similar to: Theoretical confidence regions for any non-symmetric bivariate statistical distributions

Hi Is there any way to fit a bivaraite longitudinal mixed model using R. I have a data set with col names resp1 (Y_ij1), resp2 (Y_ij2), timepts (t_ij), unit(i) j=1,2,..,m and i=1,2,..n. I want to fit the following two models Model 1 Y_ij1, Y_ij2 | U_i = u_i ~ N(alpha + u_i + beta1*t_ij, Sigma) U_i ~ iid N(0, sigu^2) Sigma = bivariate AR structure alpha and beta are vectors of order 2.

Dear R users, I am doing MCMC using Metropolis-Hastings. My model is bivariate-log-normal and the prior for variance-covariance is wishart distribution. I am wondering if there are some simple codes about how to get the density of Wishart distribution in my case ? Thanks in advance. Meihua [[alternative HTML version deleted]]

Dear all, Is there any standard statistical terminology describing the points beyond a confidence region? Obviously, the "outlier" is improper here. Please help me if you happens have the info. Thanks you very much for your kindly help. Best wishes, Zhiqiu [[alternative HTML version deleted]]

Am trying to do some study on bivariate binomial distribution. Anyone knows if there is package in R that I can use to calculate the density function of bivariate binomial distribution and to generate random samples of it. Thanks, -- View this message in context: http://n4.nabble.com/How-to-calculate-density-function-of-Bivariate-binomial-distribution-tp992002p992002.html Sent from the R help

Dear all, I have a bivariate dataset from a preliminary study. I want to do two things: (1) estimate the probability density of this bivariate distribution using some nonparametric method (kernel, spline etc); (2) sample a big dataset from this bivariate distribution for a simulation study. Is there any good method or package I can use in R for my work? I don?t want parametric models like

sorry for cross-posting Dear all, I have tow (several) bivariate distributions with a known mean and variance-covariance structure (hence a known density function) that I would like to compare in order to get an intersect that tells me something about "how different" these distributions are (as t-statistics for univariate distributions). In order to visualize what I mean hear a little

Hi: I am using the package "KernSmooth" to do the local polynomial regression. However, it seems the function "locpoly" can only deal with univariate covaraite. I wonder is there any kernel smoothing package in R can deal with bivariate covariates? I also checked the package "lcofit" in which function "lcofit" can indeed deal with bivariate case. The

Dear list members I need a function that calculates the bivariate normal distribution for each observation. It is part of a likelihood function and I have 1000's of cases. As I understand it I cannot use packages like "mvtnorm" because it requres a covariance matrix of the same dimension as the number of observations. Basically what I need is a function that takes as arguments a

Hello, I'm quite new with R and so I would like to know if there is a command to calculate an integral. In particular I simulated a bivariate normal distribution using these simple lines: rbivnorm <- function(n, # sample size mux, # expected value of x muy, # expected value of Y sigmax, # standard deviation of

Hello all!!! 1) I am wondering is there a way to generate random numbers in R for Bivariate Uniform distribution? 2) Does R have  built-in function for generating random numbers for any given bivariate distribution. Any help would be greatly appreciated !! Good day! Haneef Anver [[alternative HTML version deleted]]

Any one know is there any package or function to generate bivariate exponential distribution? I gusee there should be three parameters, two rate parameters and one correlation parameter. I just did not find any function available on R. Any suggestion is appreciated. -- View this message in context:

Dear all, I created a bivariate normal distribution: set.seed(138813) n<-100 x<-rnorm(n); y<-rnorm(n) and plotted a scatterplot of it: plot(x,y) Now I'd like to add the 2D-standard deviation. I found a thread regarding plotting arbitrary confidence boundaries from Pascal H?nggi http://www.mail-archive.com/r-help at r-project.org/msg24013.html which cites the even older thread

x <- iris$Sepal.Length[1:50]/iris$Sepal.Width[1:50] y <- iris$Petal.Length[1:50]/iris$Petal.Width[1:50] I want to check whether (x,y) follows a bivariate normal distribution or not, using density plot or scatter plot. Is it possible to plot a bivariate density in R. I cant find any. Arindam Fadikar M.Stat Indian Statistical Institute. New Delhi, India [[alternative HTML version

Is there code for bivariate kernel density estimation? For bivariate kernels there is kde2d in MASS kde2d.g in GRASS KernSur in GenKern (list probably incomplete) but none of them seems to accept a weight parameter (like density does since R 2.2.0) -- Erich Neuwirth, University of Vienna Faculty of Computer Science Computer Supported Didactics Working Group Visit our SunSITE at

Hello, I have used the univariate Ripley K function in R, but does anyone know if there is a bivariate function built in? I have two species that I am dealing with. Also, how might I add error bars into the graphs (univariate and/or bivariate)? Thank you, Karin Leiderman k_leiderman at hotmail.com Graduate Student/Research Assistant Department of Mathematics Univesity of New Mexico

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 list I have a vector of values that allegedly have a bivariate normal distribution. I want to create a plot that shows the values I have obtained, and the bivariate normal distribution curve for the data. Is there a way of doing this in R? Many thanks for your help, Sarah. --------------------------------- [[alternative HTML version deleted]]

hello, I'm trying to improve the speed of my calculation but didn't get to a satisfying result. It's about the numerical Integration of a bivariate normal distribution. The code I'm currently using x <- qnorm(seq(.Machine$double.xmin,c(1-2*.Machine$double.eps),by=0.01), mean=0,sd=1) rho <- 0.5 integral <- function(rho,x1){

Dear R-List: I`m trying to calculate the probabilities for a bivariate normal distribution while using the mvtnorm-package(dmvnorm) and the adapt-package for multidimensional integration. The problem is that I can`t specify the upper bound in the adapt-package the way I need it because I don`t need a rectangular area. I want to calculate the probability starting at the origin under the line y=x.

Hi, I am using the KernSmooth package to estimate nonparametrically bivariate density functions. However, it seems that the bandwidths (one for each co-ordinate direction) have to be selected manually. This does not apply for the univariate case, for which dpik (included in KernSmooth) uses up-to-date plug-in rules. Does anyone know about a package, or function, which estimates bandwidths