similar to: quantile of a mixture of bivriate normal distributions

Hi, Does anyone know how to write a R function to solve the quantile c for the following equation. P(Z1>1.975, Z2<c)+ P(Z1>c, Z2>c)=0.05/6. Z1 and Z2 have a bivariate normal distribution with mean 0, variance 1 and correlation 0.5. Thanks a lot! Hannah [[alternative HTML version deleted]]

Dear all, I am trying to calculate certain critical values from bivariate normal distribution (please see the function below). m <- 10 rho <- 0.1 k <- 2 alpha <- 0.05 ## calculate critical constants cc_z <- numeric(m) var <- matrix(c(1,rho,rho,1), nrow=2, ncol=2, byrow=T) for (i in 1:m){ if (i <= k) {cc_z[i] <- qmvnorm((k*(k-1))/(m*(m-1))*alpha,

hi i'm looking for a gibbs sampling algorithm for R for the case of mixture of K normals, and in particular for the case of bivariate normals. i'd be grateful if anyone could send its own R-routine, at least for the univariate case. thank you in advance matteo

Hi, I would like to know if it is possible to have a "R code" to estimate the parameters of a mixture of bivariate (or multivariate) normals via EM Algorithm. I tried to write it, but in the estimation of the matrix of variance and covariance, i have some problems. I generate two bidimensional vectors both from different distribution with their own vector means and variance and

Dear all, Y, X are bivariate normal with all the parameters known. I would like to generate numbers from the distribution Y | X > c where c is a constant. Does there exist an R function generating random numbers from such a distribution? Thank you very much. hannah [[alternative HTML version deleted]]

Hello I am trying to find an automated way of fitting a mixture of normal and log-normal distributions to data which is clearly bimodal. Here's a simulated example: x.1<-rnorm(6000, 2.4, 0.6)x.2<-rlnorm(10000, 1.3,0.1)X<-c(x.1, x.2) hist(X,100,freq=FALSE, ylim=c(0,1.5))lines(density(x.1), lty=2, lwd=2)lines(density(x.2), lty=2, lwd=2)lines(density(X), lty=4) Currently i am using

I have been authoring a very small R package on CRAN, named "normix" which implements an S3 class "norMix" has plot and print methods; further, E[X] and Var[X] methods, random number generation ("r") and density evaluation. It also provides the 16 "Marron-Wand densities" (known in the (1d) density estimation business). Erik J?rgensen has provided

I have been authoring a very small R package on CRAN, named "normix" which implements an S3 class "norMix" has plot and print methods; further, E[X] and Var[X] methods, random number generation ("r") and density evaluation. It also provides the 16 "Marron-Wand densities" (known in the (1d) density estimation business). Erik J?rgensen has provided

I'd like to mention that there is a new quantile regression package "nprq" on CRAN for additive nonparametric quantile regression estimation. Models are structured similarly to the gss package of Gu and the mgcv package of Wood. Formulae like y ~ qss(z1) + qss(z2) + X are interpreted as a partially linear model in the covariates of X, with nonparametric components defined as

Hi, I woul like to know if it is possible to have a "R code" to generate EM Algorithm for a normal bivariate mixture. Best regard, S.F.

Dear R helper, I hope that u can help me to sort out my problem because I sent an E-mail last night to R-list but I have not receive any help and at the same time I think this problem is not so hard. I have used the following functions before > K<-10 > prime<-c(2,3,5,7,11,13,17) > UN<-seq(1:K)%*%t(sqrt(prime)) > U1<-UN-as.integer(UN) > U<-matrix(qnorm(U1),K,7)

Hi everyone, I am using R 2.4.1 on a MacOS X ("Tiger") operating system. In the last few day I was trying to estimate the parameters of a mixture of two normal distributions using Maximum Likelihood. The code is from Modern Applied Statistics with S (4th edition), chapter 16 ("Optimization"), the dataset is available under MASS in R. Unfortunately, when I tried out the

Dear Ingmar & Dave, Thanks a lot for your help and sorry for the late reply. Finally, I've found a way to separate the mixture of distributions (empirically). But the gamlss package looks great, I'm sure it will help me during my further studies. Kind regards, Susanne On 15 Jun 2009, at 20:09, Ingmar Visser wrote: > Dear Susanne & Dave, > > The gamlss package family

Dear R-Users I would like to estimate a constrained bivariate normal density, the constraint being that the means are of equal magnitude but of opposite signs. So I need to estimate four parameters: mu (meanvector (mu,-mu)) sigma_1 and sigma_2 (two sd deviations) rho (correlation coefficient) I have looked at several packages, including Gaussian mixture models in Mclust, but I am not sure

Hello, I would like to fit a mixture of two Weibull distributions to my data, estimate the model parameters, and compare the fit of the model to that of a single Weibull distribution. I have used the mix() function in the 'mixdist' package to fit the mixed distribution, and have got the parameter estimates, however, I have not been able to get the log-likelihood for the fit of this model

Hi, I'm dealing with wind data and I'd like to model their distribution in order to simulate data to fill-in missing values. Wind direction are typically following a vonmises distribution and wind speeds follow a weibull distribution. I'd like to build a joint distribution of directions and speeds as a VonMises-Weibull bivariate distribution. First is this a stupid question? I'm

Dear R users: I am wondering how to ask for *fixed* number of distributions under parameterized Gaussian mixture model. I know that em() and some related functions can predict the parameterized Gaussian mixture model. However, there seems no parameter to decide number of distributions to be mixed (if we known the value in advance). That is, assume I know the (mixed)data is from 3 different

Dear All, Suppose Z_i, i=1,...,m are marginally identically distributed as a two normal mixture p0*N(0,1) + (1-p0) *N( miu_i, 1) where miu_i are identically distributed according to a mixture and I have generated Z_i one by one . Now suppose these m random variables are jointly m-dimensional normal with correlation matrix M= (m_ij). How to proceed next or how to start correctly ? Question:

You'll find that it is a lot easier to do it in R: # lets first simulate a bivariate normal sample library(MASS) bivn <- mvrnorm(1000, mu = c(0, 0), Sigma = matrix(c(1, .5, .5, 1), 2)) # now we do a kernel density estimate bivn.kde <- kde2d(bivn[,1], bivn[,2], n = 50) # now plot your results contour(bivn.kde) image(bivn.kde) persp(bivn.kde, phi = 45, theta = 30) # fancy contour with

Hello, I am searching for a way to generate random values from a multivariate mixture distribution. For starters, one could create a mixture distribution consisting of a gamma or normal distribution and then a tail/spike of values in a normal distribution (eg as in Figure 1,?A Eyre-Walker, PD Keightley. 2007. The distribution of fitness effects of new mutations. Nature Review Genetics 8: 610-618)