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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.

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

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, I posted this message earlier in "Rmetrics" and I don't know whether I posted in the wrong place, so I'm posting it again in Rhelp. I have a function in x and y and let's call it f(x,y). I need to get the Hessian matrix. i.e I need (d^2f/dx^2), (d^2f/dxdy), (d^2f/dydx), (d^2f/dy^2).I can get these using the D function. now I need to evaluste the hessian matrix for

...differently. there's really no point in offending people that get confused by r's details. vQ ------------------------------ Message: 9 Date: Mon, 29 Sep 2008 13:39:20 +0200 From: Eik Vettorazzi <E.Vettorazzi at uke.uni-hamburg.de> Subject: Re: [R] density estimate To: Lavan <rsumithran at yahoo.com> Cc: r-help at r-project.org Message-ID: <48E0BE68.6050801 at uke.uni-hamburg.de> Content-Type: text/plain; charset=ISO-8859-1; format=flowed Hi Lavan, a continuous density is not restricted to be within [0, 1]. Its only bound to have an integral of 1. For example dnorm(0,sd...

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 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