similar to: difference between sm.density() and kde2d()

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

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

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

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.

Hello! I am trying to do a smooth with the kde2d function, and I'm getting an error message about NAs. Does anyone have any suggestions? Does this function not do well with NAs in general? fit <- kde2d(X, Y, n=100,lims=c(range(X),range(Y))) Error in if (from == to || length.out < 2) by <- 1 : missing value where TRUE/FALSE needed Thanks in advance!! Jen [[alternative

Dear all, Please, look at the following code: attach(geyser) f1 <- kde2d(duration, waiting, n = 5) a <- 0 for (i in 1:5){ for (j in 1:5){ a <- a + f1$z[i,j] } } As far as I understood from Help kde2d returns matrix elements of which are values of joint probability mass function Pr(X=x,Y=y) therefore, sum of its elements should sum to 1. Which is not the case from my check. Where is

Hello, I am a little bit confused regarding the density values obtained from the function kde2d() from the package MASS because the are not in the intervall [0,1] as I would expect them to be. Here is an example: x <- c(0.0036,0.0088,0.0042,0.0022,-0.0013,0.0007,0.0028,-0.0028,0.0019,0.0026,-0.0029,-0.0081,-0.0024,0.0090,0.0088,0.0038,0.0022,0.0068,0.0089,-0.0015,-0.0062,0.0066) y <-

Hi, The silverman's paper introduction offer how to find a mode for one dimensional data based on software http://www.stanford.edu/~kasparr/software/silverman.r, for two dimensional data I use kde2d to smooth it out first, then I get a matrix of densities for all the X(one dimension) cross Y(another dimension). I sort X and Y first before I pass the values to kde2d(x, y, c(hx, hy)), the

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:

Hallo, I want to use kde2d to visualize data on a sphere given in spherical coordinates. Now the problem is, that "phi == 2*pi = 0", so in principal I have to connect (in a graphical view) the left and right border of my plot (and the bottom and top). Has anyone any idea how to do that ? Thanks, Manuel -- ------------------------------------- Manuel Metz Sternwarte der

Good evening, I am Marta Colombo, student at Milan's Politecnico. Thank you very much for your kindness, this mailing list is really useful. I am using the function kde2d for two-dimensional kernel density estimation and I'd like to know something more about this kind of density estimator. In particular I'd like to know: what bandwidth is used ? Thank you in advance for your attention

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

The Problem: how to do the validation. I have not written it correctly!!! Given two tables: sizes and prices; with a relation: has_many ,belongs_to a Price.unit_price for a given Size.meassure must be marked ''true'' as the standard unit_price for that meassure. There will be many other unit_prices for a given meassure, those must be marked ''false'' table