work fine.Any ideas on how to get around this?# Samba config file created using SWAT # from 192.168.1.200

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.

Hello, I am switching to R from Stata and I am having particular trouble with the transition from Stata's 'xtabond' and 'ivreg' commands to the "plm" package. I am trying to replicate some of the dynamic panel data work using the UK Employment data in Arellano and Bond (1991) and available as 'EmplUK' under the 'plm' package. I have been